Radiology Research Forum

Our perennial lecture series features scientists from U.S. and foreign institutions presenting the latest research in biomedical imaging.

Radiology research forum is an ongoing lecture series held approximately every two weeks at our Center.

The forum rotates among lectures by distinguished visiting researchers, presentations by partners involved in Collaborative Projects with our faculty, and research reports by scientists from the radiology department at NYU Langone Health, which operates our Center.

Many of the lectures comprise the Seminar in Biomedical Imaging (BMSC-GA 4416), part of the Biomedical Imaging and Technology PhD Training Program.

Upcoming and most recent lectures are shown first.

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2022 Lectures

For the time being, all research lectures are conducted via Webex. To request an invitation, get in touch with Rania Assas.

  • May 3, 2022, at noon via Webex

    In Vivo Functional Neuroimaging in the First Near-Infrared Optical Window

    Sarah Shaykevich, MPhil

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    The first near-infrared (NIR I) wavelength range of 650-950 nm is preferable in many optical biological imaging techniques due to reduced light absorption by hemoglobin and water. I will present in vivo functional neuroimaging findings from optical and optoacoustic imaging in the NIR I range. My work utilizes novel dyes and genetically encoded calcium indicators as well as endogenous hemodynamic signals.

    About the Speaker

    Sarah is a doctoral candidate in the laboratory of Shy Shoham, PhD, at NYU Langone’s Tech4Health Institute. Her research is currently focused on functional neuroimaging with optoacoustic tomography.

  • April 26, 2022, at noon via Webex

    Neural Dynamics of Prior-Guided Visual Ambiguity Resolution

    Jonathan Shor, MS

    Graduate Student
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Recognition is a fundamental cognitive function during which the brain projects perceptual templates learnt from past experiences onto the current sensory input. A key factor in this process is how much the brain weights prior knowledge versus the sensory input. This weighting is known to vary between individuals and may contribute to hallucinations in mental illnesses when prior knowledge is weighted to a pathologically high degree. By manipulating human subjects’ ability to use prior knowledge to recognize stimuli during fMRI and electrocorticography neural recording, this project examines the underlying neural mechanisms of prior knowledge deployment across sensory modalities and distinct sources of prior knowledge. Specifically, this project examines visual and auditory recognition, and prior knowledge derived from lifelong learning as well as one-shot learning. This talk will present initial results and plans to integrate data from multiple experiments.

    About the Speaker

    Jonathan Shor is a fourth-year graduate student in the Vilcek Institute’s Biomedical Imaging and Technology PhD Training Program working with Dr. Biyu He. Shor has a background in computer science and is interested in developing computational models of cognitive functions, such as conscious perception. His current focus in the Perception and Brain Dynamics Lab is establishing the neural mechanisms driving prior knowledge deployment during recognition.

  • April 21, 2022, at noon via Webex

    A Study of Functional, Structural and Effective Connectivity of the White Matter in Patients Undergoing Awake Brain Surgery

    Petru Isan

    Master in Neuroscience Candidate
    Paris Est-Creteil University


    White matter bundles underline structural connectivity in the human brain: they link functional cortical and subcortical gray matter areas and thus, they allow complex interactions between these regions. Awake brain surgery is a unique opportunity to study these white matter fibers, to which we have physical access inside the post-resection cavity. In the present protocol, we study the structural connectivity using diffusion MRI – tractography and the effective connectivity using electrocorticography – subcortico-cortical evoked potentials. We attempt to correlate biomarkers provided by these two methods in order to better understand the propagation of electrical activity in the brain.

    About the Speaker

    Petru Isan is currently undertaking a Master’s degree in Neurosciences at the Paris Est-Creteil University. He has graduated from the Tours Faculty of Medicine and is a second year Neurosurgery resident at the Pasteur 2 University Hospital in Nice, France. At the moment, Petru is doing an internship at INRIA Sophia-Antipolis, where he is investigating how different brain regions communicate with each other.

  • April 19, 2022, at noon via Webex

    An AI System for Screening Mammography Could Reduce Unnecessary Recalls

    Jungkyu Park, MS

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Digital breast tomosynthesis (DBT) is a new imaging technique in mammography. Even though it is proven to be even more accurate than full-field digital mammography, false-positive recalls are still a subject of concern in the breast cancer screening setting. We developed an AI system using the screening mammography exams at NYU Langone Health which could save 29% of unnecessary recalls and potentially reduce radiologist workload by 40% while missing no malignancies. Specifically, our system consists of deep neural networks trained on both breast-wise pathology labels and a limited amount of pixel-level segmentation labels. The system can also highlight the location of suspicious findings on 2D and 3D mammography images for AI decision support.

  • April 12, 2022, at noon via Webex

    How Does Microstructure Change during Development and in Disease: A Comparison of Diffusion MRI Models in White Matter

    Ying Liao, MPhil

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Biophysical models provide specificity to the tissue microstructure with diffusion MRI. In the brain, the Standard Model (SM) of diffusion in white matter (WM) was proposed as an overarching framework unifying many previous WM models. To stabilize the parameter estimation in clinical datasets with limited information, different constraints have been adopted to the WM models, resulting in different outcomes. Meanwhile, a machine learning (ML) model has shown promise in estimating SM parameters without constraints. To evaluate these WM models, we first compare the accuracy and specificity of them in simulation. Then we apply these models to early brain development, multiple sclerosis and stroke. Through this extensive comparison both in simulation and in several pathologies or processes, our goal is to determine the most reliable WM model for clinical datasets to extract tissue microstructure parameters.

    About the Speaker

    Ying Liao is a doctoral candidate in Vilcek Institute’s Biomedical Imaging and Technology training program working with Els Fieremans, PhD, and Dmitry Novikov, PhD. He has a background in biomedical engineering and is interested in developing and employing machine learning (ML) methods to characterize tissue microstsructure. His focus in the MRI biophysics lab is the ML-based estimation of white-matter parameters in the standard model of diffusion MRI.

  • March 30, 2022, at noon via Webex

    Robust Quantitative Structural Neuroimaging in Development and Degeneration

    Dylan Tisdall, PhD

    Research Assistant Professor of Radiology
    Perelman School of Medicine
    University of Pennsylvania


    Structural neuroimaging is central to MRI’s role in both clinical practice and neuroscience. In addition to its role as a diagnostic modality, structural MRI provides the basis for cortical morphometric analyses that are widely used to study both developmental and degenerative processes. However, many populations of interest are often unable to remain still enough to produce the high-quality structural MRI needed for either clinical interpretation or quantitative morphometry. Moreover, while group differences in MRI-based morphometry have been presented across a variety of populations, it is generally difficult to extend these results to single-subject diagnoses.

    I will present my ongoing work towards making structural neuroimaging methods both more robust, and more sensitive to disease processes. First, I will show how we have improved morphometric accuracy by studying motion as a source of bias and developing methods to help ameliorate these errors. As an example of this work, I will present refinements to our motion correction system for application in the upcoming HEALthy Brain and Child Development study. Second, I will show how we are developing intra-cortical measures by studying Frontotemporal Lobar Degeneration (FTLD) using joint ex vivo MRI and histopathology. I will present our recent findings of iron-rich pathology within the cortical laminae in FTLD, and discuss our plans for translating these results to in vivo imaging studies with the goal of single-subject diagnosis.

  • March 29, 2022, at noon via Webex

    In Vivo Mapping of Cerebral Venous Vasculature and Oxygen Metabolism in Aging Brain

    Chenyang Li, MSE, MPhil

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Aging is a major risk factor of neuronal loss and cognitive impairment (e.g., Alzheimer’s disease). Neurovascular abnormalities and brain atrophy are proved to be pathophysiological biomarkers in normal- and abnormal-aging brains. In vivo detection of microvascular changes underlying neurodegeneration plays a crucial role in early diagnosis and better understanding disease mechanism in age-related dementia. Susceptibility weighted imaging (SWI) and quantitative susceptibility mapping (QSM) are sensitive to the deoxygenated hemoglobin in the veins, which can be used to map the venous vasculature and characterize venous (de)oxygenation level in the brain. This presentation will demonstrate two studies that use high resolution SWI and QSM 1) to characterize the venous oxygenation/utilization changes related to neurodegeneration in the elderly; 2) to map the in vivo venous vasculature of hippocampus at 7T. These studies aim to gather more evidence on the role of SWI/QSM as an early imaging marker of age-related neurodegenerative diseases.

  • March 22, 2022, at noon via Webex

    Revisiting White Matter Hyperintensity Etiology from a Physics Perspective

    Johannes Weickenmeier, PhD

    Assistant Professor of Mechanical Engineering
    Director of the Center for Neuromechanics
    Stevens Institute of Technology


    White matter changes are a frequent observations in the aging human brain and are considered a reliable indicator for cognitive impairment and long-term functional decline. On T2-weighted fluid attenuated inversion recovery magnetic resonance images, these lesions appear as white matter hyperintensities (WMH) and are commonly associated with vascular degeneration. From a physics perspective, however, the persistent (onset) locations of periventricular WMHs along the edges of the lateral ventricles suggests involvement of mechanical (over)loading of the ependymal cells forming the functional brain-fluid barrier. We use computational modeling to systematically explore the relationship between brain aging, white matter changes, and WMH formation. To that end, we build anatomically accurate brain models and predict the mechanical loading of periventricular tissues. We observe that maximum ependymal cell stretch consistently localizes in the anterior and posterior horns irrespective of ventricular volume or shape. More importantly, these locations coincide with periventricular WMH locations observed in our patient scans. From these results, we pose that further analysis of white matter pathology in the periventricular zone that includes a mechanics-driven deterioration model for the ventricular wall.

  • March 15, 2022, at noon via Webex

    Photon Counting CT as Clinical Reality

    Thomas O’Donnell, PhD

    Senior Staff Scientist
    Siemens Healthineers


    Next month NYU will be the 6th facility in the US to receive an FDA approved CT scanner with photon counting capabilities. Photon counting CT represents a new paradigm in CT scanning. Compared to conventional CT where the energies of the photons incident on the detector are reported as a total sum, photon counting CT measures the energies of individual photons (i.e., “counts” them). In this talk I will describe the fundamental physics of photon counting CT, the implications of this approach to scanning, and its clinical benefits.

  • March 8, 2022, at noon via Webex

    Thalamic and Basal Ganglia Iron in Psychotic Spectrum Disorders

    Yu Veronica Sui, MA, MPhil

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Iron is critical for healthy brain biochemistry and function. While deficient peripheral iron was found to increase psychiatric morbidity risk, in vivo examination of non-heme brain iron in psychotic spectrum disorders (PSD) are lacking. The current study employed quantitative MRI to examine iron content in several iron-rich subcortical structures in a young adult PSD group. Using a modified cross-relaxation imaging method, we fitted R1 and macromolecular proton fraction maps and estimated region-wise R2* values using a linear regression model. Our findings suggest that subcortical non-heme iron deficiencies play a role in PSD risk and symptomatology and may precede both structural and myelin alterations.

  • February 22, 2022, at noon via Webex

    Training a Machine Learning Algorithm for DCE-MRI Reconstruction

    Zhengnan Huang, MS, MPhil

    Doctoral Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    DCE-MRI is a critical imaging method used in cancer diagnosis. However, it is limited by long reconstruction time, and has inherent trade-off between temporal and spatial resolution. While machine learning has been shown to improve MR reconstruction quality in several studies, these methods require ground truth image data for training. For DCE-MRI reconstruction, we do not have access to a simultaneously high temporal and spatial resolution ground truth image. In this talk, I’m going to introduce a novel pipeline to simulate realistic ground truth training images based on pharmacokinetic models and anatomical structure. We trained the machine learning model to reconstruct images by using the simulated k-space and ground truth images. The result shows, with different models, machine learning models could reconstruct images with high image quality in less time. This simulation pipeline is available online, suitable for future development and exploration.

  • February 15, 2022, at noon via Webex

    Translating the Standard Model of Diffusion in White Matter into the Clinic

    Santiago Coelho, PhD

    Postdoctoral Fellow
    Center for Advanced Imaging Innovation and Research
    NYU Grossman School of Medicine


    Biophysical modeling of diffusion MRI data is appealing due to its potential to provide specificity to pathological processes. However, robust parameter estimation of the Standard Model (SM) of diffusion in white matter has been elusive due to intrinsic model degeneracies. Machine learning approaches improve parameter estimates but at low SNR these are determined by the training data. We develop a theory to analyze this behavior as function of SNR. Finally, we use these results to explore the design of optimal scanner-specific protocols to enable SM estimates in 15-minute acquisitions where we show reproducible results. Combining protocol optimization and robust parameter estimation may enable quantitative microstructure mapping in clinical settings.

  • January 25, 2022, at noon via Webex

    Performance of ODF-fingerprinting with a Biophysical Multicompartment Diffusion Model

    Patryk Filipiak, PhD

    Postdoctoral Fellow
    Center for Advanced Imaging Innovation and Research
    NYU Grossman School of Medicine


    Visualizations of white matter fibers are reconstructed in vivo from diffusion MRI through tractography. To this end, dedicated reconstruction techniques need to identify spatial orientations of fibers, typically by seeking maxima of orientation distribution functions (ODFs). However, commonly used methods often fail to reconstruct fibers crossing at shallow angles below 40 degrees. We aim to break this barrier with our proposed approach called ODF-fingerprinting (ODF-FP).

    In this talk, I will introduce the concept of ODF-FP and the process of generation of ODF dictionaries that covers biologically plausible microstructure parameters. I will present the accuracy of crossing fibers reconstruction with ODF-FP in numerical simulations, diffusion phantoms, and the mouse model. In the latter, I will show the ODF-FP recontruction of the optic pathways from in vivo diffusion MRI acquisition validated with the manganese chloride enhancement of the reconstructed tracts.

  • January 11, 2022, at noon via Webex

    Improving Breast Cancer Diagnostics with Artificial Intelligence for MRI

    Jan Witowski, MD, PhD

    Postdoctoral Fellow
    Center for Advanced Imaging Innovation and Research
    NYU Grossman School of Medicine


    Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has a very high sensitivity in detecting breast cancer, but it often leads to unnecessary biopsies and patient workup. In this project, we developed and used an artificial intelligence (AI) system to improve the overall accuracy of breast cancer diagnosis and personalize management of patients undergoing DCE-MRI. In a clinical validation study, the AI system was found to be statistically equivalent to 5 board-certified breast radiologists. Radiologists’ performance improved when their predictions were averaged with AI’s predictions. We demonstrated the generalizability of the AI system using multiple data sets from Poland and the U.S. In subgroup analysis, we observed consistent results across different cancer subtypes and patient demographics. We showed that the AI system can reduce unnecessary biopsies in the range of clinically relevant risk thresholds. This would lead to avoiding benign biopsies in up to 20 percent of all BI-RADS category 4 patients. This exploratory work creates a foundation for deployment and prospective analysis of AI-based models for breast MRI.

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2021 Lectures

  • December 21, 2021, at noon via Webex

    Cloud MR: A Comprehensive Software Platform for the Design and Evaluation of Radiofrequency Coils for Applications in Magnetic Resonance Imaging

    Eros Montin, PhD

    Postdoctoral Fellow
    Center for Advanced Imaging Innovation and Research
    NYU Grossman School of Medicine


    MRI technology is being continuously developed and new MRI scanners are installed every year. However, most of them are used clinically, without being available to researchers. Furthermore, the majority is in western countries. As a result, access to MRI research and training has been limited.

    Several open-source software tools are available to simulate different aspects of the MRI experiment, from hardware design to signal encoding and image reconstruction. However, their dissemination has been limited because they are not general enough, often lack documentation, and require extensive background knowledge. Nevertheless, they have enormous potential.

    The goal of this project is to integrate, generalize and extend this existing software to develop a comprehensive open-source software platform to simulate the complete lifecycle of an MRI experiment. By relying on a web-based graphic user interface and cloud computing, Cloud MR will enable anyone with an internet connection to perform MRI research anywhere in the world. Furthermore, it will be a unique tool for MRI training, which is increasingly needed due to the clinical widespread use of MRI.

  • December 7, 2021, at noon via Webex

    DW-MRS: Beyond Cell Structure

    Julien Valette, PhD

    Research Director
    Commissariat a l’Energie Atomique (CEA)
    Paris, France


    Basic understanding of brain metabolite diffusion as measured using diffusion-weighted magnetic resonance spectroscopy (DW-MRS) in vivo has progressed over the last years, allowing relevant interpretation of DW-MRS in terms of cellular microstructure. When combined with adequate modeling, DW-MRS may even allow cell-specific (neuron versus astrocytes) quantification of some microstructural parameters. We will see how recent results suggest that DW-MRS may actually open perspectives beyond cell structure determination, namely characterizing diffusion in the extracellular space, and assessing the distribution of brain lactate between neurons, astrocytes and the extracellular space, which is a highly relevant neuroscience question related to energy metabolism and the astrocyte-to-neuron lactate shuttle.

  • December 3, 2021, at noon via Webex

    Single Shot Spiral TSE at 1.5 and 3 T

    Jürgen Hennig, PhD

    Professor and Scientific Director
    Department of Diagnostic and Interventional Radiology
    University Medical Center Freiburg, Germany


    Spiral MRI has been known since 1983. Spiral trajectory offer an extremely fast and efficient way to cover two-dimensional k-space with an intrinsically one-dimensional trajectory – much more efficeint than the line-by-line scanning of commonly used Cartesian sampling. Spirals have additional advantages like intrinsic motion compensation. They still haven’t made it into clinical routine due to their extreme sensitivity against deviations of the actual from the nominal trajectory and against off-resonance effects, where even slight inhomogeneities due to susceptibility effects lead to strong image artifacts.

    The presentation will discuss principles and implementation of single shot spiral TSE at 1.5 and 3 T. High-quality images with 1 mm in-plane resolution are acquired in < 200 ms allowing extremely fast screening e.g. in non-cooperative patients.

  • November 30, 2021, at noon via Webex

    The Influence of Eddy Currents on Hybrid-state-based Quantitative MRI and Initial Results from 0.55 T

    Sebastian Flassbeck, PhD

    Postdoctoral Fellow
    NYU Langone Health


    Hybrid-state Free precession (HSFP) is a quantitative transient state technique that allows the rapid mapping of relaxation times, in part, due to the high signal which results from a fully balanced sequence design. However, this design makes the transient magnetization of HSFP highly susceptible to disruptions caused by eddy-current induced phases. These eddy current artifacts in balanced sequences result from large jumps in k-space. The 3D kooshball HSFP sequence samples the spin dynamics repeatedly while acquiring different parts of k-space. We swap individual k-space lines between different repetitions in order to minimize jumps within each repetition. This reordering can be formulated as a traveling salesman problem, and we tackle the discrete optimization with a simulated annealing algorithm.

    In the second part, HSFP based quantitative MRI is applied at 0.55 T. Here, the SNR-efficient design of HSFP allows quantitative maps to be obtained in 12 mins with 1 mm isotropic resolution. Further, a theoretical analysis of the Cramér–Rao bound is performed and the expected SNR is compared to 3T.

  • November 23, 2021, at noon via Webex

    Measuring Subtle Changes in Blood-brain Barrier (BBB) in Alzheimer’s Disease and Aging Using Dynamic Contrast-enhanced MRI (DCE-MRI)

    Jonghyun Bae, MPhil

    PhD Candidate
    Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    The disruption of blood-brain barrier (BBB) is associated with various pathologies in the brain. Dynamic contrast-enhanced MRI has been widely used as a tool for quantitatively measuring the changes in the microstructural environment in brain. In recent year, there has been increasing interest in measuring the BBB disruption in Alzheimer’s disease (AD) and normal aging. However, unlike diseases that exhibits substantial changes in brain such as brain tumor, the BBB disruption in AD or aging is suggested to be very subtle.

    We utilize Golden-angle RAdial Sparse Parallel (GRASP) sequence to effectively measure this subtle disruption and to overcome the following 3 challenges: (a) To reduce the long scan time to observe small extravasation of the contrast agent, (b) To obtain the arterial input function using the help of AI, and (c) To validate and measure the sensitivity of current approach in different levels of BBB disruption.

    To address these challenges, we have (a) developed a novel pharmacokinetic model suitable for measuring BBB disruption with reduced scan time, (b) trained and implemented a deep neural network to deterministically estimate the capillary-level input function, and (c) conducted an animal study to artificially induce the different levels of BBB disruption and compared the sensitivity of measuring subtle disruption via the conventional Gadolinium contrast agent exchange rate and the water exchange rate using Ferumoxytol contrast agent.

  • October 26, 2021, at noon via Webex

    Intelligent Imaging to Study Degenerative Joint Diseases

    Valentina Pedoia, PhD

    Assistant Professor
    Center of Intelligent Imaging (CI2)
    Department of Radiology and Biomedical Imaging
    University of California, San Francisco


    Active multi-disciplinary research is ongoing to discover quantitative biomarkers for early diagnosis, monitoring and assessment of joint degeneration. Medical imaging has played a substantial role in this area; for example, radiographs can detect structural alterations in bone, but these scans have low sensitivity for detecting tissues that are thought to be important in joint degeneration in OA (such as cartilage, menisci and other soft tissues) and cannot capture changes in bone marrow (that is, bone marrow lesions). Conversely, magnetic resonance imaging (MRI) has higher sensitivity than radiography in detecting soft tissue changes, bone marrow oedema and early osteophytic changes. Advanced quantitative imaging techniques, novel computerized image post-processing and more recent machine learning (ML) techniques have made possible further advances towards quantitative characterization of early joint degeneration and identification of imaging biomarkers associated with OA. Deep learning advances are revolutionizing the use of MRI in clinical research by augmenting activities ranging from image acquisition to post-processing. Automation is key to reducing the long acquisition times and processing of MRI, conducting large-scale longitudinal studies, and quantitatively defining morphometric and other important clinical features of both soft and hard tissues in various anatomical joints. Deep learning methods have been used recently for multiple applications in the musculoskeletal field. Compared with labor-intensive human efforts, DL-based methods have advantages and potential in all stages of imaging as well as post-processing steps, including aiding in diagnosis and prognosis. In this talk, I’ll explore how recent applications of DL have improved imaging-based understanding of knee OA. We illustrate how DL techniques are applied at all stages of imaging to enable automation of acquisition analysis and new imaging biomarkers discovery.

  • October 19, 2021, at 2:00 p.m. via Webex

    Diffusion MRI to Early Detect Slow-growing and Diffuse Glioma Models and to Explore Muscle Structure, Function, and Disease

    Paola Porcari, PhD

    Research Scholar
    Memorial Sloan Kettering Cancer Center


    Diffusion weighted imaging is a powerful technique sensitive to tissue microstructure. Three possible applications of this technique will be presented: (a) to early detect slow growing diffuse glioma models; (b) to evaluate the effect of ageing on mouse muscle microstructure in dystrophic and healthy mice; (c) to image the motor unit activity in the human muscles.

    (a) Diffuse gliomas (WHO grade II to IV) are the most common primary brain tumours in humans. Their diffuse infiltration into the surrounding normal brain precludes complete resection and they all eventually recur, usually having progressed to a more aggressive tumour. The infiltrative part, which is “invisible” using conventional T1 and T2-weighted MRI is difficult to target with treatment. We investigated whether diffusion MRI might be a useful method to detect the microstructural changes induced in the normal brain by the slow infiltration of glioma sphere cells. Localized proton MR spectroscopy of lesions and immunohistochemical assessment were compared with imaging results.

    (b) During postnatal development, muscle fibres grow enormously and the sarcolemma dynamically and constantly expands. Investigating this process in the time up to maturity may help the understanding of clinical onset of infant myopathies, such as Duchenne muscular dystrophy.The evolution of hindlimb muscle microstructure between young (development) and adult mice was investigated in dystrophic and healthy muscles using diffusion-weighted imaging protocols and histology. Multiple diffusion times (range: 25 – 350 ms) were explored, and significant differences between the diffusion properties of hindlimb muscles in healthy and diseased mice were found for long diffusion times, with increased water diffusivity in dystrophic mice. Muscle fibre size distributions showed higher variability and lower mean fibre size in dystrophic than wild-type animals. The extensive uptake of Evans Blue Dye in dystrophic muscles revealed a substantial sarcolemmal damage, suggesting diffusion measurement as more consistent with altered permeability rather than changes in muscle fibres.

    (c) Neuromuscular diseases can lead to characteristic changes in the anatomy of motor units (MU). A diffusion-weighted imaging protocol sensitive to contraction was developed by synchronizing in scanner-electrical stimulation of MU with a pulsed gradient spin-echo imaging sequence. The spatial pattern of muscle fibres forming different MU was imaged in six healthy controls and subsequently in four patients with confirmed amyotrophic lateral sclerosis (ALS). Florid fasciculation in ALS patients was revealed at a frequency higher than healthy controls.

  • October 12, 2021, at noon via Webex

    Novel Numerical Methods Based on Integral Equations for Computational Electromagnetics Applications at Ultra-High-Field MRI

    Ilias Giannakopoulos, PhD

    Postdoctoral Fellow
    Department of Radiology
    NYU Langone Health


    The single frequency nature of magnetic resonance (MR) allows the design and optimization of fast and robust algorithms for computational electromagnetics, based on integral equations. Specifically, surface integral equations (SIE) can be employed to analyze radio-frequency (RF) transmit-receive coils, while volume integral equations (VIE) can model the interactions between RF fields and biological tissues with finite electrical properties. The fast and accurate estimation of the above interactions is critical for optimal RF coil design at ultra-high-field MRI and to solve inverse problems based on MR measurements. For example, rapid SIE/VIE methods can be used to estimate tissue electrical properties, as in Global Maxwell Tomography, or to estimate coil sensitivities, as in Maxwell Parallel Imaging. This talk will describe our latest developments in this areas and present results for various applications of computational electromagnetics at ultrahigh field MRI.

  • October 6, 2021, at 2:00 p.m. via Webex

    That Implant Is Hot: How Insights from In Silico Medicine Changed the Surgical Practice and Opened New Horizons to Enable Implant-Friendly MRI

    Laleh Golestani Rad, PhD, P.Eng

    Assistant Professor of Biomedical Engineering
    Assistant Professor of Radiology
    Northwestern University
    Chicago, IL


    More than 12 million Americans currently carry a form of orthopedic, cardiovascular, or neuromodulation device and the number grows by 100,000 annually, driving medical implant market to reach $160 billion by 20221. It is estimated that 50%-75% of patients with active electronic implants will need to undergo MRI during their life time, with some needing repeating examination. Recent advances in device engineering have led to a new generation of electronic implants that are largely immune to MRI-generated static and gradient fields. Tissue heating from radiofrequency (RF) excitation fields, however, remains a major issue.

    This talk will give an overview of recent advances in development of specialized MRI hardware for implant-friendly imaging with a focus on patients with deep brain stimulation implants. We then discuss the unique role of computer modeling in MRI safety assessment and recent success stories in guiding the surgical practice toward MR-friendly device implantation, and novel insights into RF heating of implants in new MRI platforms (e.g., vertical scanners). Finally, we will talk about feasibility of applying machine learning for real-time risk assessment of MRI in patients with conductive implants.

  • October 1, 2021, at noon via Webex

    Learning with Incomplete Supervision for Image Analysis and Medical Imaging Applications

    Elena Sizikova, PhD

    Moore Sloan Faculty Fellow/ Assistant Professor
    Center for Data Science
    New York University


    There exists an abundance of neural network techniques that achieve impressive performance on various visual processing tasks. Most of these methods require full supervision and large annotated training datasets. Collecting such training datasets and annotations is often expensive and time-consuming, or not possible due to limitations on data privacy. In this talk, I discuss how weakly supervised learning and synthetic data generation can be used as a substitute when full training data annotations are not available. The proposed methodologies are evaluated on various computer vision and medical imaging benchmarks.

  • September 28, 2021, at noon via Webex

    Magnetic Particle Imaging: Andvanced Engineering, Application, and Tracers

    Steve Conolly, PhD

    Montford G. Cook Endowed Chair of Bioengineering and Electrical Engineering and Computer Sciences
    UC Berkeley


    Magnetic Particle Imaging (MPI) is an emerging noninvasive biomedical imaging modality that shows great promise for vascular and cellular imaging. MPI uses different physics from all conventional imaging modalities. MPI offers ideal “positive” tracer contrast, because human tissues produce zero MPI signal and tissue is magnetically transparent. The signal comes only from superparamagnetic iron oxide tracers (25-nm SPIOs). MPI has very high molar sensitivity because the SPIO magnetization is 51 million times stronger than the nuclear magnetization, M0, imaged in a 3.0T MRI. Indeed, we can even detect 1 micromolar [Fe] concentrations in seconds with quantitative, linear and positive contrast. MPI technology has not reached its true physics limit; we believe MPI could soon achieve single-cell sensitivity and 100-micron resolution with optimized tracers.

    MPI applications today already compete with nuclear medicine studies on dose-limited sensitivity. But MPI offers a zero-radiation option for: tracking, cellular therapies [2,3]; pulmonary embolism detection with 3D ventilation-perfusion MPI, akin to Tc99m V/Q studies [4]; capillary-level noninvasive CBV & CBF for stroke or traumatic brain injury [5], or to rule out a traumatic gut bleed (akin to RBC-Tc99m scintigraphy) [6]. Cancer MPI is more challenging but soon could provide a noninvasive screening alternative to X-ray mammography for high-risk women with radiologically dense breast tissue [7]. We recently showed that antibody labeled SPIOs can track WBCs (akin to In-111 WBC studies) [9]. WBC-MPI could emerge to be the best method to image infection, inflammation or cancer, and a powerful tool for optimizing immunotherapies. An important advantage relative to scintigraphy GI bleed and V/Q studies (which can take up to 3 hours including prep and scan) is speed: the targeted magnetic tracers can be safely injected immediately from the refrigerator providing first scans in just a few minutes. A crucial advantage of WBC-MPI is zero radioactivity of the tracers. CAR-T and CAR-NK cell therapies cannot survive the radiation dose of In-111 and so you cannot use standard nuclear medicine tools to track these exciting immunotherapies. Indeed, Immuno-MPI could soon become medicine’s most powerful tool for optimizing immunotherapies. Our lab has recently developed a potential breakthrough in MPI that already shows dramatic SNR and spatial resolution dramatically (10-fold for SNR and linear resolution) [9]. Experiments and physical models show that chained SPIOs act like ferromagnetic particles, with remanence and coercivity. This is well-modeled as a positive, regenerative feedback control system akin to Schmitt trigger comparators. Moreover, the new tracers show enormous improvements in SNR and spatial resolution, allowing for up to 1000-fold reduction in voxel volume. Our new tracers are not superparamagnetic (SPIO); they are actually superferromagnetic particles [9]. We will show that superferromagnetic tracers could remove the final obstacle to human MPI, allowing for safe 1mm resolution in a human MPI scanner with cost comparable to a human whole-body 0.5T MRI scanner.

    1. Gleich and Weizenecker Nature 435, 2005.
    2. B. Zheng et al., Nature Scientific Reports 5, 2015.
    3. B. Zheng et al., Theranostics, 2016.
    4. X. Zhou, et al., Phys. Med. Biology 62, 2017.
    5. R. Orendorff, et al., Phys. Med. Biology 62, 2017.
    6. E. Yu et al. ACS Nano, 11 (12), 2017.
    7. E. Yu, et al Nano Lett., 17 (3), 2017.
    8. P. Chandrashkharan, Nanotheranostics, 2021.
    9. ZW Tay, Small Methods, 2021.
  • September 27, 2021, at noon via Webex

    Noninvasive Detection of Chronic Diseases Enabled by Precision Molecular Imaging (pMRI)

    Jenny J. Yang, PhD

    Regents Professor
    Georgia State University
    Atlanta, GA


    Acute and chronic human diseases including liver and lung diseases, cancer, cardiovascular diseases and virus infection, share common key determinants including inflammation and fibrosis. In order to facilitate early detection, staging, and treatment responses, it is essential to develop a non-invasive imaging methodology that will allow us to longitudinally map and quantify the dynamic changes of inflammation biomarkers, such as chemokine receptors and collagen expression, during disease progression and upon treatment. Here we report our recent breakthrough in optimization, characterization, formulation, and production of a set of novel human protein-based contrast agents (ProCA®s) pioneered by our team for both preclinical and clinical applications.

    We have developed a human collagen-targeted MRI contrast agent (hProCA32.collagen) with optimized binding fibrosis specificity. hProCA32.collagen exhibits 6.7-fold and 13.7-fold higher binding affinities for collagen type I over types III and IV, respectively. Our developed inflammation and fibrosis biomarker-targeted contrast agents specifically delineate activation of several types of cells and can capture the pro-metastasis niche and fibrosis associated with fatty liver and tumor microenvironment. With newly enabled dual and multi-color MR imaging methodology (precision imaging by MRI, pMRI) at multiscale, we have achieved robust longitudinal detection of early-stage liver and lung fibrosis, as well as micro-metastasis quantification of molecular biomarker changes for staging and monitoring treatment responses. We are moving rapidly toward clinical applications in early detection, monitoring progression, image-guided intervention/treatment, and patient stratification against human diseases including NASH, ASH, IPF, COPD, and metastasis from multiple cancers.

    Acknowledgement: This work was supported in part by the NIH grants R01DK126080, R33CA235319, R42CA183376, R42AA025863, UT2AA028659, and S10OD027045 to Jenny Yang

    1. Salarian M, Turaga RC, Xue S, Nezafati M, Hekmatyar K, Qiao J, Zhang Y, Tan S, Ibhagui OY, Hai Y, Li J, Mukkavilli R, Sharma M, Mittal P, Min X, Keilholz S, Yu L, Qin G, Farris AB, Liu ZR, Yang JJ. Early detection and staging of chronic liver diseases with a protein MRI contrast agent. Nat Commun. 2019;10(1):4777. Epub 2019/10/31. doi: 10.1038/s41467-019-11984-2. PubMed PMID: 31664017; PMCID: PMC6820552.
    2. Tan S, Yang H, Xue S, Qiao J, Salarian M, Hekmatyar K, Meng Y, Mukkavilli R, Pu F, Odubade OY, Harris W, Hai Y, Yushak ML, Morales-Tirado VM, Mittal P, Sun PZ, Lawson D, Grossniklaus HE, Yang JJ. Chemokine receptor 4 targeted protein MRI contrast agent for early detection of liver metastases. Sci Adv. 2020;6(6):eaav7504. Epub 2020/02/23. doi: 10.1126/sciadv.aav7504. PubMed PMID: 32083172; PMCID: PMC7007242.
    3. Xue S, Yang H, Qiao J, Pu F, Jiang J, Hubbard K, Hekmatyar K, Langley J, Salarian M, Long RC, Bryant RG, Hu XP, Grossniklaus HE, Liu ZR, Yang JJ. Protein MRI contrast agent with unprecedented metal selectivity and sensitivity for liver cancer imaging. Proceedings of the National Academy of Sciences of the United States of America. 2015;112(21):6607-12. doi: 10.1073/pnas.1423021112. PubMed PMID: 25971726; PMCID: 4450423.
    4. Turaga RC, Yin L, Yang JJ, Lee H, Ivanov I, Yan C, Yang H, Grossniklaus HE, Wang S, Ma C, Sun L, Liu ZR. Rational design of a protein that binds integrin alphavbeta3 outside the ligand binding site. Nat Commun. 2016;7:11675. Epub 2016/06/01. doi: 10.1038/ncomms11675. PubMed PMID: 27241473; PMCID: PMC4895024.
  • September 16, 2021, at noon via Webex

    PET-MR Imaging of Hypoxia and Vascularity in Breast Cancer

    Julia Carmona-Bozo, MD

    Clinical Research Collaborator
    Breast Imaging Program
    University of Cambridge, UK


    Breast cancer is the most common cancer in the UK and in women globally. Imaging methods like mammography, ultrasound (US) and magnetic resonance imaging (MRI) play an important role in the diagnosis and management of breast cancer; they are generally utilized to provide anatomical or structural description of tumors in the clinical setting. It is widely accepted that the tumor microenvironment influences the phenotype, progression and treatment of breast cancer. This gave the impetus to move beyond tumor visualization in images to radiomics in order to provide additional disease characterization and early biomarkers of tumor response.

    Due to their ability to assess physiological processes in vivo, positron emission tomography (PET) and MRI can provide non-invasive characterization of the tumor microenvironment, including perfusion, vascular permeability, cellularity and hypoxia, which is associated with poor clinical outcome and metastasis. Clinical imaging studies in breast tumors have hitherto assessed tumor physiological parameters separately, with only few directly comparing data from these modalities. To this end, hybrid PET-MRI represents an attractive option as it can allow examination of dynamic functional processes and features of tumors simultaneously, while also conferring methodological advantages to the way imaging information is combined.

    The main aim of this work is to provide a better understanding of breast cancer pathophysiology using simultaneous PET and multi-parametric MRI. In particular, this work aims to explore relationships between imaging biomarkers of tumor vascularity measured by dynamic contrast-enhanced (DCE) MRI, cellularity using diffusion-weighted imaging (DWI) and hypoxic status using 18F-fluoromisonidazole (18F-FMISO) PET. Correlations between functional PET-MRI parameters and immunohistochemical (IHC) biomarkers of hypoxia and vascularity as well as MRI morphological tumor descriptors are also presented. This study concludes with an investigation of the utility of MRI markers of perfusion to quantitatively monitor and predict pathological response in patients undergoing neo-adjuvant chemotherapy (NACT) as surrogate markers of hypoxia.

  • July 20, 2021, at noon via Webex

    Pinpointing the Site(s) of Neural Impairment in Amblyopia

    Bas Rokers, PhD

    Associate Professor of Psychology
    Director of the Neuroimaging Facility
    New York University Abu Dhabi


    Effective treatment of perceptual disorders such as amblyopia requires that we pinpoint the site(s) of neural impairment. Classic results indicate that the reduced visual acuity and contrast sensitivity in amblyopia are associated with smaller cell bodies in the LGN of the thalamus and a weaker drive of activity in cortex. However, it is unknown if the LGN is the first site of neural impairment, or if earlier retino-thalamic projections are affected as well.

    We used diffusion MRI to quantify the white matter integrity of the retino-thalamic pathway in amblyopes and age-matched controls. We found reductions in fractional anisotropy in both the optic nerve and optic tract compartments of the retino-thalamic pathway of amblyopes. Moreover, when comparing between anisometropic and strabismic subtypes of amblyopia, we found that much of the effect was driven by anisometropic amblyopia.

    These results suggest that the perceptual deficits that characterize amblyopia are due in part to impairments in the earliest segments of the brain’s visual processing pathway. Moreover, the treatment of the disorder may require different interventions and timecourses depending on the type of amblyopia. Future work should separately consider the impact of anisometropic and strabismic amblyopia, and carefully re-consider if the optic nerve impairments may be detected using retinal imaging methods.

  • July 13, 2021, at 10:00 a.m. via Webex

    Segmentation of Substantia Nigra and Red Nucleus in MRI Images: Application to Parkinson’s Disease

    Dibash Basukala, PhD

    PhD Researcher
    Computer Science
    University of Canterbury


    Accurate segmentation of substantia nigra (SN) and red nucleus (RN) is challenging, yet important for understanding brain diseases like Parkinson’s disease (PD). This work proposes improved algorithms to segment SN and RN from T2*-weighted images and quantitative susceptibility mapping (QSM) MRI. After optimising segmentation algorithms to produce reliable SN and RN segmentations, multiple MRI (QSM, R2*, diffusion tensor imaging, arterial spin labelling, and volume) metrics extracted from the SN and RN are compared across groups (both PD/healthy controls and across cognitive subgroups) and investigate relationships with global cognitive ability and motor function in PD employing Bayesian regression models, and interesting evidence of associations is obtained. The multi-modal MRI features are also utilized to distinguish healthy controls and PD using a linear support vector machine (SVM) classifier. Therefore, multiple imaging modalities measuring complementary tissue characteristics such as iron deposition, microstructural alterations, perfusion changes, and volume atrophy may be useful for monitoring several ongoing processes in midbrain nuclei in Parkinson’s disease and also could be helpful for the distinction between PD and controls.

  • July 6, 2021, at noon via Webex

    A Drunk Man’s Path through Brain Microstructure and Function

    Ileana Jelescu, PhD

    Research Staff Scientist
    CIBM MRI EPFL Section


    There is currently no microscopy technique that qualifies as in vivo and non-invasive… or is there? It had been a long winding road, but diffusion MRI combined with biophysical modeling may just fill that role. In this talk, we will (i) go through the most recent validation steps of the white matter microstructure model and its applications to characterize white matter pathology, (ii) start from scratch to build a relevant model for cortical gray matter, and (iii) take a fresh look at diffusion functional MRI as a promising alternative to BOLD, particularly at low field strength.

  • June 30, 2021, at noon via Webex

    Light Field Compression and Feature Extraction via Residual Convolutional Neural Network

    Eisa Hedayati, PhD

    Computational Science and Engineering
    Michigan Technological University


    Light field (LF) photography has properties such as refocusing, perspective change, occlusion removal that yields a breakthrough in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality improvements. We can extract all of these properties by LF post-processing. However, a high-quality LF is bulky that makes these post-processing computations time-consuming and challenging for real-time deployment. To reduce the wait-time for LF data compression, we have merged conventional image compression techniques with residual convolutional neural networks for LF compression to make the task of LF storing and streaming more than two orders of magnitude faster than the state-of-the-art. Also, we developed RefNet to extract a set of refocused images from the raw LF in real-time as an example of the credibility of the machine learning techniques for LF feature extraction. While our proposed RefNet is faster in estimating the refocused images than the classical methods, it is more robust than current state-of-the-art none-learning methods in color prediction where discretizing the image will cause artifacts.

  • June 22, 2021, at noon via Webex

    Trusting Yourself: Understanding the Neural Mechanisms Enabling Prior Knowledge Assisted Percept Recognition

    Jonathan Shor, MSc

    Graduate Student, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    The act of recognizing a percept involves matching the sensory input with one’s prior knowledge to arrive at a best fit. When input is ambiguous, the best fit may be so poor that recognition fails. However, this threshold varies among individuals, up to pathological cases in which the weight given to prior knowledge is so great that individuals “recognize” things that are not there as in the case of hallucinations often suffered by schizophrenia patients. Distinct sources of prior knowledge have been identified to influence recognition, including one-shot learning, lifelong learning, and expectation, but the neural mechanisms underlying this process are poorly understood. By using two paradigms that manipulate the deployment of prior knowledge while recording neural activity with 7T fMRI and electrocorticography (ECoG), I will test for a common neural process modulating the weight given to one-shot learning and lifelong prior knowledge in visual processing tasks. In this talk, I will present these paradigms along with some preliminary data I have collected, discuss further analysis plans, and possible follow-up studies.

  • June 8, 2021, at noon via Webex

    Automated 3D Segmentation and Morphometry of White Matter Ultrastructures

    Ali Abdollahzadeh

    PhD Candidate
    A.I. Virtanen Institute for Molecular Sciences
    University of Eastern Finland


    Three-dimensional electron microscopy (EM) techniques have enabled acquiring images of hundreds of micrometers of tissue with synaptic resolutions—images whose size can range from gigabytes to several petabytes. Applying manual or semi-automated methods for tracing and analyzing individual ultrastructures, even for a small section in such datasets, consumes hundreds of hours of experts’ time.

    We developed ACSON and DeepACSON pipelines to automatically segment the entirety of neuronal processes in multi-resolution EM volumes of white matter. In ACSON, we automatically segmented white matter ultrastructures in high-resolution small field-of-view EM volumes. In DeepACSON, we emphasized low-resolution EM imaging to cover larger fields of view where severe membrane discontinuities became unavoidable. DeepACSON performed convolutional neural network (CNN)-based semantic segmentation and cylindrical shape decomposition (CSD)-based instance segmentation. CSD is a top-down instance segmentation algorithm we designed to decompose under-segmented myelinated axons into their constituent axons, accounting for the tubularity of axons as a global objective.

    ACSON and DeepACSON segmented hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores, enabling comprehensive 3D morphometry of the white matter ultrastructures and capturing nanoscopic morphological alterations in healthy and pathological brains.

  • May 25, 2021, at noon via Webex

    Echo Planar Time-resolved Imaging for Efficient Brain MRI

    Fuyixue Wang

    PhD Candidate
    Medical Engineering and Medical Physics
    Harvard-MIT Division of Health Sciences and Technology


    The sensitivity and specificity of brain MRI are limited by the low image encoding efficiency, leading to long acquisition time and limited spatial resolution especially for in vivo imaging. In order to address this, this talk will present our newly developed acquisition method, Echo Planar Time-resolved Imaging (EPTI), which uses novel encoding strategies in the high-dimensional space, together with efficient data sampling schemes, to allow better use of multi-channel receiver coil arrays and shared data correlation to achieve high acceleration capability.

    EPTI has been extended and applied to improve the efficiency of quantitative relaxometry, functional and diffusion imaging. We demonstrate that the significantly improved imaging efficiency enables ultra-fast multi-parametric mapping at submillimeter isotropic resolution with an order-of-magnitude faster acquisition speed, functional MRI with higher neuronal specificity as well as dMRI with higher SNR efficiency and better structural integrity. The future application of the proposed techniques should improve the diagnosis power of clinical brain MRI and allow further understanding of the structural and functional organization of the human brain.

  • May 11, 2021, at noon via Webex

    Characterizing Intracortical Myeloarchitecture through Cortical Profiles

    Yu Veronica Sui, MA

    Graduate Student, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Intracortical myelin is a critical feature of the cortical mantle that is assumed to closely relate to high-order cognitive and behavioral functioning. Its abnormalities have also been implicated in a myriad of psychiatric and neurodegenerative disorders including schizophrenia and Alzheimer’s disease. One of the challenges in studying the cerebral cortex is the presence of non-uniformly distributed microstructural features across cortical layers. In this talk I’ll discuss how we may utilize myelin variations across the cortex and characterize cortical myeloarchitecture using cortical profiles sampled from high-resolution MRI images. Findings from applying this method in out schizophrenia dataset and the Human Connectome Project Aging dataset will be presented.

  • April 28, 2021, at noon via Webex

    Design and Safety of RF Antennas for Body MRI at Ultra-high Field

    Bart Steensma, PhD

    Postdoctoral Fellow
    UMC Utrecht

    No abstract was provided for this talk.

  • April 27, 2021, at noon via Webex

    In Vivo Imaging Using a Near-infrared Genetically Encoded Calcium Indicator

    Sarah Shaykevich

    Graduate Student, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Genetically encoded fluorescent calcium indicators are a crucial tool for preclinical neuroimaging. Most of these indicators have fluorescent excitation and emission ranges at visible wavelengths, with few reliable indicators existing in the biologically useful near-infrared range. In the past few years, some progress has been made on developing near-infrared indicators. I will present my ongoing in vivo work with NIR-GECO2G, a state-of-the-art near-infrared calcium indicator.

  • April 16, 2021, at noon via Webex

    Genetically Encoded MRI and Optical Reporters and Sensors

    Assaf A. Gilad, PhD

    Professor of Biomedical Engineering and Radiology
    Chief, Division of Synthetic Biology and Regenerative Medicine, Institute for Quantitative Health Science and Engineering
    Affiliated: Neuroscience program, department of Electrical and Computer Engineering, BEACON Center for the Study of Evolution in Action
    Michigan State University


    The use of advanced imaging technologies has increased significantly in the past two decades and has revolutionized patients’ treatment on a daily basis, in terms of earlier and more accurate diagnosis. Essentially, no critical medical decisions are taken without relying on some sort of imaging. In the future, these decision-making processes will rely, to an even greater extent, on molecular imaging, in which personalized imaging probes, designed for specific medical conditions, will be used for diagnosis and to assess treatment success, by allowing clinicians to monitor therapy non-invasively and over time. Dr. Gilad research is in the intersect of synthetic biology and molecular imaging. where his lab is implementing the principles of synthetic biology to develop cutting edge technologies for better understanding the central nervous system and cancer. We bioengineer genetically encoded gene circuits and novel fusion protein based on unique properties adopted from a variety of organisms. The Gilad lab has been focusing on bioengineering of genetically encoded reporters for MRI mostly based on chemical exchange saturation transfer (CEST). Using protein engineering tools and machine learning algorithms, we have improved the sensitivity and expended the arsenal of reporters. These reporters were implemented in array of in vivo models with an emphasis on neuroimaging and cancer. We complement our reporters with genetically encoded optical sensors that allow detecting neurotransmitters.

  • April 7, 2021, at 12:30 p.m. via Webex

    GRASP MRI: Past, Present and Future

    Li Feng, PhD

    Assistant Professor
    Icahn School of Medicine at Mount Sinai
    New York, NY


    The GRASP project, started from 2011, is 10 years old today! GRASP MRI represents years of innovation and efforts by a research team consisting of MRI physicists, clinician scientists and industry partners. To date, GRASP MRI has been successfully demonstrated in many clinical applications; its overall performance has been greatly improved after years of optimization; and it has also been extended to a number of new variants. In this talk, Li will take this opportunity to summarize the GRASP developments over the past decade and to discuss future directions that GRASP MRI could potentially be heading to. Of course, in the era of artificial intelligence, how to make a smart version of GRASP by incorporating the latest deep learning technology is an important question we have to think and plan. If you are interested in hearing the latest of this project, you won’t want to miss this story.

  • March 17, 2021, at 1:00 p.m. via Webex

    Reproducibility of 1H MRSI in Mild Traumatic Brain Injury

    Anna Chen, BS

    Graduate Student, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Traumatic brain injury (TBI) is a global health concern, with mild TBI (mTBI) accounting for 60%-80% of cases. TBI sequelae can be histologically explained by axonal varicosities known as diffuse axonal injury, but this pathology is not detectable using conventional CT and MRI. 1H MRS is a technique sensitive to neurochemical alterations which may enable more precise evaluation of TBI severity and prognostication when macroscopic structural damage is lacking. Unfortunately, varying results in regard to which metabolite(s) are most likely to be affected and what brain region(s) should be sampled, contribute to the limited clinical use of MRS in TBI. 1H MRSI has shed light on the regional distribution of metabolite findings, but a key part of translating the new knowledge to the clinic rests on determining how reproducible are the results of any particular study. This talk will present 1) initial data from a project intending to test the reproducibility of 1H MRSI findings from previous studies with a different mTBI cohort, 2) an outlook on future directions, as well as 3) recent findings from sodium imaging.

  • March 17, 2021, at 1:00 p.m. via Webex

    Microstructure of the Cortical Grey Matter

    Nima Gilani, PhD

    Postdoctoral Researcher
    Previously at the Department of Cognitive Neuroscience, Maastricht University


    Neurodegenerative diseases such as Alzheimer’s disease cause changes and disruption to cortical microstructure and architecture. MRI could potentially be sensitive to such changes. There is a growing interest in modelling human cortical areas using a combination of quantitative MRI and 3D microscopy ex vivo. This presentation contains a brief review of MR modalities that could be used for this purpose in addition to a Monte Carlo simulation study of DWI in light fluorescence microscopy samples.

  • March 16, 2021, at noon via Webex

    Building Deep Neural Networks to Find Small Lesions from Hundreds of Millions of Pixels

    Jungkyu Park, MS

    Doctoral Candidate, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Our effort at NYU School of Medicine towards building deep neural networks for Digital Breast Tomosynthesis (DBT) volumes ranked #1 at the DBTex challenge. In this international challenge, we built an AI system to find biopsy-proven lesions from the DBT volumes collected from the Duke University Hospital. In this talk, I will discuss how our team was able to reach the best performance on the external dataset by utilizing our own private datasets at NYU Langone and how the model outputs could benefit the radiologists.

  • March 3, 2021, at 2:00 p.m. via Webex

    Neuromodulation Technologies for Restoring and Augmenting Neuro-performance

    Galit Pelled, PhD

    Chief, Division of Neuroengineering, Institute for Quantitative Health Science and Engineering
    Michigan State University
    East Lansing, MI

    No abstract was provided for this talk.

  • March 2, 2021, at noon via Webex

    Measuring Apparent Water Exchange in Post-mortem Mouse Brains using Filter Exchange Imaging and Diffusion Time Dependent Kurtosis Imaging

    Chenyang Li, MS

    Doctoral Candidate, Biomedical Imaging and Technology
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Water exchange between compartments in the brain (e.g., the vascular, ventricular, extracellular, and intracellular spaces) is a crucial biological process for maintaining homeostasis and may serve as a biomarker for diagnosis of structural and functional deficits. FEXI and DKI(t) are promising diffusion MRI techniques for measuring apparent exchange in the brain. FEXI employs a double-diffusion-encoding scheme to filter tissue compartment based on differences in diffusivities and measures the recovery in diffusion measurements over an increasing mixing time, characterized by Apparent Exchange Rate (AXR). DKI(t), on the other hand, measure apparent exchange based on its effects on the asymptotic decay of diffusion kurtosis described by the Kärger model. In this study, we investigated the relationship between FEXI and DKI(t) based measurements of apparent exchange in post-mortem mouse brains and elucidated its confounding factors in determining the desired exchange process.

  • February 24, 2021, at 2:00 p.m. via Webex

    Fat or Fit: Focus on Epicardial Adiposity Phenotypes

    Jadranka Stojanovska, MD, MS

    Clinical Assistant Professor, Radiology
    Director, Cardiac MRI Service, Cardiothoracic Radiology Division
    University of Michigan


    The parallel growth of obesity and diabetes has escalated over the last four decades placing over 1.9 billion overweight and obese individuals at increased risk of developing cardiovascular disease (CVD). This risk has been attributed to the pressure of a low-grade inflammatory state, but the mechanism underlying the inflammation is unclear. An increased epicardial adipose tissue volume or thickness quantified by echocardiography, computed tomography (CT) or magnetic resonance (MR) has been shown to correlate with cardiovascular disease and diabetes independent of anthropometric measurements such as body mass index. However, in visceral obesity, epicardial adipose tissue can assume a white adipose phenotype that is hypothesized to be associated with proinflammatory markers. The white adipose tissue may precede the accumulation of fat and increase in epicardial adipose volume. The objectives for this talk are to discuss the current theories of defining cardiovascular or cardiometabolic risk, what research has been done by others and our group that could leverage future utilization of imaging as a surrogate marker of identifying patients at risk for adverse CVD outcome. We will emphasize the research performed by our group to understand the correlation between the increased epicardial edipose fat fraction quantified by water-fat imaging and coronary artery disease including tissue inflammation defined by lipidome and transcriptome profiling in patients undergoing open-heart surgery. Epicardial, extrapericardial, and subcutaneous depots expressed different imaging, lipidome and transcriptome signatures. Furthermore, increased epicardial fat fraction positively correlated with coronary artery disease, tissue ceramides, a pro-inflammatory lipids, and proinflammatory gene expressions. We will discuss research questions and future direction of utilizing epicardial fat fraction to risk stratify CVD patients and monitor therapeutic response.

  • February 16, 2021, at noon via Webex

    Microvascular and Microstructural Changes in Psychotic Spectrum Disorders Relate to Cognition, Disease Duration and Metabolites: A Multiparametric Imaging Study

    Faye McKenna, MS

    PhD Candidate in Biomedical Imaging & Technology
    Lazar Translational Brain Imaging Lab
    Vilcek Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Previous research has suggested both perfusion and free water (FW) alterations in Psychotic Spectrum Disorders (PSD), assessed independently of each other. To study PSD neuropathology, we applied a three-compartment IVIM-FWI model which disentangles FW diffusion and perfusion along with an anisotropic diffusion tissue compartment. The estimation of each of these metrics may be affected when the effects of the other are not taken into consideration. Previous histological studies have suggested an array of microvascular and microstructural deficits likely to impact perfusion and FW in PSD, including increased inflammation, morphological differences in capillaries, and disruptions in the neurovascular unit cells and the blood brain barrier. The aim of this research was to evaluate, for the first time, if the three compartment IVIM-FWI model can describe microvascular and microstructural changes in PSD in both gray and white matter. Additionally, we examined the relationships between the IVIM-FWI derived measures of perfusion fraction (PF), FW, and fractional anisotropy of tissue (FAt) and psychosis duration, cognition, and MR spectroscopy metabolites.

To the top ↑

2020 Lectures

  • December 17, 2020, at 2:00 p.m. via Webex

    MRI reconstruction and pulse design for accelerated neuroimaging

    Tianrui Luo, MSE

    PhD Candidate
    Functional MRI Lab
    University of Michigan


    Pulse design and reconstruction are two important topics in MR research for enabling faster imaging. On the pulse design side, selective excitations that confine signals to be within a small ROI instead of the full imaging FOV can promote sampling sparsity in the k-space, as a direct outcome of the change of the corresponding Nyquist sampling rate.

    On the reconstruction side, besides improving algorithms’ capability on restoring images from less data, another objective is to reduce the reconstruction time, particularly for dynamic imaging. This talk presents our developments on these two perspectives: The first part introduces a pulse design framework built on our efficient auto-differentiable Bloch simulator. By propagating the derivatives in an automatic way, this tool connects excitation objectives (e.g., accuracy) directly to the pulse waveforms to be designed without approximations such as the small-tip model. It enables us to address excitation losses that are previously not approachable. We apply this tool on outer volume saturated inner volume imaging, which confines imaging signals into an ROI by selectively spoiling spin magnetizations outside.

  • November 24, 2020, at 10:00 a.m. via Webex

    MRI Assessment of Renal Tubular Volume Fraction with DWI-Continuum Modeling

    Joao Periquito, MSE

    PhD Candidate
    Max-Delbrück-Centrum für Molekulare Medizin
    Berlin Ultrahigh Field Facility


    Renal tissue hypoxia is considered to be an important factor in the development of numerous acute and chronic kidney diseases. Blood oxygenation sensitized MRI can provide quantitative information about changes in renal blood oxygenation via mapping of T2*. Simultaneous MRI and invasive physiological measurements in rat kidneys demonstrated that changes in renal T2* do not accurately reflect renal tissue oxygenation under pathophysiological conditions. Confounding factors that should be taken into account for the interpretation of renal T2* include renal blood volume fraction and tubular volume fraction. Tubuli represent a unique structural and functional component of renal parenchyma, whose volume fraction may rapidly change, e.g., due to alterations in filtration or tubular outflow.

    Diffusion-weighted imaging (DWI) provides a method for in-vivo evaluation of water mobility. In the kidneys intravoxel incoherent water motion may be linked to three different sources: i) renal tissue water diffusion, ii) blood perfusion within intrarenal microvasculature and iii) fluid in the tubules. The latter provides means to probe for changes in the tubular volume fraction. Recognizing this opportunity this presentation examines the feasibility of assessing tubular volume fraction changes using the non-negative least squares (NNLS) analysis of DWI data.

  • October 29, 2020, at 9:00 a.m. via Webex

    PET/MR Attenuation Correction

    Hongyu An, PhD

    Associate Professor of Radiology
    MIR, Mallinckrodt Institute of Radiology
    Washington University School of Medicine in St. Louis

    No abstract was provided for this talk.

  • October 28, 2020, at 2:00 p.m. via Webex

    Beyond B0 shimming: Emerging applications for local magnetic field control in MRI

    Jason Stockmann, PhD

    Assistant Professor
    Department of Radiology
    Massachusetts General Hospital


    This talk will explore new ways to use local magnetic field control besides conventional “B0 shimming”. Perturbations of the main magnetic field (“B0”) due to tissue susceptibility interfaces are a long-standing obstacle in Magnetic Resonance applications. Inhomogeneous B0 fields can lead to artifacts such as geometric distortion, signal voids, poor RF pulse performance, and spectral line broadening. This has limited the use of diffusion, functional, and spectroscopic MR imaging in many regions of the brain and body. Recently, it has been shown that multi-coil arrays of independently-driven loops placed close to the body can generate nonlinear, high spatial-order field offsets to “shim out” unwanted susceptibility fields on a subject-specific basis, benefiting field homogeneity and image quality. In this talk, we explore the potential for repurposing multi-coil shim arrays for new applications that exploit their nonlinear, rapidly-switchable local field offsets. Examples include tailored field offsets for improved lipid suppression in MR spectroscopic imaging; zoomed functional MRI of target brain anatomy; flip angle correction at ultra-high field; and supplementary spatial encoding for improved parallel imaging. We will also explore ways to add local field control capability to coil arrays originally designed for other applications, such as RF receive arrays and Transcranial Magnetic Stimulation probe arrays, so that their degrees of freedom can be brought to bear.

  • October 21, 2020, 2:00 p.m. via Webex

    Myelin Water Imaging: Past, Present & Future

    Corree Laule, PhD

    Associate Professor
    University of British Columbia


    The presentation will provide a broad overview of the history of myelin water imaging in humans. Myelin water imaging is based on measurement of the short T2 component of water in brain and spinal cord tissue. What began as a lengthy single slice, single center measurement has expanded to many countries on multiple continents in just over 25 years. Important work along the way has included post-mortem validation studies in human CNS tissue, comprehensive assessment of development and normal characterization in adults, as well application to many neurological diseases including multiple sclerosis, concussion, stroke and beyond. The creation of normative atlases and development of faster analysis approaches promises to help move myelin water imaging to clinic in the coming decade.

  • September 30, 2020, at 2:00 p.m. via Webex

    Investigating Cortical Microstructure in Parkinson Disease Patients Using Diffusion Magnetic Resonance Imaging

    Wafaa Sweidan, MS

    PhD Candidate in Translational Neuroscience
    Graduate Research Fellow
    Sastry Foundation Advanced Imaging Laboratory
    Department of Psychiatry and Behavioral Neurosciences
    Wayne State University
    Detroit, MI


    Parkinson disease (PD) is a neurodegenerative disorder characterized pathologically by nigrostriatal dopaminergic terminal loss and the development of Lewy pathology in surviving neurons of the substantia nigra (SN). Lewy pathology extends beyond the SN, and can be found in limbic and prefrontal cortical regions associated with cognitive decline. In vivo assessment of cortical microstructure and the extent of pathological changes will be clinically useful to monitor disease progression. For this purpose, our study used two diffusion MRI models, diffusion tensor imaging and neurite orientation dispersion and density imaging, to study the microstructural changes in the cerebral cortex of PD participants (n=18) compared to healthy controls (n=8). We demonstrate that in the absence of cortical thinning, PD pathology is associated with significant abnormalities in cortical diffusion metrics. Specifically, we found that the anterior cingulate cortex and inferior temporal lobe are consistently involved in PD through reductions in the intracellular volume fraction, fractional anisotropy (FA) and increased orientation dispersion index. FA reductions were extensive and involved more limbic areas such as entorhinal cortex, parahippocampus and insula. These findings are consistent with the presence of Lewy pathology in limbic regions and might be reflecting the earliest stages of tissue involvement in PD.

  • September 29, 2020, at 2:00 p.m. via Webex

    Cardiac Magnetic Resonance Fingerprinting

    Nicole Seiberlich, PhD

    Associate Professor
    Department of Radiology
    University of Michigan


    Cardiovascular Magnetic Resonance (CMR) is a valuable tool that enables non-invasive characterization of tissue and assessment of cardiac function. Parametric mapping techniques play an important role in CMR due to their sensitivity to physiological and pathological changes in the myocardium. The capability of mapping T1 and T2 simultaneously in a single scan makes the novel cardiac Magnetic Resonance Fingerprinting (cMRF) technique a promising technology to facilitate diagnosis and treatment evaluation in various cardiac diseases. Unlike conventional parametric mapping approaches which may yield different T1 or T2 values for the same subject depending on the specifics of the MRI system hardware or pulse sequence implementation, cMRF has the potential to offer reproducible measurements of tissue properties on all MRI scanners. This talk aims to introduce the basics of the cMRF technique, including pulse sequence design, dictionary generation, and pattern matching, as well as highlighting potential applications.

  • September 22, 2020, at 2:00 p.m. via Webex

    Low field MRI: hardware, data acquisition, image processing, sustainability and in vivo applications

    Andrew Webb, PhD

    Professor, Director C.J. Gorter Center for High Field MRI
    Department of Radiology
    Leiden University Medical Center


    Commercial magnetic resonance imaging (MRI) systems cost millions of euros to purchase, require large electromagnetically shielded spaces to house, are extremely expensive to maintain and require highly trained technicians to operate. These factors together means that their distribution is confined to centrally-located medical centres in large towns and cities. Globally over 70% of the world’s population has absolutely no access to MRI, and clinical conditions which could benefit from even very simple scans cannot be treated. In the financially developed world, although MRI is diagnostically very important, the high cost and fixed nature prohibits any type of role in widespread health screening, for example. The magnetic fields typically used are very high, which means that there are severe contraindications so that, for example, MRI cannot currently be used in the emergency room. From the considerations above it is clear that if low-field MRI could be made more portable, accessible and sustainable then it would open up new opportunities in both developed and developing countries.

    Rather than designing a highly sophisticated and expensive piece of equipment that can be used for all types of scanning, we use the philosophy of tailored design, such that we can design much more inexpensive systems for specific medical applications. Thus rather than one large MRI, the model is similar to having tens of different mobile ultrasound machines in a medical facility. In order to achieve portability, we design systems that use thousands of very small low-cost permanent magnets, arranged in designs which have no fringe field and therefore very easy siting requirements. The low magnetic fields allow scanning of patients with implants, and the scanner could potentially be transported on an ambulance for differentiation of hemorrhagic or ischemic stroke, for example. This talk will cover aspects of magnet, gradient and RF coil design for low fields (~50 mT) , as well as corrections for gradient- and B0-distortions, and present the latest in vivo results as well as an outlook on future developments.

  • August 5, 2020, at 2:00 p.m. via Webex

    Artificial Intelligence System for Predicting the Deterioration of Patients with COVID-19 in the Emergency Department

    Farah Shamout, DPhil

    Assistant Professor/Emerging Scholar of Electrical and Computer Engineering
    Engineering Division
    NYU Abu Dhabi


    There is a pressing need to identify deterioration amongst patients with COVID-19 in order to avoid life-threatening adverse events. Chest radiographs are frequently collected from patients presenting with COVID-19 upon arrival to the emergency department, since it is considered as a first-line triage tool and the disease primarily manifests as a respiratory illness. In this talk, I will discuss the AI prognosis system we developed using data collected at NYU Langone Health to predict in-hospital deterioration, defined as the occurrence of intubation, mortality, or ICU admission. In particular, our system consists of an ensemble of an interpretable deep learning model to learn from chest X-ray images and a gradient boosting model to learn from routinely collected clinical variables, e.g. vital signs and laboratory tests. The system also computes deterioration risk curves to summarise how the risk is expected to evolve over time. The results of retrospective validation on the held-out test set, the reader study, and silent deployment in the hospital infrastructure highlight the promise of our AI system in assisting front-line workers through real-time assessment of prognosis.

  • July 22, 2020, at 2:00 p.m. via Webex

    Characterizing tissue microstructure in the living human brain using high-gradient diffusion MRI and ultrafast susceptibility-weighted Imaging

    Susie Y. Huang, MD, PhD

    Assistant Professor
    Athinoula A. Martinos Center for Biomedical Imaging
    Department of Radiology
    Massachusetts General Hospital, Harvard Medical School


    Less is known about the structure-function relationship in the human brain than in any other organ system. The challenge of studying brain structure is that brain networks span multiple spatial scales, from individual neurons all the way to whole-brain systems. Diffusion magnetic resonance imaging (MRI) holds great promise among noninvasive imaging methods for probing cellular structure of any depth and location in the living human brain. Robust methods for in vivo mapping of tissue microstructure by diffusion MRI remain elusive due to the demand for fast and strong diffusion-encoding gradients. I will present an overview of our group’s efforts to advance MR hardware, biophysical modeling, and validation of microstructural metrics derived from diffusion MRI in order to probe the structure of the human brain across multiple scales. I will review current progress and applications of these methods to study axonal microstructure in the normal and aging human brain and assess axonal damage in multiple sclerosis.

    To bridge the divide between the neuroscientific and clinical use of MRI in probing tissue microstructure, this presentation will also provide an overview of our ongoing efforts to optimize, translate and validate novel encoding and reconstruction techniques for the ultrafast acquisition of high-resolution, multi-contrast MR images in a clinical setting. These efforts are exemplified in our recent work exploring the benefits of improved speed and resolution of ultrafast susceptibility-weighted imaging to study microvascular injury in patients with severe COVID-19 using radiologic-pathologic correlative examinations.

  • July 15, 2020, at 2:00 p.m. via Webex

    Chemical Exchange Saturation Transfer (CEST) and Inhomogeneous Magnetization Transfer (ihMT) for Molecular and Microstructural Contrast in Human MRI

    Elena Vinogradov, PhD

    Associate Professor
    Radiology Department and Advanced Imaging Research Center
    UT Southwestern Medical Center


    Recently, methods employing single- and dual-frequency saturation are gaining recognition to detect events on microstructural and molecular level. Specifically, Chemical Exchange Saturation Transfer (CEST) employs selective saturation of the exchanging protons and subsequent detection of the water signal decrease to create images that are weighted by the presence of a metabolite or pH1. Here, we will describe aspects of translating CEST to reliable clinical applications at 3Tesla and discuss its potential uses in human oncology, specifically breast cancer. Second, we will discuss a method called inhomogeneous Magnetization Transfer2 (ihMT), which employs dual-frequency saturation to create contrast originating from the residual dipolar couplings and thus specific to microstructure. We will focus on principles of ihMT, its comparison to other white matter metrics (diffusion) and the methods application to the detection of myelin in brain and spinal cord.

  • May 27, 2020 at 11:00 a.m. via Webex

    Bent Folded-End Dipole Head Array for Ultra-High-Field Magnetic Resonance Imaging Turns “Dielectric Resonance” from an Enemy to a Friend

    Nikolai Avdievitch, PhD

    Senior Research Scientist
    High-Field MR Center
    Max Planck Institute for Biological Cybernetics
    Tubingen, Germany


    Due to a substantial shortening of the RF wave length (below 15 cm at 7T), RF magnetic field at UHF has a specific transmit (Tx) excitation pattern with strongly decreased (more than 2 times) values at the periphery of a human head. This effect is seen not only in the transversal slice but also in the coronal and sagittal slices, which considerably limits the longitudinal Tx-coverage (along the magnet’s axis) of conventional surface loop head arrays. In this work, we developed a novel human head UHF array consisted of 8 transceiver folded-end dipole antennas circumscribing a head. Due to the asymmetrical shape of the dipoles (bending and folding) and the presence of an RF shield near the folded portion, the array simultaneously excites two modes, i.e. a circular polarized mode of the array itself, and the TE mode (“dielectric resonance”) of the human head. Mode mixing can be easily controlled by changing the length of the folded portion. Due to this mixing, the new dipole array improves longitudinal coverage as compared to unfolded dipoles. By optimizing the length of the folded portion, we can also minimize the peak local SAR value and decouple adjacent dipole elements.

  • May 20, 2020, at 2:00 p.m. via Webex

    COVID-19: The Evolving Role of Chest Imaging

    Georgeann McGuinness, MD

    Associate Dean for Mentoring and Professional Development
    Professor and Senior Vice Chair of Radiology
    Vice Chair of Academic Affairs
    Director, Clinical Faculty Mentoring
    NYU Langone Health


    This lecture will provide a brief clinical overview of SARS-CoV-2 infection and COVID-19 manifestations in the lungs. Imaging findings in the chest will be defined and literature reports summarized. Our evolving clinical experience will be described, including the subacute and chronic manifestations of COVID-19 lung disease we are now seeing. Finally, completed and ongoing thoracic COVID research projects will be presented.

  • May 13, 2020, at 2:00 p.m. via Webex

    Computational Approaches for Efficient MRI: Applications in Neuroscience Research

    Merry Mani, PhD

    Director, Microstructure Imaging Lab
    Assistant Professor of Radiology – Division of Neuroradiology
    University of Iowa


    Magnetic Resonance Imaging has revolutionized the field of neuroscience by providing a non-invasive means to study the brain, to understand its organization, specialization and anomalies in an unprecedented manner. Despite the rapid advances in MRI instrumentation, it is still challenging to achieve high quality data in an efficient manner for several MR imaging modalities, especially for those modalities involving multi-dimensional imaging. In this talk, I will discuss several computational approaches that we have developed to achieve high efficiency MR imaging to enable many applications. These approaches strive to achieve high resolution, high SNR and artifact-free MRI by jointly optimizing the contribution of MR acquisition, the signal modeling under investigation and the reconstruction methods to provide meaningful information in an efficient manner. In this talk, I will focus the discussion mainly on diffusion magnetic resonance imaging and our work towards improving the efficiency of this modality.

    Speaker Bio

    Merry Mani received her PhD in 2014 from the University of Rochester, NY. Later in 2014, she joined the Magnetic Resonance Research Facility at the University of Iowa as a post-doctoral research fellow, where she developed new imaging methods on the 7T MRI. In 2019, she became an Assistant Professor in the department of Radiology, Carver College of Medicine, University of Iowa. Her lab focuses on integrating cross-disciplinary tools such as signal modeling and signal processing with imaging physics and image analysis tools to enable high efficiency MRI. These include the development of novel pulse sequences and optimization of sampling trajectories and reconstruction methods for maximum performance.

  • May 6, 2020 at 2:00 p.m. via Webex

    Nonlinear gradients for spatial encoding and contrast

    Gigi Galiana, PhD

    Associate Professor
    Radiology and Biomedical Imaging
    Yale University School of Medicine


    Like standard gradients, nonlinear gradients modulate the magnitude of Bz as a function of position; the difference is that the magnitude as a function of position is generally not linear or unidirectional. One important consequence of gradient nonlinearity is that the modulation of spins is no longer sinusoidal, so MR data do not correspond to points in k-space. Therefore, early encoding strategies focused on optimizing sequences by considering encoding in the spatial domain. However, a k-space analysis of nonlinear encoding provides significant insights on sequence design and suggests novel strategies, such as FRONSAC encoding. With FRONSAC, most of the encoding comes from a standard linear trajectory (e.g. Cartesian, radial or spiral), but nonlinear gradients are used to effectively increase the width of the k-space trajectory. For an undersampled scan, the additional width reduces gaps in k-space and improves reconstructions, but most other properties of the underlying linear method are unchanged. For example, Cartesian-FRONSAC retains features like insensitivity to off-resonance spins and timing delays, ease of changing FOV, resolution, and orientation, and relatively simple contrast behavior, while still allowing for higher undersampling factors. This versatile approach can be added to nearly any sequence, improving undersampling artifacts even for low channel arrays, as we have shown by acquiring a full FRONSAC-enhanced brain protocol in a cohort of healthy subjects.

    An additional emerging application of nonlinear gradients is in generating diffusion contrast. In some sense, a linear gradient is the maximally egalitarian way to distribute a ΔB(x): it generates the same Gx (d(ΔB)/dx) everywhere, but the peak Gx across the FOV is the lowest possible. By allowing nonlinearity, Gx is different at each voxel, but it can be concentrated to certain regions of interest. Thus, for specialized applications, it may be possible to achieve massive gradients strengths and very high diffusion weightings using simple equipment. For example, for prostate DWI, we propose an inside-out nonlinear gradient, which simulations suggest will ultimately double CNR in ADC maps.

  • April 29, 2020 at 2:00 p.m. via Webex

    Histotripsy: Image-guided Ultrasound Therapy for Non-invasive Surgery

    Zhen Xu, PhD

    Associate Professor and Associate Chair of Graduate Education
    Department of Biomedical Engineering
    University of Michigan


    Wouldn’t it be great to perform a surgery without incision or bleeding? “Histotripsy” is the first non-invasive, non-ionizing, and non-thermal ablation technique that is invented by Dr. Xu and her colleagues at the University of Michigan. Using ultrasound pulses applied from outside the body and focused to the target diseased tissue, histotripsy produces a cluster of energetic microbubbles at the target tissue using the endogenous gas pockets with millimeter accuracy. These microbubbles, each similar in size to individual cells, function as “mini-scalpels” to mechanically fractionate cells to acellular debris in the target tissue. The acellular debris is absorbed over time via metabolism, resulting in effective tissue removal. Off-target tissue remains undamaged and no incision is needed. Thus histotripsy can perform non-invasive surgery guided by real-time imaging. Histotripsy has potential for many clinical applications where non-invasive tissue removal is desired. Recent research in Dr. Xu’s lab also shows potent immune response and abscopal effects induced by histotripsy and its potential for immunotherapy. Dr. Xu will talk about the mechanism and instrumentation development of histotripsy as well as the latest pre-clinical and clinical studies of histotripsy for cancer, neurological, cardiovascular, and immunotherapy applications.


    Zhen Xu is a tenured Associate Professor and Associate Chair of Graduate Education at the Department of Biomedical Engineering at the University of Michigan, Ann Arbor, MI. She received the Ph.D. degree in biomedical engineering from the University of Michigan in 2005. Her research focuses on ultrasound therapy and imaging, particularly histotripsy. She received the IEEE Ultrasonics, Ferroelectrics, and Frequency Control (UFFC) Outstanding Paper Award in 2006; National Institute of Health (NIH) New Investigator Award at the First National Institute of Biomedical Imaging and Bioengineering (NIBIB) Edward C. Nagy New Investigator Symposium in 2011, The Federic Lizzi Early Career Award from The International Society of Therapeutic Ultrasound (ISTU) in 2015, the Fellow of American Institute of Medicine and Bioengineering in 2019, and The Lockhart Memorial Prize for Cancer Research in 2020. She is an associate editor for IEEE Transactions on UFFC and Frontiers in Bioengineering and Biotechnology, Deputy VP of UFFC Ultrasonics Standing Committee, and an elected board member of ISTU. She is a principal investigator of grants funded by NIH, Office of Navy Research, American Cancer Association, and Focused Ultrasound Foundation. She is also co-founder of HistoSonics, a startup company developing histotripsy for oncological applications.

  • April 22, 2020 at 2:00 p.m. via Webex

    A Collaborative CAD System (C-CAD) for Radiological Applications with Eye-Tracking, Sparse Attentional Model, and Deep Learning

    Ulas Bagci, PhD

    Principal Investigator
    Center for Research in Computer Vision (CRCV)
    University of Central Florida


    Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. However, most relevant studies in this aspect are not compatible with realistic radiology reading rooms. In this talk, I will share our unique experience for developing a paradigm shifting computer aided diagnosis (CAD) system, called collaborative CAD (C-CAD), that unifies CAD and eye-tracking systems in realistic radiology room settings. In other words, we are creating artificial intelligence (AI) tools that get benefits from human cognition and improve over complementary powers of AI and human intelligence. We first developed an eye-tracking interface providing radiologists with a real radiology reading room experience. Second, we proposed a novel computer algorithm that unifies eye-tracking data and a CAD system. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve their diagnostic decisions. The proposed C-CAD system has been tested in a lung and prostate cancer screening experiment with multiple radiologists. More recently, we also experimented brain tumor segmentation with the proposed technology leading to promising results. In the last part of my talk, I will describe how to develop AI algorithms which are trusted by clinicians, namely “explainable AI algorithms”. By embedding explainability into black box nature of deep learning algorithms, it will be possible to deploy AI tools into clinical workflow, and leading into more intelligent and less artificial algorithms available in radiology rooms.

  • April 15, 2020 at 2:00 p.m. via Webex

    Towards improved inference on connectional anatomy from diffusion MRI

    Anastasia Yendiki, PhD

    Associate Professor, Harvard Medical School
    Associate Investigator, Massachusetts General Hospital
    Athinoula A. Martinos Center for Biomedical Imaging


    This talk will provide an overview of work that our group has done on mapping connectional anatomy from diffusion MRI, and a preview of where this path might lead us next. First, I will discuss our previously developed algorithms for reconstructing white-matter pathways from diffusion MRI. These include both supervised and unsupervised methods with a common theme: like neuroanatomists, they define white-matter bundles based on relative position with respect to neighboring anatomical structures, rather than based on absolute coordinates in a template space. This makes them robust to individual variability and to the effects of disease or healthy development and aging.

    Second, I will present results from recent post mortem validation studies, where we have evaluated the accuracy of diffusion MRI with respect to polarization-sensitive optical coherence tomography in human samples, or chemical tracing in non-human primates. Our results suggest that existing methods for inferring the orientation of axon bundles from diffusion MRI do not benefit substantially from very high b-values. This implies that our analysis tools have not kept up with the rapid progress of our hardware, and that new tools are needed to fully take advantage of the data that can be acquired by today’s ultra-high-gradient MRI scanners. I will end the talk by discussing how we may be able to address this, by using the post mortem data not only to evaluate existing methods but to engineer the next generation of tractography algorithms.

  • April 8, 2020 at 2:00 p.m. via Webex

    Characterization of Cortical Myelin Deficits in Schizophrenia Spectrum Disorders using Quantitative Magnetization Transfer Imaging

    Yu Veronica Sui, MA

    PhD Student
    Biomedical Imaging and Technology Program
    Sackler Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Myelin abnormalities in schizophrenia spectrum disorders have been suggested by histological studies, which have shown aberrations in myelin lamellae, oligodendrocyte structure, and myelin- and oligodendrocyte-related gene expression. However, in vivo examination of myelin content, especially the intra-cortical myeloarchitecture remains limited. In our current project, we employ magnetization transfer imaging to derive macromolecular proton fraction (MPF), a quantitative estimate of myelin content. This talk will focus on data suggesting a flattening of the cortical myelin profile in patients with schizophrenia spectrum disorders and an association of cortical myelin alterations with illness progression and cognitive outcomes. Preliminary findings on whole-brain myeloarchitectural similarity changes in schizophrenia will also be presented.

    Speaker Bio

    Yu Veronica Sui is a second-year graduate student in Sackler Institute’s Biomedical Imaging and Technology training program working with Mariana Lazar. She has a background in cognitive psychology and is interested in developing and employing new imaging and analytics methods to characterize the neural bases of psychiatric disorders. Her focus in Lazar Lab is psychosis-related pathological changes in the brain, including both microstructural and connectivity abnormalities.

  • March 3, 2020 at noon

    Adventures in Quantitative Magnetic Resonance Imaging

    Mark Does, PhD

    Professor of Biomedical Engineering
    Vanderbilt University
    Nashville, TN


    An alluring feature of magnetic resonance imaging (MRI) is its potential to provide quantitative and specific characterizations of tissue. However, the barriers to the realization of quantitative MRI (qMRI) are many and progress has been slow. This presentation will include vignettes of technical, experimental, and translational efforts to develop and utilize qMRI, with primary applications being the characterization of white matter micro-structure/composition and bone fracture risk.

  • February 11, 2020 at noon

    Functional Optoacoustic Neuro-Tomography

    Sarah Shaykevich

    PhD Student
    Biomedical Imaging and Technology PhD Training Program
    Sackler Institute of Graduate Biomedical Sciences
    NYU Langone Health

    No abstract was provided for this talk.

  • February 7, 2020 at noon

    Opportunities in clinical imaging using a high-performance 0.55T MRI system

    Adrienne Campbell-Washburn, PhD

    Director, MRI Program
    National Heart, Lung, and Blood Institute (National Institutes of Health)


    Lower field strength MRI systems paired with high-performance hardware and advanced imaging methods offer unique opportunities for clinical imaging. Specifically, this system configuration offers improved safety for MRI-guided invasive procedures, improved imaging in high-susceptibility regions including the lung, and advantages for efficient image acquisitions. In light of developments in MRI engineering and available computational power, and as well as the drive to reduce MRI costs, there is significant value in revisiting lower field MRI in the context of modern clinical imaging. This talk will describe the experience of the NHLBI imaging patients on a ramped down 0.55T system for 2 years.

    Speaker Bio

    Dr. Adrienne Campbell-Washburn is the Director of the MRI Technology Program at the National Heart, Lung, and Blood Institute (National Institutes of Health). Her research focuses on the development of MRI technology for cardiac imaging, lung imaging, and MRI-guided interventions. She works on developing advanced MRI acquisitions that leverage non-Cartesian sampling and reconstruction methods using state-of-the-art computational resources in the clinical environment. Her research aims to improve SNR-efficiency, imaging speed, interventional procedural guidance including device safety and visibility, motion robustness, quantification, and clinical integration.

  • January 28, 2020 at noon

    Treating oxidative stress in aging and disease: Moving from art to science

    Bruce Berkowitz, PhD

    Wayne State University School of Medicine


    Imaging biomarkers that bridge neuronal abnormalities in vivo and behavior, and animal models and human patients, are urgently needed to quicken discovery and application of novel disease-modifying therapy, but are not yet available. I will be discussing novel MRI and OCT approaches for measuring sustained and excessive production of free radicals (i.e., oxidative stress) in neuronal laminae without a contrast agent in untreatable neurodegenerative disease. These studies set the stage for translating and managing anti-oxidant treatment in patients for the first time.

  • January 21, 2020 at noon

    Modernizing Medical Imaging with Large-scale Computational Algorithms

    Frank Ong, PhD

    Postdoctoral Fellow
    Stanford University


    Existing clinical infrastructures severely under-utilize modern computation resources, leading to costly errors, slow workflows and limited research opportunities. However, trends in cloud computing and machine learning are rapidly changing this landscape. Tech companies, such as Amazon, Google and Microsoft, are now racing to integrate high performance computing into clinical settings. Medical imaging stands to gain tremendously from advances in computing power, which will enable many previously unthinkable applications.

    In this talk, I will focus on three directions on leveraging these emerging computing resources to improve medical imaging: 1) reconstructions of high dimensional volumetric dynamic MRI on the order of 100GBs; 2) continuous learning and image quality improvement from undersampled datasets; 3) optimizing end-to-end systems across clinical workflow.

  • January 14, 2020 at noon

    Temporal Regularization with Machine Learning for Dynamic Image Reconstruction

    Zhengnan Huang, MSc

    PhD Student, Biomedical Imaging and Technology
    Sackler Institute of Graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Dynamic MR image quality is limited by the temporal and spatial resolution trade-off. Adopting machine learning in the reconstruction network provide alternative method to reconstruct the image of better quality. This presentation will focus on our work using Recurrent Neural Networks(RNN) as regularizer in our dynamic MR reconstruction network. The regularizer is designed to take time series of kspace with flexible length and use it to reconstruct image series. I will show how we design simulation to get ground truth images for model training. Then I will show the output of the trained network on simulated breast perfusion MR data with comparison to other reconstruction methods.

    Spaker Bio

    Zhengnan Huang joined Florian Knoll’s lab in 2018. He has educational background in bioinformatics. His research interest is MR image reconstruction and machine learning application.

To the top ↑

2019 Lectures

  • December 18, 2019, at noon

    Three Deep Learning Techniques for 3D diffusion MRI Image Enhancement

    Stefano B. Blumberg

    PhD Student
    University College London


    In this talk, we discuss three deep learning techniques to improve the image quality of 3D diffusion MRI Images. We first introduce a novel low-memory method, which allows us to control the GPU memory usage during training therefore allowing us to handle the processing of 3-dimensional, high-resolution, multi-channeled medical images. Secondly we present the first multi-task learning approach in data harmonization, where we integrate information from multiple acquisitions to improve the predictive performance and learning efficiency of the training procedure. Thirdly we present an extension of the transposed convolution, where we learn both the offsets of target locations and a blur to interpolate the fractional positions. All three techniques can be applied in other image-related paradigms.

  • December 17, 2019, at noon

    PET Imaging of Immune Function in Psychiatric Disorders

    Ansel Hillmer, PhD

    Assistant Professor
    Departments of Radiology & Biomedical Imaging, Psychiatry, and Biomedical Engineering
    Yale University


    Dysregulated immune signaling contributes to many neuropsychiatric conditions. Brain PET imaging can measure neuroimmune factors that inform treatment development for such conditions. This talk will focus on PET imaging of the 18-kDa translocator protein (TSPO). Preclinical work informing the interpretation of TSPO signal, including imaging dynamic responses to endotoxin, an acute immune stimulus, will first be presented. Next, human data imaging TSPO in tobacco smoking, alcohol use disorder, and Alzheimer’s disease will be presented to demonstrate diverse applications of these techniques. Whole body imaging of TSPO following acute alcohol administration as an immune stimulus will also be presented. Finally, work characterizing new radiotracers that complement TSPO measures in immune signaling will be presented. This work depicts ways in which PET imaging can be leveraged to study immune function in the context of neuropsychiatric disorders.

  • December 13, 2020, at noon

    Validation of rheo-markers in ex-vivo human cartilage for early OA detection using multiscale MRI

    Galina Pavlovskaya, PhD

    Associate Professor
    University of Nottingham


    A novel investigation of rheo-markers (proton T2* and sodium multiple quantum filtering) shows the potential for multi-nuclear MRI biomarkers in mechanically loaded joints with good evidence of a dynamic 23Na environment during compression which may be useful for early OA detection before symptoms occur.

  • December 10, 2019, at noon

    MRI for Monitoring Health of Total Hip Arthroplasty

    Matthew Koff, PhD

    Associate Scientist, Department of Radiology and Imaging
    Associate Professor of Biomedical Imaging in Orthapaedic Surgery
    Weill Cornell Medical College of Cornell University
    New York, NY


    A majority of primary total hip arthroplasty (THA) devices function well but implant failures occur. This presentation will cover our long standing efforts to utilize MRI in identifying patients needing premature implant revision due to adverse local tissue reactions (ALTRs). The utility of advanced multi-spectral imaging to reduce metallic susceptibility artifact and visualize synovitis, osteolysis, and tendon tears near arthroplasty will be displayed. I will also show results from our on-going studies using MRI to evaluate patients with different THA bearing materials to determine which factors are predictive of abnormal synovial reaction. Finally, data will be shown regarding the longitudinal prevalence of MRI detected ALTRs in a cohort of high functioning THA patients.

  • December 5, 2019, at noon

    Protective effects of Intranasally Administered Nanoantioxidants in the Olfactory System in Mouse Models of Alzheimer’s Disease

    Robia Pautler, PhD

    Associate Professor
    Departments of Molecular Physiology and Biophysics, Neuroscience and Radiology
    Baylor College of Medicine
    Houston, TX


    Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the neuropathological accumulation of amyloid beta (Ab) plaques and neurofibrillary tangles comprised of hyperphosphorylated tau. Tau is a microtubule-associated protein involved in microtubule stability and when tau is hyperphosphorylated, microtubules become destabilized which leads to impaired axonal transport. Axonal transport is an important cellular process that shuttles vesicles, neurotransmitters, and mitochondria from the soma to the synapse. Perturbations in axonal transport disrupt neuronal activity by reducing the transport of mitochondria, increasing reactive oxygen species (ROS) and diminishing the formation of active zones at the synapse. Axonal transport deficits are thought to occur early and continue to progress in AD. Thus, there is a significant need and strong scientific premise to identify the mechanisms by which axonal transport deficits occur and also can be improved in AD.

    Olfactory receptor neurons are the only part of the central nervous system (CNS) with direct access to the outside world. They lie at the beginning of a neural network which projects to the olfactory bulb followed by the piriform olfactory cortex (primary olfactory cortex), the entorhinal cortex (secondary olfactory cortex). The olfactory system is also the first system affected in AD patients and mouse models of AD before cognitive deficits develop. Indeed, using Manganese Enhanced MRI (MEMRI), we have shown that axonal transport deficits in the olfactory receptor neurons occur before the appearance of learning and memory deficits appear and are reversed when we reduce ROS levels by overexpressing superoxide dismutase 2 (SOD-2) in AD mice. Here, we describe our current efforts with reducing oxidative stress in the olfactory structures in mouse models of AD with intranasally applied nanoantioxidants.

  • December 5, 2019, at 10:00 a.m.

    Whole-brain fMRI of the behaving mouse

    Itamar Kahn, PhD

    Associate Professor
    Department of Neuroscience
    Ruth and Bruce Rappaport Faculty of Medicine
    Technion – Israel Institute of Technology


    Functional MRI is used extensively in human brain research, enabling characterization of distributed brain activity underlying complex perceptual and cognitive processes. However, it has been limited in utility in rodents. I will present the work we have done to establish awake mouse MRI, characterize the properties of the hemodynamic response function as different from humans and how these two aspect enabled us to conduct whole-brain fMRI of the behaving animal. I will expand on recent work using whole-brain functional imaging of head-fixed mice performing odor discrimination and conclude by showing additional behavioral modalities we develop with the goal to establish this approach as a platform to be used extensively in the field.

  • November 25, 2019, at noon

    Simultaneous Quantification of Flow Velocities and Relaxation Constants Through MRF

    Sebastian Flassbeck, PhD

    Postdoctoral Fellow
    Division of Medical Physics in Radiology
    German Cancer Research Center


    A novel imaging technique is presented, capable of simultaneously quantifying time-resolved blood flow velocities and the relaxation constants of static tissue. This is accomplished through the use of a Magnetic Resonance Fingerprinting (MRF) based approach. The developed technique, termed “Flow-MRF”, allows accurate mapping of velocities and relaxation constants in measurement times up to 4-fold shorter than conventional MRI-based velocimetry techniques.

  • November 21, 2019, at noon

    glucoCEST MRI: en route to Translation

    Xiang Xu, PhD

    Assistant Professor
    Department of Radiology
    Johns Hopkins University


    Chemical exchange saturation transfer (CEST) is a relatively new type of MRI contrast that indirectly detects low concentration labile protons through water signal with enhanced sensitivity. In this presentation, I will explain the principles of CEST imaging and its applications. I will show results from using CEST to image D-glucose (glucoCEST) in vivo, first on brain tumor mouse model at ultra-high magnetic field, then on human brain tumor patient on 7T system. Our recent effort of translating the technique to clinical field strength and the promise and challenges of glucoCEST at clinical field strength will be also be discussed.

  • November 12, 2019, at noon

    Multimodal biomarker studies to understand Alzheimer´s disease: biochemical & imaging biomarkers in sporadic AD and AD in Down syndrome

    Juan Fortea, PhD

    Sant Pau Memory Unit
    Barcelona, Spain


    CSF, PET and MRI multimodal studies enable the early diagnosis of Alzheimer’s Disease. We have proposed a model in which interactions between biomarkers in preclinical AD result in a two-phase phenomenon: an initial phase of cortical thickening due to amyloid-related inflammation, followed by a cortical atrophy phase which occurs once tau biomarkers become abnormal. These results have implications in the selection of patients for clinical trials and the use of MRI as a surrogate marker of efficacy. We will also present data showing the potential of studying the cortical microstructure with DTI to assess these early changes and in the diagnosis of other neurodegenerative diseases.

  • October 24, 2019, at noon

    Bridge the functional and hemodynamic brain mapping with the multi-modal fMRI

    Xin Yu, PhD

    Research Group Leader
    Department High-field Magnetic Resonance
    Max Planck Institute
    Tuebingen, Germany


    In this talk, I will introduce the combination of the advanced fMRI method with the emerging neuro-techniques to decipher the neuro-glial-vascular (NGV) coupling basis of brain state dynamics. First, we will see through the large voxel acquired from conventional fMRI to decipher the contribution from distinct vascular components to the fMRI signal. A newly developed single-vessel fMRI method allows identifying the activity-evoked hemodynamic signal propagation through the cerebrovasculature in the brain with either sensory inputs or optogenetic activation. Second, we will combine the fMRI with the optical fiber-mediated calcium recordings to decipher the cell-type-specific contribution to the fMRI signal from neurons and astrocytes. Meanwhile, we will also show how extracellular glutamate can be recorded simultaneously to mediate NGV interaction. Finally, we are going to present how the global fMRI signal fluctuation can be linked to the brain state changes. We merge the pupillometry with the multi-modal fMRI to examine the detailed arousal index by pupil dynamics and fMRI fluctuation. In summary, we hope to provide a novel perspective to under brain function with multi-modal fMRI across different scales.

  • October 15, 2019, at noon

    Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

    Aviv Mezer, PhD

    Assistant Professor
    Human Brain Biophysics Lab
    Edmond and Lily Safra Center for Brain Sciences (ELSC)
    The Hebrew University of Jerusalem


    Quantitative MRI (qMRI) parameters such as T1 provide physical parametric measurements crucial for clinical and scientific studies. However, an important challenge in applying qMRI measurements is their biological specificity, as they change in response to both molecular composition and water content. I will discuss an approach that disentangles these two important biological quantities and allows for decoding of the molecular composition from the qMRI signal. I will demonstrate that this approach can reveal the molecular composition of lipid samples. Furthermore, we identify region-specific molecular signatures in the human brain that have been validated against histological measurements. Last, we exploit our method to reveal region-specific molecular changes in the aging human brain. I suggest that the ability to disentangle molecular signatures from water-related changes opens the door to a quantitative and specific characterization of the human brain.

  • October 8, 2019, at noon

    Hierarchical Structure of the Human Brain’s Macro-scale Networks

    Ray Razlighi, PhD

    Assistant Professor
    Department of Neurology, Columbia University
    Department of Biomedical Engineering, Columbia University
    Taub Institute for Research on Alzheimer’s Disease and the Ageing Brain


    This talk will start with a brief introduction of what is negative BOLD response in fMRI data and what are its characteristics. It continues by categorizing different types of negative BOLD signal according to their properties and outlines the optimal techniques used to extract negative BOLD response.

    The applications of negative BOLD response are unlimited, however, three on-going research projects in our lab which extensively rely on negative BOLD response will be presented. First project uses negative BOLD response to demonstrate how spontaneous activity and task-evoked activity in the brain give rise to two spatially overlapping but temporally dissociable signals which both are manifested in fMRI data. Using these findings, the second project attempts to use negative BOLD response to demonstrate evidences for the hierarchical structure in the human brain functional networks. This is done by demonstrating that task-evoked negative BOLD response in the Default mode network is modulated by switching attention whereas the functional connectivity between the same network of regions remain intact. Finally, we introduce negative BOLD response as a new brain biomarker that could potentially differentiate between normal and pathological ageing brains.

  • October 4, 2019, at noon

    A biological framework for the evaluation of per-bundle water diffusion metrics within a region of fiber crossing

    Ricardo Coronado Leija, PhD

    Postdoctoral Fellow
    Universidad Nacional Autónoma de México
    Instituto de Neurobiología Laboratorio de Conectividad Cerebral


    Several multiple fiber methods have been proposed that seem to overcome the limitations of the diffusion tensor and methodologies aimed to provide information from the diffusion signal, but that are mostly suited for single fiber population regions. Although the majority of these multiple fiber methods where created with the primary purpose of improving tractography results, some of them are able to provide per-bundle dMRI derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation.

    To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures.

    We correlated and compared histology derived information to per-bundle descriptors obtained from three multiple fiber methodologies for dMRI analysis: constrained spherical deconvolution (CSD) and two multi-tensor (MT) representations. We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density (from CSD) and fractional anisotropy (derived from the MT methods). The multiple fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions.

    Our proposed framework is useful to validate other current and future dMRI multiple fiber methods; it also can be extended for the analysis of other pathological conditions, such as inflammation and demyelination, in order to evaluate the capabilities of these dMRI methods to differentiate between.

  • October 1, 2019, at noon

    Bridging Brain Structure and Function by Correlating Structural Connectivity and Cortico-Cortical Transmission

    Patryk Filipiak, PhD

    Postdoctoral Researcher
    Athena Lab
    Inria Sophia Antipolis, France


    Elucidating the relationship between the structure and function of the brain is one of the main open questions in neuroscience. The capabilities of diffusion MRI-based (dMRI) techniques to quantify connectivity strength between brain areas, referred to as structural connectivity, in combination with modalities to quantify brain function such as electrocorticography (ECoG) have enabled advances in this field.

    The aim of the project that I will talk about is to establish a relationship between dMRI-based structural connectivity and effective connectivity maps based on the propagation of Cortico-Cortical Evoked Potentials (CCEPs). To this end, we applied direct electrical stimulation of the cortex during awake surgery of brain tumor patients and recorded the induced electrophysiological activity with subdural ECoG electrodes.

    I will briefly summarize our study of seven patients. For each of them, we correlated dMRI-based structural connectivity measures, including streamline counts and lengths, with delays and amplitudes of CCEPs. In addition, we used the structural information to predict the CCEP propagation with a linear regression model.

  • September 19, 2019, at noon

    Advanced arterial spin labeling in cerebrovascular imaging

    Lirong Yan, PhD

    Assistant Professor of Neurology
    USC Stevens Neuroimaging and Informatics Institute
    Keck School of Medicine
    University of Southern California


    Arterial spin labeling (ASL) is a non-invasive MRI technique for cerebral blood flow (CBF) measurement by using magnetically labeled blood spins as endogenous tracers. The recent development of ASL has promoted it as a useful imaging tool for tissue perfusion assessment in cerebrovascular disorders. For perfusion imaging, after spin tagging, images are generally acquired at a relatively long post-labeling delay time (~1.8s) when the labeled blood from labeling plane reaches capillaries/tissue. Additional physiological information can be derived during the passage of labeled blood through the cerebral arterial trees into capillaries and tissue, such as dynamic MR angiography, vascular territorial mapping, cerebral blood volume (CBV) and vascular compliance et al, all of which also provide useful information in the diagnosis and treatment of cerebrovascular disease. In this talk, I will introduce my work about these recent advances in ASL beyond CBF measurement.

  • September 5, 2019, at noon

    Inge at a glance

    Inge Brinkmann, PhD

    Siemens Healthcare GmbH
    Diagnostic Imaging


    Inge Brinkmann will provide an overview of her work at Siemens Healthineers.

  • September 3, 2019, at noon

    Loss Function Modeling for Deep Neural Networks Applied to Pixel-level Tasks

    Fidel Guerrero Pena

    PhD Candidate
    Computer Science
    Federal University of Pernambuco
    Recife, Brazil


    In recent years, deep convolutional neural networks have overcome several challenges in the field of computer vision and image processing. In particular, pixel-level tasks such as image segmentation, restoration, generation, enhancement, and inpainting, showed significant improvements thanks to advances in the technique. In general, the supervised training of a neural network entails solving a high dimensional non-convex optimization problem whose objective is to transform the vectors of the input domain to a prescribed output. However, due to the high dimensionality of the parameter space and the presence of saddle points and large flat regions on the error surface, the process of training a neural network is extraordinarily challenging. We propose modeling new loss functions to facilitate training while improving the generalization of models for pixel-level regression and classification tasks. Our newly introduced loss functions modify the optimization landscape to achieve better results in regions which are notoriously more prone to failure. They increase the overall optimization performance and accelerate convergence. We applied our formulations to instance segmentation of cells with full and weak supervision and tested them on challenging biological images with isolated and cluttered cells. We also propose a new pixel-level regression loss function applied to the multi-focus image fusion problem resulting in the joint learning of activity level measurement and fusion rule. New pre-processing and post-processing techniques to help improve the solutions are also introduced. Our methods have shown significant improvements in the segmentation and image restoration tasks as reported by a diverse set of metrics and visual inspections.

  • August 29, 2019, at noon

    Deep-Learning-Assisted Disease Diagnosis and Detection

    Mijung Kim

    PhD Candidate
    Computer Science Engineering
    Ghent University


    Recent achievement of deep learning algorithms using convolutional neural networks (CNNs) yields high performance of image classification and segmentation. The algorithms have been applied to assist doctor’s medical decision more efficiently and effectively. In this talk, I will introduce deep learning applications to rotator cuff tears, glaucoma, and intraocular pressure relations with daily diet pattern.

  • August 27, 2019, at noon

    A Molecular Imaging Approach to Study, Diagnose and Treat Osteoarthritis

    Amparo Ruiz, PhD

    Senior Research Scientist
    Co-Director of OLE! (Osteoarthritis Lab for Experimental Imaging)
    Department of Radiology
    NYU Langone Health


    Osteoarthritis (OA) is the most common form of arthritis, affecting millions of people in the US for which only palliative treatments are available until joint replacement surgery. The elusiveness of effective OA treatments is the consequence of OA being a complex disease. OA is a multifactorial disease with inflammatory, metabolic, and mechanical causes involving all tissues of the joint. Thus, we still lack understanding on OA pathogenesis, in part due to the lack of diagnostic biomarkers that can detect early pathological changes in the joint and monitor therapy. A major barrier in OA research is to see and understand the interplay between OA factors to both be able to phenotype OA and provide patient-specific treatments. At OLE! (Osteoarthritis Lab for Experimental !maging), we aim to solve this technological problem by developing advanced imaging technology that can monitor in vivo of the influence of OA factors and treat them. We have established an innovative research program for in vivo molecular imaging of the degenerative joint. We are developing imaging probes with theragnostic potential that combine the specificity of biochemical assays with anatomical and tissue-specific assessment of early degenerative changes.

  • August 22, 2019, at noon

    Non-Cartesian Techniques for Quantitative Parameter Mapping

    Mahesh B. Keerthivasan

    Postdoctoral Research Scientist
    Siemens Healthineers USA


    Conventional T1- and T2- weighted pulse sequences are routinely used in the clinic for the diagnosis of a variety of pathologies. Quantatative estimation of tissue relaxation times can be used to further improve the quality of diagnosis in applications including cardiac, abdominal, and musculoskeletal imaging. In this talk, I will introduce a radial Turbo Spin Echo (RADTSE) pulse sequence for simultaneous T2w imaging and T2 mapping. Specifically, I will present a RADTSE pulse sequence with very long echo train lengths and variable refocusing flip angles for improved slice coverage in abdominal breath-held imaging. I will also discuss a simultaneous multi-slice excitation technique to improve the slice and SNR efficiency of double inversion RADTSE for cardiac imaging. Finally, I will give an overview of my ongoing research on quantitative T1 mapping and the use of artificial intelligence for analysis of deep brain structures.

  • August 20, 2019, at noon

    Magnetic Resonance Spectroscopy: From Multiparametric to Functional

    Assaf Tal, PhD

    Principal Investigator
    Department of Chemical Physics
    Weizmann Institute of Science


    Magnetic Resonance Spectroscopy (MRS) is used to non-invasively monitor the in-vivo biochemistry of tissue, by quantifying the concentrations of several prominent metabolites, including glutamate, choline, GABA and creatine, among others. Conventional MRS produces static estimates of concentrations. In this talk, I will present two recent advances in MRS methodology which provide a more dynamic information. First, I will discuss our work on multiparametric MRS, which simultaneously quantifies metabolite concentrations and relaxation times (T1, T2). Both T1 and T2 provide information about the molecular microenvironment of the metabolites via their microscopic dynamics. In the second part of the talk, I will discuss our work on functional MRS, which examines the temporal changes to several prominent metabolites in response to external stimuli, and discuss some of our interpretations to the changes measured in this unsolved, fascinating puzzle.

  • August 13, 2019, at noon

    Innovation from Image Formation to Post-processing

    Fei Gao, PhD

    Staff Scientist
    Research Department at Siemens Molecular Imaging
    Knoxville, TN


    In this talk, I will introduce my recent research activities from image formation to post processing using examples of whole body scatter estimation and image reconstruction for Biograph mMR and a deep learning powered lung analysis post processing application. For the Biograph mMR, we designed a new method to process step and shoot sinogram to simulate a whole body sinogram and reconstruct the whole body image directly, which increases the quantitative accuracy of scatter estimation and improves performance of image reconstruction. For post-processing, I will showcase several AI predevelopment activities, focusing on the lung ventilation / perfusion application. Here, deep learning-based lung lobe segmentation has been developed to enable a potentially fully automated workflow for lung analysis. This prototype is available on the Siemens Frontier platform, offering a seamless integration to syngo.

  • August 6, 2019, at 4:00 p.m.


    Tullie Murrel

    Applied Research Scientist
    Facebook AI Research (FAIR)


    fastMRI is a collaborative research project between Facebook AI Research (FAIR) and NYU Langone Health. The aim is to investigate the use of AI to improve acceleration and robustness of MRI scans. In this talk, Tullie, a Research Engineer at FAIR, will give an overview of the work done on knee image reconstruction and reinforcement learning based active sampling. He will cover the plans going forward to investigate brain image reconstruction, motion robust reconstructions for Dynamic MRI and extensions to the active sampling work.

  • August 6, 2019, at noon

    Analyzing and enhancing cryo EM maps using local directional resolution

    Jose María Carazo

    Head, Bio-Computing Unit (BCU)
    National Center of Biotechnology
    Madrid, Spain


    Expecting to fully engage equally deep Physicists and Biologists, I will introduce the notion of “how good a macromolecular CyoEM map is”, addressing this question in a totally new way in the field, by providing a “resolution tensor” per CryoEM voxel map (instead of just a number, the so-called “local resolution”). The mathematical beauty of this tensor representation will immediately open a new university of opportunities for experimentalists in CryoEM (clearly impacting Pharma), with the capability to assess the quality of the map from the map itself (without the images), the alignement errors, the presence of problematic directions….. and much more.

  • August 1, 2019, at noon

    Efficient Motion-Corrected Multiple Contrast MRI with MPnRAGE

    Professor of Medical Physics and Psychiatry
    Co-Director of Waisman Brain Imaging Lab
    University of Wisconsin-Madison


    T1-weighted structural imaging with MP-RAGE is a cornerstone of brain imaging studies for both clinical and research applications. However it is sensitive to head motion, RF inhomogeneities, and provides only a single image contrast. Recently, we developed MPnRAGE which combines inversion magnetization preparation with a 3D radial rapid gradient echo readout. This sampling enables the simultaneous acquisition of n inversion recovery contrasts, which may be used to generate one or more application specific contrast images, and generate high resolution, whole-brain T1 relaxometry images. The 3D radial sampling is also highly amenable to self-navigated motion correction during the reconstruction, which provides robust and reliable high quality T1-weighted and quantitative T1 images of the brain. This technique is highly promising for brain imaging studies of children, aging and brain pathology.

  • July 23, 2019, at noon

    New directions in MRI through tailored acquisitions

    Kawin Setsompop, PhD

    Associate Professor
    Harvard Medical School


    A synergistic approach in developing MRI acquisition through utilizing the interplay between hardware design, software algorithm development, and MR physics has dramatically increased MRI’s spatiotemporal resolution capability. IN this talk, I will cover some of these tailored acquisition strategies which are being pioneered by my group, focusing particularly on applications in rapid imaging, diffusion, & fMRI, and quantitative and multidimensional/time-resolved imaging of the brain. The overarching theme is in radically improving the speed, sensitivity, and specificity of in vivo brain imaging, with the goal in providing more detailed information about the brain both in health and disease.

  • July 16, 2019, at noon

    Imaging Metabolic Processes and Identifying Biomarkers of Diseases at 7 Tesla

    Jimin Ren, PhD

    Associate Professor, Advanced Imaging Research Center
    Associate Professor, Department of Radiology
    University of Texas Southwestern Medical Center


    Dr. Ren will discuss a series of studies using dynamic and kinetic MRS, that have identified cellular energetic activities in multiple pathways. He will also demonstrate how 7T 31P MRS can serve as a powerful tool to capture aberrant brain events in remote skeletal muscle.

  • July 15, 2019, at noon

    Chasing the trinity: Characterization of acute migraine

    Nastaren Abad, MS

    PhD Candidate
    Florida State University


    Migraine is a disabling, multifactorial recurrent neurological disorder. Affecting approximately 38 million people in the United States alone, migraine is recognized by the World Health Organization as the 7th most disabling condition, due to the sufferer’s inability to perform everyday activities. The characterization, classification and diagnosis of migraine is complex due to the tremendous cohort of variable clinical triggers and symptoms reported. Collectively, the symptoms accompanying migraine implicate multiple neural networks and processes functioning abnormally. A mechanistic search for a common denominator based on the symptoms in migraine potentially involves the recruitment of the thalamic region (fatigue, depression, irritability, food cravings), brainstem (muscle tenderness, neck stiffness), cortex (sensitivity to photo and phono) and limbic response (depression anhedonia).

    The prevailing consensus in the migraine community appears to indicate a combination of neuronal and vascular involvement with the trigeminal vascular system (TGVS) complicit in the progression of migraine. Broadly, various triggers initiate migraine to differing degrees and treatment methodologies target a variety of pathways with varied results; the fundamental mechanism driving change is unclear. In the absence of an identifiable locus for anatomical, biochemical or pathological change in common clinical migraine, a fundamental question remains unanswered: What endogenous media and pathways link the stimulus to perception of migraine and potentially pain?

    The goal of this talk is to highlight progress made in the characterization of acute triggered migraine. To elucidate this neurovascular coupled system, two fundamental mechanisms complicit in neuronal disorders are explored, namely ionic fluxes using sodium MRI and metabolic changes by utilizing proton spectroscopy as well as ongoing efforts to characterize cerebral perfusion—with and without pharmaceutical prophylaxis.

  • June 26, 2019, at noon

    Structure-Aware Shape Analysis in Medical Imaging

    Elena Sizikova

    PhD Student and NSF Graduate Fellow
    3D Vision Lab
    Princeton University


    Automatic delineation and measurement of main organs is one of the critical steps for assessment of disease, planning and postoperative or treatment follow-up. Internal human anatomy is composed of complex shapes that exhibit a large degree of variation, which is challenging to capture using existing modeling tools. We observe that complex shapes can be learned by neural networks from large amounts of examples and summarized using a coarsely defined structure, which is consistent and robust across variety of observations. Further, shape structure can be used in the synthesis process to improve the quality of generated shapes. We study medical applications of 3D organ reconstruction from topograms and synthetic X-ray prediction and propose several ways of incorporating structure into the synthesis process, and. We also show compelling quantitative results on 3D liver shape reconstruction and volume estimation on 2129 CT scans.

  • June 11, 2019, at noon

    The essential role of multidisciplinary engineering in Ultra High-Field MRI

    Simone A. Winkler, PhD

    Weill Cornell Medicine
    MRI Research Institute
    Ultra High-Field MRI


    Magnetic Resonance Imaging (MRI) has emerged as one of the most powerful and informative diagnostic tools in modern medicine. While most clinical MR studies use magnetic field strengths of 1.5T or 3T, leading research is pushing these magnetic field strengths to 7T and beyond. These new ultra high‐field (UHF) technologies promise images with higher spatial resolution, higher sensitivity to subtle change, and novel contrasts, which will in turn improve our basic understanding of anatomy and physiology in both healthy tissue and disease. However, there are substantial hurdles to surmount before we will reap the promised benefits of UHF MRI in clinical applications. This talk will introduce some of the major challenges faced in UHF MRI and will summarize a number of concepts in engineering and multiphysics that are being researched to overcome these issues.

  • May 22, 2019, at noon

    Monitoring progression in kidney disease using pH and perfusion MRI

    Michael T. McMahon, PhD

    Associate Professor
    F.M. Kirby Research Center for Functional Brain Imaging
    Kennedy Krieger Institute
    Johns Hopkins University


    Chronic Kidney Disease (CKD) is a cardinal feature of methylmalonic acidemia (MMA), a prototypic organic acidemia. Impaired growth, low activity, and protein restriction affect muscle mass and lower serum creatinine concentrations, which can delay the diagnosis and management of renal disease in this patient population. We have designed a general alternative strategy for monitoring renal function based on administration of a pH sensitive MRI contrast agents to acquire functional information. We have tested our methods in a mouse model of MMA, and detected robust differences in the perfusion fraction and pH maps we produce between groups with severe, mild, and no renal disease. Our results demonstrate that MRI contrast agents can be used for early detection and monitoring of CKD, particularly in disorders that alter renal pH and perfusion such as MMA.

  • May 21, 2019, at noon

    Window to Understanding Multisensory Large-scale Brain Networks through Optogenetic Functional MRI (fMRI)

    Alex T. L. Leong, PhD

    Research Assistant Professor
    Department of Electrical and Electronic Engineering
    The University of Hong Kong


    One grand challenge for the 21st century is to achieve an integrated understanding of brain circuits and networks, particularly the spatiotemporal patterns of neural activity that give rise to functions and behavior. Brains form highly complex circuits where circuit elements communicate using electrical and/or chemical signals. Such communications are typically facilitated through long-range projections that interconnect numerous regions, giving rise to a network-like property in the brain. Despite their importance, the functions of long-range projections remain poorly understood. Here, I will show you our recent developments in deploying multimodal techniques in-vivo on rodents to interrogate multisensory brain networks; leveraging on the strengths of optogenetics to enable cell-type specific neuromodulation, functional MRI (fMRI) to visualize brain-wide neural activity, and electrophysiology to explore the neural mechanism(s) that underlie our observations. I will present key findings from our work in the multisensory thalamo-cortical, cortico-cortical, and cortical-subcortical circuits, including the unique dynamic spatiotemporal response properties of multisensory pathways as well as their functional relevance. From this talk, I aim to show you how utilization of multimodal brain imaging techniques can be vital in our quest to achieving an integrated and systemic understanding of large-scale brain-wide multisensory interactions.

  • May 9, 2019, at noon

    Extreme MRI: Reconstructing Hundred-Gigabyte Volumetric Dynamic MRI from Non-Gated Acquisitions

    Frank Ong, PhD

    Postdoctoral Researcher
    Stanford University


    In this talk, I will present techniques to reconstruct 3D dynamic MRI of ~100 GBs from non-gated acquisitions. The problem considered is vastly undetermined and demanding of computation and memory. I will introduce a multi scale low rank matrix model to compactly represent dynamic image sequence. This enables compressed storage, which in combination with a stochastic optimization approach, renders the reconstruction of 100s of GBs of images feasible. The proposed method is applied to dynamic contrast enhanced MRI and free breathing lung MRI, with reconstruction resolution of near millimeter spatially, and sub-second temporally. The attached animated gif shows a 3D rendered result from this talk. (Joint work with Xucheng Zhu, Joseph Cheng, Peder Larson, Shreyas Vasanawala, and Michael Lustig)

  • May 3, 2019, at noon

    Imaging Pain: Pinpointing the site of pain generation using clinical molecular imaging and PET/MRI

    Sandip Biswal, MD

    Associate Professor
    Department of Radiology
    Stanford University School of Medicine


    Pain is now the #1 clinical problem in the world and, yet, our current imaging methods to correctly identify pain generators remain woefully innacurate. The fact that meniscal tears, herniated discs, arthritis and rotator cuff tears are seen in asymptomatic individuals supports the disturbing fact that standard-of-care imaging techniques are extremely poor at pinpointing the exact site of pain generation. This dearth of unreliable diagnostic tools necessarily facilitates significant misdiagnosis, mismanagement, rampant use of opioids and unhelpful surgeries. Thankfully, relatively recent developments in clinical molecular imaging (MI) are affording the opportunity to pinpoint the exact site(s) of pain generation due to advances in biomarker discovery, imaging technology and radiotracer design. Our group has developed a highly specific 18F-labeled positron emission tomography (PET) radiotracer for imaging the sigma-1 receptor (S1R), a master regulator of ion channel activity and molecular biomarker of pain generation. Additionally, we have repurposed 18F-fluordeoxyglucose (FDG) as a marker of inflammation by virtue of its proclivity for metabolically active processes. Here, we will describe our experience using these radiotracers in our ongoing PET/MRI clinical trials of patients with chronic pain. Importantly, we will illustrate how this new imaging method is enabling more accurate identification and localization of pain generators and is starting to positively impact the way we treat pain.

  • May 3, 2019, at 12:30 p.m.

    Imaging the brain on fire—PET tracer design and development for visualizing neuroinflammation

    Michelle James, PhD

    Stanford University School of Medicine


    Neuroinflammation is a key pathological feature of many central nervous system (CNS) diseases. Although extensive work in preclinical rodent models demonstrate a significant role for both the innate and adaptive immune response in the initiation and progression of neurological diseases, our understanding of these responses and their contribution to human disease remains very limited. ​Additionally, ​both beneficial and toxic inflammatory processes are associated with progression and remission of neurological disease, and the spatiotemporal course of these complex responses remain a mystery especially in the clinical setting. Molecular imaging using positron emission tomography (PET) has enormous potential as a translatable technique to enhance our understanding of neuroinflammation in CNS diseases. Our experience with developing new PET radioligands for visualizing the neuroinflammatory component of Alzheimer’s disease, multiple sclerosis, and stroke will be described. I will provide examples regarding our work on d​esigning radioligands for the translator protein 18 kDa (TSPO), triggering receptor expressed on myeloid cells 1 (TREM1), and two B lymphocyte surface antigens. Specifically, the in vivo role, spatiotemporal dynamics, ​peripheral contribution and different functional phenotypes of innate and adaptive immune cells throughout the progression of CNS diseases will be shown. Moreover, I will describe how we are starting to apply these tools to track disease progression, guide therapeutic selection for individual patients, and serve as surrogate endpoints in clinical trials.

  • April 30, 2019, at noon

    Combining MRI with X-rays to Assess Tissue Microstructure

    Marios Georgiadis, PhD

    Postdoctoral Fellow
    NYU Grossman School of Medicine


    Although both MRI and CT resolutions are limited, different MRI and X-ray modalities offer possibilities for tissue microstructure analyses. Diffusion MRI is sensitive to proton displacement in the micrometer scale, whereas X-ray photons scatter off the sample’s micro- and nano-structure.

    Recently, we developed techniques based on X-ray scattering that allow tomographic investigations of the sample’s fiber orientations. In brain, these techniques also allow quantifying myelin content, due to myelin’s repetitive structure.

    In this talk I will give an overview of my work in CBI in the past two years; I will present applications of these techniques to mouse and human CNS, to derive fiber orientations and myelin content in healthy, diseased and treated tissue, and comparison to diffusion MRI metrics.

  • April 25, 2019, at noon

    Breast DWI: ADC and beyond

    Mami Iima, MD, PhD

    Department of Radiology
    Institute for Advancement of Clinical and Translational Science
    Kyoto University Hospital, Kyoto, Japan


    Diffusion MR imaging has become an important clinical imaging modality in breast imaging, for the detection of malignant lesions and metastases, as well as for therapy monitoring. Some studies have shown that pretreatment ADC has might be a useful biomarker to predict response to breast cancer therapy. However, non-Gaussian diffusion might potentially extract more microstructural information than the ADC, as with a high degree diffusion weighting (high b values) one increases the effects of obstacles to free diffusion present in tissues, notably cell membranes. Indeed, the “kurtosis” which reflects diffusion non-gaussianity is high in malignant lesions compared to benign lesions. Still, a particularly challenging problem for breast diffusion MRI is the detection of the non-mass enhancing lesions seen on contrast-enhanced MRI, such as with DCIS. High-resolution images using readout- segmented EPI might overcome the low sensitivity of such lesions. On the other hand, tissue perfusion which is also available from diffusion MRI images (IVIM effect) gives information on the blood fraction which appears correlated with vessel density. The IVIM fraction is usually high in malignant lesions, but there seems to be a large overlap with benign lesions. Combination of non-Gaussian diffusion and IVIM parameters appears to boost diagnosis accuracy. Still, the results have been sometimes inconsistent in the literature partly due to differences in study design (choice of b values and acquisition methods, data analysis approaches, differences in patient population), and the standardization of acquisition protocols and processing methods used for quantitative DWI analysis is a very important step for for diffusion MR imaging to become a clinically recognized biomarker.

    The investigations on the relationship between the IVIM/diffusion parameters and the underlying tissue structure at microscopic level, as well as changes induced by therapy, must be pursued using animal models, MRI of specimens at ultra-high resolution and validation with histology. Reliability and reproducibility of diffusion MRI results must also be assessed to facilitate monitoring disease progression or response to therapy in individual patients.

  • April 25, 2019, at 10:30 a.m.

    From molecules to mind: MRI’s potential for future’s medicine

    Denis Le Bihan, MD, PhD

    NeuroSpin, CEA-Saclay Center, Gif-sur-Yvette, France
    NIPS, Okazaki, Japan
    Human Brain Research Center, Kyoto University, Kyoto, Japan


    The understanding of the human brain is one of the main scientific challenges of the 21st century. Unraveling the biological mechanisms of our mental life should help us understanding neurological or psychiatric diseases to allow early diagnosis and treatment of patients, with obvious economical counterparts. In this quest of the human brain neuroimaging and especially MRI has become an inescapable pathway because it allows getting maps of brain structure and function in situ, non-invasively, in patients or normal volunteers of any age. MRI allows brain anatomy of individuals to be visualized in 3 dimensions with great details, as well as networks of brain regions activated by high order cognitive functions, together with stunning images of the connections between those areas. Still, images remain at a macroscopic scale (millions of brain cells), while invasive techniques in animals and tissues explore very small ensembles of neurons. This large gap must be bridged to understand how the brain works, as interaction and synergy exist between all brain levels. One approach is to rely on diffusion MRI, a concept which has been develop from the mid-1980s based on Einstein’s framework to probe tissue structure at a microscopic scale while images remain at millimeter scale through parametrization or modeling, providing unique information on the functional architecture of tissues. Since then, diffusion MRI has become a pillar of modern clinical imaging. Diffusion MRI has mainly been used to investigate neurological disorders, but is now also rapidly expanding in oncology, to detect, characterize or even stage malignant lesions, especially for breast or prostate cancer. In the brain diffusion MRI even allows to reveal dynamic changes occurring in tissue microstructure intimately linked to the neuronal activation mechanisms. On the other hand, outstanding instruments operating to field of 11.7 teslas or above are now emerging to boost the spatial and temporal resolution to not only allow us to “better” see inside our brain, confirming or invalidating our current assumptions on how it works, but also to generate new assumptions and elaborate a kind of “Gauge Theory” to help us decode the functioning of our brain.

  • April 16, 2019, at noon

    DeepPET: A deep encoder–decoder network for directly solving the PET image reconstruction inverse problem

    Ida Haggstrom, PhD

    Postdoctoral Research Fellow
    Memorial Sloan Kettering Cancer Center
    The Thomas Fuchs Lab


    To overcome the lack of automation and long computational times for advanced PET image reconstruction methods, we present a novel encoder-decoder architecture that quickly reconstructs high quality images directly from PET sinogram data. DeepPET is trained and evaluated on realistic, simulated data, and resulting images have higher quality than conventional techniques, and takes a fraction of the time to generate.

  • April 9, 2019, at noon

    Sodium (23Na) MRI in breast at 7T

    Carlotta Ianniello, MS

    PhD Candidate
    Biomedical imaging program
    Sackler Institute of graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Sodium (23Na) MRI has shown promise for monitoring neoadjuvant chemotherapy (NACT) response in breast cancer. Unfortunately, due to low sodium content in the body, its low MR sensitivity and short relaxation times in biological tissues, 23Na MRI suffers from intrinsically low signal-to-noise ratio (SNR), which can be up to 20,000 times lower than that of proton. Such low SNR translates into low spatial resolution and long acquisition times. Efforts to alleviate these challenges generally utilize high field systems (≥ 3 T), ultra-short echo time (UTE) acquisition methods, and tailored radiofrequency coils to boost the baseline SNR. Our focus is the coil design aspect. Specifically, we present a dual-tuned multichannel 1H/23Na bilateral breast coil consisting of volume transmit/receive (Tx/Rx) 1H coils, volume 23Na transmit coils and an 8-channel 23Na receive array for 7 T MRI which enabled sodium imaging in vivo with 2.8 mm isotropic nominal resolution (~5 mm real resolution) in 9:36 min. The proposed coil could enable access to even more specific biomarkers of cellular metabolism such as intracellular sodium concentration, and cellular density such as extracellular volume fraction that are still largely unexplored due to the challenges associated with 23Na MRI.

  • April 8, 2019, at noon

    Transcranial Ultrasound and Monitoring Devices for Brain Stimulation: Benchtop to Human-Scale Prototype Development in the Lab

    Spencer Brinker, PhD

    Associate Research Scientist
    Yale School of Medicine


    Transcranial Ultrasound (TUS) is an emerging field with a vast range of new potential clinical applications. Here, a series of new human scale TUS devices and the novel benchtop strategies used to develop them in the laboratory will be presented. These devices are intended for brain tumor cancer therapy and for treating neurological disorders such as epilepsy, pain, depression, and essential tremor. The presentation will include the latest developments for: 1) A neuronavigation-guided single-element transducer platform for delivering multi-target pulsed low-intensity TUS to human brain. 2) An integrated scalp sensor for simultaneous electroencephalography and acoustic emission detection. 3) A 3D passive acoustic mapping array device compatible with the FDA approved ExAblate 4000 system for localizing microbubble cavitation. Highlights of each technique relevant to current clinical investigations and future directions of each strategy will be discussed.

  • April 2, 2019, at noon

    Simultaneous proton MRF and sodium MRI

    Zidan Yu, MS

    PhD Candidate
    Biomedical imaging program
    Sackler Institute of graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Sodium(23Na) MRI can provide unique metabolic information to study the human body and its afflictions. However, the low intrinsic SNR of sodium MRI limits the resolution of the images to 3-5 mm isotropic and necessitates long acquisition times (~10-20 min). Moreover, the necessity to perform 1H and 23Na acquisitions sequentially prolongs the total scan time, which impedes the wide spread adoption of sodium imaging. In this talk, we will present a technique to simultaneously acquire sodium images and multi-parametric proton maps in one single scan.

  • March 26, 2019, at noon

    Diffusional Kurtosis Imaging of Gray Matter Neuropathology: Schizophrenia and Autism Spectrum Disorder

    Faye McKenna, MS

    PhD Candidate
    Biomedical imaging program
    Sackler Institute of graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Prior histological post-mortem studies have highlighted gray matter (GM) microstructural abnormalities as a pathological feature of both schizophrenia (SZ) and autism spectrum disorder (ASD). However, these histological studies were limited by the small sample sizes and focus on restricted brain areas. In this talk, we present our work examining the feasibility of diffusional kurtosis imaging (DKI) to describe gray matter microstructural abnormalities in SZ and ASD non-invasively and in vivo. DKI is an extension of diffusion tensor imaging that accounts for non-Gaussian water diffusion contributions to the diffusion MRI signal and provides several kurtosis indices that reflect tissue microstructural complexity. The talk will review existing research investigating DKI’s use to describe GM microstructure pathology in several clinical populations and animal disease models, as well as our recent findings showing significant differences in kurtosis intensity and lateralization metrics in SZ and ASD populations.

  • March 19, 2019, at noon

    Where to go beyond DTI: Diffusion MRI advantages at high b-values

    Emilie McKinnon

    PhD Candidate
    Medical University South Carolina


    Diffusion MRI (dMRI) has the unique ability to study brain microstructure at a resolution much smaller than the MRI voxel itself. The strength of diffusion weighting (i.e., the b-value) strongly impacts what information is contained in the dMRI signal. Since modern scanners have much stronger gradients, high b-value dMRI is becoming more feasible, and its utilization is likely to increase. High b-value acquisitions provide information beyond what is attainable with DTI and have proven useful for fiber tractography and for calculating diffusion measures that have greater biological specificity. This presentation will revisit a high b-value technique known as fiber ball imaging (FBI) but will mostly focus on how it can be used in combination with diffusion kurtosis imaging (DKI) to estimate microstructural parameters, such as compartmental water fractions and diffusion tensors. In addition, FBI provides the opportunity to calculate compartmental transverse relaxation times (T2) while avoiding multi-exponential fitting schemes.

  • March 14, 2019, at noon

    Quantitative Magnetic Resonance Imaging for Neurology and Cancer

    Ju Qiao, PhD

    Nanomedicine Science and Technology Center
    Department of Mechanical and Industrial Engineering
    Northeastern University, Boston, MA


    Magnetic Resonance Imaging (MRI) is an invaluable diagnostic tool for imaging the human body, diagnosing and characterizing diseases, and developing new treatments. In this work, we describe two applications of a novel MRI technique, Quantitative Ultra-short Time-to-echo Contrast-Enhanced (QUTE-CE) MRI to brain disease.

    In a first application, QUTE-CE is employed to quantify nanoparticle accumulation in tumors, which is of great clinical interest for stratifying cancer patients who may benefit from therapeutic nanoparticles. Using FDA-approved superparamagnetic iron oxide nanoparticle (SPION) ferumoxytol in QUTE-CE MRI, we produce quantitative measurement of contrast and delineate clear, positive-contrast brain/tumor vasculature image in mice and rats. QUTE-CE MRI is shown to improve contrast and contrast efficiency compared to conventional high-resolution T1- and T2-weighted imaging. QUTE-CE is ideally suited for non-invasive visualization and quantification of tumor nanoparticle uptake, and accordingly, it can potentially be used for identifying cancer patients who can respond to treatment with therapeutic nanoparticles.

    In a second application, QUTE-CE is employed to characterize traumatic brain injury (TBI). TBI is a prevalent risk of death and disability in young people with about 1.6 million cases reported per year in the US. Some of the most devastating injuries from brain trauma are the rupturing of arteries between the dura and the skull in an epidural hematoma (blood brain barrier disruption), as well as tears in emissary veins, resulting in hemorrhagic contusions seen in subdural hematomas. This accumulation of blood can squeeze and increase pressure on the brain. Here, we introduce a novel application of QUTE-CE to image blood accumulation and detect microbleeds in mild TBI animals. Rats which underwent 3 mild concussions showed significant difference in QUTE-CE MRI measure of ferumoxytol accumulation in extravascular space indicating blood brain barrier damage following TBI. These differences were observed primarily in cortex, hypothalamus, basal ganglia, cerebellum and brainstem. This study demonstrates that QUTE-CE MRI can be used to detect blood brain barrier disruption and microbleeds in mild TBI rats.

  • March 7, 2019, at noon

    Nonlinear Image Reconstruction Methods

    Prof. Dr. Martin Uecker

    German Centre for Cardiovascular Research
    University Medical Center Gottingen

    No abstract was provided for this talk.

  • March 5, 2019, at noon

    Metabolic and Physiologic MR Imaging in Evaluating Treatment Response in Patients with Glioblastomas

    Sanjeev Chawla, PhD

    Research Assistant Professor
    Department of Radiology
    Perelman School of Medicine at University of Pennsylvania


    Glioblastoma (GBM) is the most common primary malignant brain tumor in adults with poor prognosis. The standard of care for patients with GBM includes maximal surgical resection and concurrent chemo-radiation therapy followed by 6 to 12 cycles of adjuvant temozolomide (TMZ). Standard therapeutic approaches provide modest improvement in progression-free and overall survival, necessitating the investigation of novel therapies. Recently, FDA approved the use of tumor-treating fields for the treatment of patients with GBM. Additionally, several immunotherapeutic modalities such as chimeric antigen T cell receptors, check-point inhibitors and dendric cell vaccines hold much promise in the future treatment paradigms for these patients. In this presentation, I will discuss the potential roles of 3D-echoplanar spectroscopic imaging, diffusion and perfusion MR imaging techniques in evaluating treatment response in patients with GBM receiving established and novel treatment modalities. As non-invasive identification of patients harboring isocitrate dehydrogenase (IDH) mutant gliomas can have significant clinical implications, I will also present our initial experience on the utility of 2D-correlational spectroscopy in identifying glioma patients with IDH mutation.

  • February 12, 2019, at noon

    Time-Dependent Diffusion in the Brain

    Hong-Hsi Lee, MD, MS

    PhD Candidate
    Biomedical imaging program
    Sackler Institute of graduate Biomedical Sciences
    NYU Grossman School of Medicine


    Diffusion MRI is sensitive to the length scale of tens of microns, which coincides to the scale of microstructure in the human brain tissue. By varying the diffusion time, we can evaluate the brain micro-geometry via time-dependent diffusion measurements and the biophysical modeling. To validate our model, we segmented 3-dimensional realistic microstructure of the mouse brain white matter and performed Monte Carlo simulations of the diffusion in segmented axons. This talk will focus on the time dependence either along or perpendicular to white matter axons and corresponding micro-geometries, such as axonal diameter variation.

  • January 29, 2019, at noon

    Jens Jensen, PhD

    Professor of Neuroscience
    Associate Director of the Center for Biomedical Imaging
    Charleston, SC


    Fiber Ball Imaging (FBI) is a diffusion MRI method that estimates the orientation of axonal fibers in white matter from an inverse Funk transform. This approach avoids the need for numerical fitting to a signal model and for a fiber response function. FBI also yields predictions for certain microstructural parameters, including the fraction anisotropy axonal. When combined with triple diffusion encoding MRI, FBI can also be used to find the intra-axonal diffusivity and the axonal water fraction. This talk will focus on the basic concepts that underlie FBI but will also show data that support its validity and illustrate its application.

  • January 25, 2019, at noon

    Exploring the CUBES – expanding PET SPECT and CT imaging capabilities to accelerate translational research

    Niek Van Overberghe

    International Sales Manager


    Niek Van Overberghe (International Sales Manager @ MOLECUBES) will present on the unique technology at the core of the β-,γ and X-CUBE, preclinical imagers for PET, SPECT and CT. This new generation of in vivo imaging systems makes use of monolithic crystals coupled to solid state siPMs taking imaging one step further, combined with an in vivo CT system that ensures fast and low dose acquisitions. Thanks to this new technology, researchers can now inject lower activities, scan for a shorter time, hereby reducing the stress level on animals, increasing throughput, lowering radiotracer cost, and lowering the dose of the operator. Because of their unique bench top size, the instruments can be used in any lab around the world without needing building modifications. In addition, Niek will present on different applications that highlight the superior capabilities of these bench top modular systems compared to older systems.

To the top ↑

2018 Lectures

  • December 20, 2018, at noon

    Outlier Detection using Bayesian Deep Learning

    Nick Pawlowski

    PhD Candidate
    Biomedical Imaga Analysis Group
    Imperial College London


    Regardless of improved accuracy scores and other metrics, deep learning methods tend to be overconfident on unseen data or even when predicting the wrong label. Bayesian deep learning offers a framework to alleviate some of these concerns by modeling the uncertainty over the weights generating those predictions. This talk will introduce Bayesian deep learning and present the use of Bayesian NNs for outlier detection in the medical imaging domain, particularly the application of Brain lesion detection.

  • December 18, 2018, at noon

    Democratizing Magnetic Resonance Imaging; Open Source MRI Scanners for Education, Innovation, and Accessible Radiology

    Thomas Witzel, PhD

    Instructor in Radiology, Harvard Medical School
    Assistant in Biomedical Engineering, Massachusetts General Hospital
    Director Human MR Imaging Core, Athinoula A. Martinos Center for Biomedical Imaging


    Since its inception 45 years ago, development of MRI systems has been predominantly driven by commercial entities and innovation is centered on the commercial interests of these vendors. In a relatively small ecosystem of MRI manufacturers that compete in a low quantity, high profit margin market, innovation is effectively controlled by the manufacturer’s openness to outside access and is often limited by the manufacturer’s market needs. The possibilities are even more limited when it comes to disruptive modifications of the scanner hardware. In my presentation, I’ll discuss the need for and show the prospects of an open-source MRI system for education, disruptive innovation, and accessible healthcare and will show the results of educational work with a $500 fully open-source MRI spectrometer.

  • December 12, 2018, at noon

    Recent Advances in Using Machine Learning for Image Reconstruction

    Ozan Öktem, PhD

    Associate Professor
    Department of Mathematics
    KTH-Royal Institute of Technology
    Stockholm, Sweden


    The talk will outline recent approaches for using (deep) convolutional neural networks to solve a wide range of inverse problems, such as image reconstruction in medical tomography. A key element is to use a neural network architecture for reconstruction that includes physics based models that describe how data is generated as well as its statistical properties. Another is the possibility to integrate complex task related a priori information and elements of decision making into the reconstruction procedure. The resulting approach outperforms current state-of-the-art in terms of ‘quality’, computational speed and there is no need to manually set parameters as with variational methods. Furthermore, the amount of training data and network size can be kept surprisingly small. The talk will also touch upon further developments based on using generative adversarial networks for uncertainty quantification.

  • December 11, 2018, at noon

    Future Directions of Breast MRI—Potential of Deep Learning Tools

    Ritse M. Mann, MD, PhD

    Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, the Netherlands
    Department of Radiology, The Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands


    Ultrafast breast MRI provides excellent data for automated breast lesion classification. Incorporating morphology, T2 and DWI using various automated approaches, improves the classification task slightly, but late phase dynamics seem redundant. Since about a 3rd of detected breast cancers are missed on prior breast MRI while in retrospect clearly visible, there is a strong incentive for the development of systems that not only classify, but automatically detect breast lesions in MRI volumes. Using a deep learning system about 70% of these cancers can be marked at 2 false positives per volume. Since automated detection also demands automated segmentation of the breast and the fibroglandular tissue to determine the search space, these algorithms may potentially also be used for risk prediction and prognostication based upon qualified measures of fibroglandular tissue and background parenchymal enhancement.

  • December 4, 2018, at noon

    Diffusion of Intracellular Metabolites: a Compartment Specific Probe for Microstructure and Physiology

    Itamar Ronen, PhD

    Associate Professor of Radiology
    C.J. Gorter Center for High Field MRI Research
    Leiden University Medical Center
    The Netherlands


    Intracellular metabolites that give rise to quantifiable MR resonances are unique structural probes for the intracellular space, and are oftentimes specific, or preferential enough to a certain cell type to provide information that is also cell-type specific. In the brain, N-acetylaspartate (NAA) and glutamate (Glu) are predominantly neuronal/axonal in nature, whereas soluble choline compounds (tCho), myo-inositol (mI) and glutamine (Gln) are predominantly glial. The diffusion properties of these metabolites, examined by diffusion weighted MR spectroscopy (DWS) exclusively reflect properties of the intracellular milieu, thus reflecting properties such as cytosolic viscosity, macromolecular crowding, tortuosity of the intracellular space, the integrity of the cytoskeleton and other intracellular structures, and in some cases – intracellular sub-compartmentation and exchange.

    The presentation will introduce some of the methodological concepts of DWS and the particular challenges of acquiring robust DWS for accurate estimation of metabolite diffusion properties. Subsequently, the unique ability of DWS to characterize cell-type specific structural and physiological features will be demonstrated, focusing on combining DTI/DWI and DWS in a combined analysis framework aimed at better characterizing tissue microstructural properties, as well as acquisition strategies aimed at characterizing compartment-specific microscopic anisotropy (µFA) in tissue. Also presented are applications of DWS to discern cell-type specific intracellular damage in disease.

  • November 19, 2018, at noon

    Acquisition Advances for Efficient & Joint Diffusion-Relaxometry MRI

    Jana Hutter, PhD

    Research Fellow
    Centre for Biomedical Engineering & Centre for the Developing Brain
    King’s College London


    Emerging novel analysis techniques offering insights into microstructure and tissue properties require more and more eloquent data. This talk will introduce some of our recent advances on the acquisition side, presenting multi-parametric diffusion acquisitions – extending the parameter space to allow integrated T1, T2* and Diffusion sampling (b-value,b-vector, b-shape) within reasonable imaging times. Combination with Multiband imaging and sampling strategies in this multi-dimensional space will be discussed and exploratory data-driven analysis results presented.

  • November 13, 2018, at noon

    Excitement and Challenges in Building Medical Imaging Products in the Real World with Artificial Intelligence

    Li Yao, PhD

    Lead Data Scientist
    Enlitic, San Francisco, CA


    AI, in its much misinterpreted form, holds the promise of revolutionizing medical imaging in healthcare. In practice, however, many challenges remain. This talk presents some of the challenges that we, as a company, have recognized on the way of building better tools for radiologists. In particular, Dr. Li Yao, the Lead Data Scientist at Enlitic, will share with the audience three project stories, one with Chest X-ray, one with Chest CT, and one with medical text reports, each of which highlights unique excitement and challenge in the real world clinical context. The talk will be overall technical on AI and machine learning side.

  • November 6, 2018, at noon

    Machine learning and Computer Vision in Radiology

    Maciej Mazurowski, PhD

    Associate Professor of Radiology
    Electrical and Computer Engineering, Biostatistics and Bioinformatics
    Duke University


    The terms artificial intelligence, machine learning, deep learning, or computer vision are mentioned increasingly often in the radiology community. In this talk, Dr. Mazurowski will talk about how these methods can help radiologists in their clinical practice as well as how they can advance science by improving our understanding of cancer. The talk will be concluded with more general thoughts on the future of the radiology profession in the advent of human-level artificial intelligence. Dr. Mazurowski is an Associate Professor of Radiology, Electrical and Computer Engineering, and Biostatistics and Bioinformatics and Duke University. He leads a research laboratory with focus on applications of machine learning to cancer imaging.

  • October 23, at noon

    Towards MRI Virtual Tissue Microscopy with Diffusion MRI: the Aarhus Perspective

    Sune Jespersen, PhD

    CFIN/MindLab and Deptartment of Physics and Astronomy
    Aarhus University, Denmark


    Being sensitive to tissue structural features on the micrometer level (microstructure), diffusion MRI combined with biophysical modeling has the potential to map relevant biological properties on scales far below the nominal voxel resolution. In the brain and spinal cord, much work in this direction has been based on a relatively simple biophysical model of diffusion, recently dubbed “the standard model”. This model characterizes the diffusion signal in terms of a handful of relevant parameters: neurite volume fraction, intra-neurite and extra-neurite diffusivity, and the neurite or fiber orientation distribution. In this talk, I will give some background for the standard model and an overview of our work with it, covering our efforts to validate the model in animal model systems including some comparison with histology. I will also outline some current problems with the model and ongoing attempts to overcome them.

  • October 16, 2018, at noon

    Learning from Noisy Data: How to Teach Machines when Doctors Disagree with Each Other

    Ryutaro Tanno

    PhD student in Machine Learning and Medical Imaging
    University College London, UK
    Centre for Medical Image Computing, Department of Computer Science


    Access to clean and voluminous datasets is a piece of luxury confined to academic research for many machine learning applications. In practice, such datasets are hard to come by, and consequently limit the performance of deployed machine learning systems. This problem is pervasive in medical imaging applications where the cost of data acquisition and labelling is high. In this talk, I will present a method that is capable of learning more intelligently from such noisy data by modelling the human annotation process. This is particularly relevant in situations where data is labelled by multiple annotators of varying skill levels and biases.

  • October 9, 2018, at noon

    Electroporation-based Technologies and Treatments

    Prof. Dr. Damijan Miklavcic

    Faculty of Electrical Engineering
    University of Ljubljana


    When cells are exposed to high voltage electric pulses their membranes become transiently permeable, i.e. molecules otherwise deprived of transmembrane transport molecules can gain access into the cytosol. This phenomenon is called electroporation. It can be reversible – cells survive or irreversible, if cells die. The former is used to introduce genes into cells for gene therapy and DNA vaccination (gene electrotransfer) or to increase effectiveness of some chemotherapeutic drugs (electrochemotherapy), while the latter is used as a non-thermal tissue ablation method.

    Electroporation of cells depends on local electric field to which cells are exposed. In vivo in tissue electric field is impossible to measure directly. Therefore current density imaging and magnetic resonance impedance tomography have been used to elucidate electric field distribution and was correlated with cell membrane permeabilisation.

  • October 2, 2018, at noon

    Unraveling Breast Cancer with Multimodal Molecular Imaging

    Kristine Glunde, MS, PhD

    Professor of Radiology and Radiological Science
    Johns Hopkins University School of Medicine


    Novel molecular imaging techniques are allowing us to visualize breast tumor biology in unprecedented molecular detail. These include the use of magnetic resonance spectroscopic imaging, mass spectrometric imaging, and Raman imaging for mapping molecular and metabolic pathways in breast cancer. Applications of these molecular imaging techniques are improving our understanding of metabolic and oncogenic signaling in breast cancer progression, metastasis, and response to therapy. Finally, we are also investigating the processes that lead to “molecular priming” of metastatic target organs prior to the arrival of the first metastasizing cancer cells.

  • September 11, 2018, at noon

    Imaging-Based Methods for Assessment of Metabolic Bone Disease

    Chamith S. Rajapakse, PhD

    Assistant Professor of Radiology
    University of Pennsylvania


    Millions of people worldwide suffer from metabolic bone diseases, predisposing them to bone fractures and devastating consequences. Within a year of a hip fracture, for example, 20-30% of patients die and 50% lose the ability to walk. Medical imaging plays an important role in the diagnosis of bone disease, staging, fracture risk assessment, and monitoring of treatment. Radiographs and dual energy X-ray absorptiometry (DXA), which provides semi-quantitative assessment, are the modalities of choice for clinical management of metabolic bone diseases. Recent advances in medical imaging technologies and analysis techniques have enabled novel non-invasive approaches for quantification of bone quality. For example, it is now possible to obtain three-dimensional high-resolution images depicting the trabecular and cortical microstructure in human subjects. Ability of obtain high resolution images has paved the way for elegant image analysis algorithms for extracting information about various aspects of bone quality not feasible previously. For example, it is now possible to characterize trabecular bone microarchitecture using multi-row detector CT and the tensor scale algorithm or estimate the hip fracture strength using high-resolution imaging based finite element analysis. More recently, MRI, CT, ultrasound, and PET techniques have been developed for extracting novel biomarkers related to bone strength. For example, bone water assessed by MRI has been proposed as a new biomarker for bone quality. Many of these imaging-based techniques could provide early differential diagnosis, periodic monitoring, and a comprehensive assessment of bone quality thereby potentially changing the way metabolic bone diseases are managed in the future.

  • August 28, 2018, at noon

    Bringing MR-Guided Focused Ultrasound into Focus

    Kim Butts Pauly, PhD

    Stanford University

    No abstract was provided for this talk.

  • August 21, 2018, at noon

    Fast Analog to Digital Compression for High Resolution Imaging

    Prof. Yonina Eldar

    Department of Electrical Engineering
    Technion, Israel Institute of Technology


    The famous Shannon-Nyquist theorem has become a landmark in the development of digital signal processing. However, in many modern applications, the signal bandwidths have increased tremendously, while the acquisition capabilities have not scaled sufficiently fast. Consequently, conversion to digital has become a serious bottleneck. Furthermore, the resulting high rate digital data requires storage, communication and processing at very high rates which is computationally expensive and requires large amounts of power. In the context of medical imaging sampling at high rates often translates to high radiation dosages, increased scanning times, bulky medical devices, and limited resolution.

  • August 7, 2018, at noon

    Emergent Functional Connectomics in Human Fetal Brain Networks

    Moriah E. Thomason, PhD

    Associate Professor, Department of Child and Adolescent Psychiatry
    NYU School of Medicine


    While we possess rather detailed understanding of select micro- and macroscopic processes of normal human brain development, we know far less about how brain changes relate to behavioral changes over the course of life from the prenatal period to early adulthood. This lack of understanding is especially pronounced in very early years of human life, years where change is most rapid, and vulnerability heightened. The primary objective of our research is to characterize fundamental properties of human brain macroscale neural system development, and examine how early experiences, beginning in utero, influence life-long learning and neurological health. We are testing models in which early psychosocial stress and concomitant toxin exposure influence development of neural systems, particularly those that support the establishment of cognitive control and regulatory processes in childhood. Rigorous evaluation of emergent self-regulatory processes and their neurological and biobehavioral bases has potential to inform educational strategies and lead to biologically-informed behavioral interventions for those with enhanced risk.

  • July 24, 2018, at noon

    Imaging Microstructural Dynamics Using Diffusion MRI

    Daniel Nunes, PhD

    Neuroplasticity and Neural Activity Lab
    Champalimaud Center for the Unknown
    Lisbon, Portugal

    No abstract was provided for this talk.

  • July 10, 2018, at noon

    What Do We Really Measure with the MRI Signal Phase?

    Valerij G. Kiselev, PhD

    University Medical Center Freiburg
    Freiburg, Germany


    Decade-old measurements of the MRI signal phase in the human brain white matter ignited a still-ongoing discussion of how to calculate the Larmor frequency of NMR-visible spins in magnetically heterogeneous media. While this discussion has somewhat decoupled from the original biomedical context going deep into NMR physics and questioning the assumptions behind the Lorentz cavity construction, its practical implications may significantly limit the applicability of quantitative susceptibility mapping (QSM). This talk will give an overview of the biophysical origins of the Larmor frequency offset. A simple model, which remains in the debate focus, is used to illustrate the relation between the microstructure and the Larmor frequency. A closed-form analytical solution for this model is obtained in the practically relevant limit of fast diffusion. This solution illustrates the microstructural correlates of the recent empirical nerve tissue description, adds to the discussion of the Lorentz cavity construction in heterogenous media, and formulates the major challenge for the QSM. The talk will conclude with a discussion of the unresolved problems on the way to building realistic models for white matter magnetic microstructure.

  • June 12, 2018, at noon

    Magnetic Resonance Spectroscopic Imaging of Epilepsy

    Rebecca Feldman, PhD

    Senior Scientist
    Translational and Molecular Imaging Institute
    Icahn School of Medicine at Mount Sinai


    Epilepsy affects approximately 2.2 million people in the United States. Thirty percent of epilepsy is refractory to pharmacotherapy, and surgical treatment of refractory epilepsy can often be the most effective treatment option. Investigations of the resected tissue of MRI-negative subjects suggest that there exist focal epileptogenic lesions, amenable to resection, that are not detectable using current clinical MRI protocols.

    Magnetic resonance spectroscopic imaging (MRSI) provides metabolic information which is complimentary to structural imaging. We have developed a novel B1-insensitive semi-adiabatic spectral-spatial imaging sequence (SASSI) which was designed to overcome many of the limitations of MRSI at ultra-high fields, enabling effective acquisition of high resolution grids of spectra. We have used SASSI to detect metabolic alterations in the head and body of the hippocampus of patients with focal epilepsy who were non-lesional or inconclusive in their clinical MRI exams.

  • May 25, 2018, at noon

    Life at the Bottom: Deconstructing MRI at 6.5 mT with Physics, AI, and Nanodiamonds Too

    Matthew Rosen, PhD

    Director, Low Field MRI and Hyperpolarized Media Laboratory
    Co-Director, Center for Machine Learning
    MGH/Martinos Center for Biomedical Imaging
    Harvard Medical School

    No abstract was provided for this talk.

  • May 18, 2018, at noon

    Biophysically Interpretable Recurrent Neural Network for Functional Magnetic Resonance Imaging Analysis

    Yuan Wang, PhD

    Department of Electrical and Computer Engineering
    Tandon School of Engineering, NYU


    Dynamic Causal Modelling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional magnetic resonance imaging (fMRI) signals via neural activity and cerebral hemodynamics. To conduct a DCM study, one needs to represent the experimental stimuli as a compact vector-valued function of time, which is hard in complex tasks such as book reading and natural movie watching. Deep learning provides the state-of-the-art signal representation solution, encoding complex signals into compact dense vectors while preserving the essence of the original signals. There is growing interest in using Recurrent Neural Networks (RNNs), a major family of deep learning techniques, in fMRI modeling. However, the generic RNNs used in existing studies work as black boxes, making the interpretation of results in a neuroscience context difficult and obscure.

    In this paper, we propose a new biophysically interpretable RNN built on DCM, DCM-RNN. We generalize the vanilla RNN and show that DCM can be cast faithfully as a special form of the generalized RNN. DCM-RNN uses back propagation for parameter estimation. We believe DCM-RNN is a promising tool for neuroscience. It can fit seamlessly into classical DCM studies. We demonstrate face validity of DCM-RNN in two principal applications of DCM: causal brain architecture hypotheses testing and effective connectivity estimation. We also demonstrate construct validity of DCM-RNN in an attention-visual experiment. Moreover, DCM-RNN enables end-to-end training of DCM and representation learning deep neural networks, extending DCM studies to complex tasks.

  • May 17, 2018, at 13:30 p.m.

    Various Roles of Total Variation Regularization for Low-Level Vision and Inverse Problems in MRI

    Youngwook Kee , PhD

    NY Postdoctoral Associate in Radiology
    Weill Cornell Medical College, New York


    In this talk, I will present 3 different roles of total variation (TV) regularization in variational methods for unsupervised image segmentation in computer vision, deconvolution in quantitative susceptibility mapping (QSM), and image reconstruction for fast multicontrast MRI. First, TV as a measure of the perimeter of a candidate partition encoded by the indicator function of a set. In unsupervised image segmentation, the total length of region boundaries is often minimized to obtain a compact partition that likely matches the way humans perceive. A statistical distance between color distributions of distinctive regions in a candidate partition is maximized with the minimization of TV for unsupervised image partitioning. Second, TV as a measure of the amount of streaking artifacts in QSM deconvolution. QSM is a noninvasive MRI method for a quantitative study of the tissue magnetic susceptibility distribution by solving magnetic field to susceptibility source inversion problem. A major challenge in the ill-posed inverse problem is streaking artifacts from noise in the field which propagates at the complementary magic angle. These artifacts can be selectively reduced by weighted TV regularization that makes use of anatomical information of the corresponding magnitude image. Lastly, TV as a measure of undersampling artifacts in image reconstruction for multicontrast MRI. In clinical MRI, multiple contrasts such as T1w, T2w, and FLAIR are sequentially acquired, consequently taking a long scan time. To shorten such a long scan time, structural information that exists between contrasts is extracted from T1w and is incorporated into the TV term as an orthogonal projector in the model-based image reconstruction for the subsequent contrasts that are highly undersampled.

  • May 15, 2018, at noon

    MRI-guided Targeting of Therapeutics to the Brain at High Precision

    Piotr Walczak, MD

    Associate Professor
    Johns Hopkins Medicine
    Department of Radiology

    No abstract was provided for this talk.

  • May 11, 2018, at noon

    Magnetic Particle Imaging: Introduction to Physics and Instrumentation

    Alexey Tonyushkin, PhD

    Research Assistant Professor and Director of Technical Operations
    University of Massachusetts Boston


    Magnetic Particle Imaging (MPI) is a new tomographic imaging modality that offers high spatial and temporal resolutions. Compared to the other imaging modalities such as MRI/CT/PET, MPI is non-toxic, more sensitive, and fully quantitative technique. MPI addresses clinical and research needs for safe diagnostic and therapeutic applications such as cancer screening, cell tracking, and angiography. To date, a few small-bore MPI systems have been developed, however, human-size MPI scanner has yet to be built. The major challenge of scaling up of MPI is in high power consumption that is associated with the traditional approach to designing the scanner. In my talk, I will overview the basics of MPI, specifically, physics and instrumentation that includes two fundamental types of MPI topologies: field-free-point and field-free-line. Then I will describe my approach to designing MPI scanner and also will show how traditional MPI can be blended with atom optics to incorporate an atomic magnetometer as a very sensitive way of detecting the signal.

  • May 2, 2018, at 10:30 a.m.

    Physiologically Informed Diagnosis Using Cardiac Mobile Health Systems

    Joachim A. Behar, PhD

    Postdoctoral Fellow
    Department of Biomedical Engineering
    Technion Israel Institute of Technology


    With billions of mobile devices worldwide and the low cost of connected medical hardware, recording and transmitting medical data has become easier than ever. However, this ‘wealth’ of physiological data has not yet been harnessed to provide actionable clinical information. This is due to the lack of smart algorithms that can exploit the information encrypted within these ‘big databases’ of biomedical time series and take individual variability into account. Exploiting these data necessitates an in depth understanding of the physiology underlying these biomedical time series, the use of advanced digital signal processing and machine learning tools to recognize and extract characteristic patterns of health function, and the ability to translate these patterns into clinically actionable information.

    In this talk I will present my research in electrophysiology, namely the “Fetal Holter AI” and “Cardio AI” projects. For these two research projects I leverage state-of-the-art signal processing and machine-learning techniques to harness physiological information contained in biomedical time series and provide clinically actionable information. The “Fetal Holter AI” project aims to create a novel intelligent non-invasive fetal Holter electrocardiogram system to diagnose for fetal arrhythmias and remotely monitor the fetal cardiac health. The “Cardio AI” project has two aims: (1) to better understand the physiological information contained in the heart rate variability i.e. the time interval variation between consecutive heartbeats. I will present a new software, PhysioZoo, which we developed in our laboratory at the Technion for analyzing the heart rate variability from animal models; (2) to create an artificial intelligence system which can identify cardiac pathologies from the electrocardiogram with accuracy similar to the cardiologist’s direct interpretation. I will finish my presentation by briefly mentioning the “SmartCare Sleep AI” project which aims at creating a single channel screening test for obstructive sleep apnea. I will present my work in elaborating this test using patterns recognition from the oximetry time series in order to recognize individuals with this medical condition.

  • May 2, 2018, at noon

    Imaging Insights into the Vascular Nature of Brain Disorders

    Audrey Fan, PhD

    Instructor, Radiology
    Stanford University


    Our brain depends on continuous blood flow to deliver the oxygen and nutrients it needs to function. Disruption to this oxygen supply, as in cerebrovascular diseases, has devastating consequences, most strikingly in acute stroke. Noninvasive imaging of brain blood flow and metabolism is technically challenging, but would provide critical information to diagnose and select therapies for patients.

    My mission is to engineer new imaging biomarkers of brain physiology to address this need. In this talk, I describe development of a novel magnetic resonance imaging (MRI) technique to quantify oxygenation in cerebral blood vessels. I also validated MRI methods to measure cerebral blood flow against the reference standard by positron emission tomography (PET), using state-of-the-art simultaneous PET/MRI hardware. I performed these studies in challenging cerebrovascular patient cases, including Moyamoya disease, and used imaging to inform our basic understanding of disease pathophysiology.

    In the long term, the imaging tools I develop will establish a vascular “fingerprint” that succinctly captures the metabolic health of an individual, and alerts us to a broad set of neurological diseases in its earliest stages.

  • May 1, 2018, at noon

    The Deep Learning Revolution: Implications for Radiologists

    Greg Zaharchuk, MD, PhD

    Associate Professor of Radiology, Neurosciences Institute
    Stanford University, California

    No abstract was provided for this talk.

  • April 27, 2018, at noon

    Magnetic Resonance Fingerprinting: A Flexible Framework for Fast Quantitative MRI

    Dan Ma, PhD

    Research Scientist
    Department of Radiology
    Case Western Reserve University


    Current clinical MRI often consists of a series of qualitative or weighted measurements of tissue properties, such as T1-weighted or T2-weighted images. These qualitative measurements have some inherent limitations. The relative contrasts from these images may change depending on the set-up of the acquisitions, the type of the scanners and so on. The interpretation of images thus only relies on subjective assessment or morphological measurement. This limits the ability to diagnose pathology in a reproducible and reliable manner, to characterize tissues and lesions and to longitudinally follow up lesions or to assess response to novel therapies. The weighted contrast from multiple underlying tissue properties may also reduce the sensitivity and specificity to detect and characterize subtle and diffuse diseases. Although these limitations could be overcome by collecting fully quantitative tissue maps using quantitative MRI techniques, the adoption of quantitative MRI in clinical practice is hampered due to its long scan time, low repeatability and lack of robustness.

    This talk will introduce the concept and technical advances of magnetic resonance fingerprinting (MRF), which is a robust and flexible framework for fast, multi-parametric quantitative MRI. This technology allows quantification of multiple key tissue properties, such as T1, T2, T2*, and perfusion in a clinically feasible time and with high repeatability, which overcomes the barrier of clinical adoption of quantitative MRI. Since MRF is a dictionary based method that has no requirement of the encoding methods and signal shapes, this technology also allows flexible sequence designs for various clinical applications, and flexible numerical simulation for sophisticated physical and physiological settings. Both features contribute to more robust and accurate quantitative results. Finally, the talk will discuss some clinical applications of MRF, demonstrating promising clinical translation of this technology.

  • April 18, 2018, at noon

    Recent Developments in Cardiovascular MRI and MR-guided Radiation Therapy

    Peng Hu, PhD

    Associate Professor
    Department of Radiological Sciences
    David Geffen School of Medicine
    University of California, Los Angeles, California


    MRI with ferumxotyol as a intravascular contrast agent holds great promises for a number of clinical applications. In this talk, Dr. Hu will discuss translational ferumoxytol-enhanced cardiovascular MRI techniques that enables new paradigms for imaging congenital heart disease and beyond. In the second half of the talk, Dr. Hu will discuss his recent work in developing MRI techniques for guiding radiation therapy with regard to anatomical tracking and tumor response assessment.

  • April 3, 2018, at noon

    Quantitative Neuroimaging of the Peripheral Nervous System

    Richard Dortch, PhD

    Research Assistant Professor
    Radiology and Radiological Sciences
    Vanderbilt University


    The peripheral nervous system is primarily composed of nerves that transmit motor and sensory information between the spinal cord and the body. Damage to these nerves results in a wide array of symptoms, ranging from temporary numbness, tingling, and pricking sensations to burning pain, muscle weakness, paralysis, organ failure, and death. Although clinicians have tools for assessing peripheral neuropathies (e.g., nerve conduction studies), they provide limited information in proximal and/or transected nerves. Quantitative MRI techniques (e.g., diffusion and magnetization transfer) may overcome these limitations by providing assays of myelin and axon pathologies throughout the peripheral nervous system. Unfortunately, few studies have applied quantitative MRI techniques to study peripheral neuropathies in humans in vivo. This can be attributed to the technical challenges associated with peripheral nerve MRI, including the need for higher spatial resolution in feasible scan times, a lack of contrast on standard anatomical images, and the influence of surrounding fat. In this talk, Dr. Dortch will discuss i) methods to overcome the technical challenges associated with peripheral nerve MRI and ii) applications of quantitative MRI methods in inherited neuropathies and trauma.

  • March 27, 2018, at noon

    Neuroreceptors at Work: Imaging Molecular Dynamics & Signaling with PET/fMRI

    Christin Sander, PhD

    A.A. Martinos Center of Biomedical Imaging
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School


    Advances in simultaneous positron emission tomography (PET) and magnetic resonance imaging (MRI) have enabled novel approaches for in vivo functional brain mapping. The complementary nature of the imaging signals acquired by PET and functional MRI (fMRI) permits new insights into neurotransmission of the living brain: fMRI localizes changes in brain activity, whereas PET captures the underlying molecular and receptor-specific dynamics. One of the potentials of this technology is to provide new clinical biomarkers for the evaluation of dynamic receptor function and therapeutic interventions.

    This talk will describe how simultaneous functional imaging with PET/fMRI leads to novel mechanistic insights through neuromodulation of brain function. The focus will be on interventions that target the dopamine receptor system, either through pharmacological or direct electrical stimulation. I will show that neurovascular coupling to receptors as identified by PET/fMRI can be used to classify drug properties in vivo. Together with biological and pharmacokinetic models, mechanistic insight into receptor adaptations over time can be gained. I will then talk about the in vivo effects of deep brain stimulation, and how the combined use of experimental approaches allows us to unravel receptor subtype contributions to observed signal changes. Finally, I will show how PET and fMRI can be used for evaluating the effects of flow, and how the combination of both modalities can provide alternative approaches for evaluating radiotracer probes of novel in vivo receptor targets.

  • March 23, 2018, at noon

    Oxidative Stress and its Functional Consequences Measured In Vivo by MRI

    Bruce Berkowitz, PhD

    Professor, Department of Anatomy & Cell Biology
    Professor, Department of Ophthalmology
    Director of Small Animal MRI Facility
    Wayne State University School of Medicine
    Detroit, Michigan

    No abstract was provided for this talk.

  • March 23, 2018, at 10:30 a.m.

    Novel Detection Methods for Chemical Exchange and Their Applications

    Shu Zhang, MSc

    PhD candidate
    The University of Texas Southwestern Medical Center
    Dallas, TX


    Chemical exchange saturation transfer (CEST) and the closely related off-resonance T1ρ methods are gaining popularity for their ability to visualize chemical exchange process between protons bound to solutes and surrounding bulk water, thus providing a contrast based on proton exchange sites with different chemical shifts. The contrast is also influenced by pH, temperature and molecule concentration. Therefore, CEST/T1ρ can provide molecular level information which reflects biochemical composition of tissues and their microenvironments. As a result, many promising applications of CEST imaging are explored, including but not limited to brain tumor imaging, brain ischemia, prostate cancer, breast cancer, kidney pH measurement and cartilage quality assessment. In the meanwhile, a lot of efforts are made to develop fast and quantitative CEST imaging methods to push CEST techniques toward clinical use.

    To accelerate quantitative CEST imaging, we have developed a method based on the balanced steady-state free precession sequence as an alternative way for chemical exchange detection (bSSFPX). The feasibly of bSSFPX for chemical exchange detection was proved both theoretically and experimentally on phantoms. Mathematical models for bSSFPX were developed for quantitative measurements of T1ρ and exchange rate. bSSFPX was applied in the human brain. While the exact origin of the contrast is still under investigation, we hypothesize it is due to the chemical exchange from fast exchanging metabolites with resonance frequencies close to water. Detection of these metabolites is challenging for standard CEST imaging methods at 3T.

    While application of CEST to brain malignancy is increasing, its application in body imaging is still challenging. One of the difficulties is the presence of large lipid signals. We have studied the influence of non-exchanging fat on CEST imaging using simulation, phantoms, and in vivo studies at different fat fractions and echo times. To remove the fat influence on body CEST imaging, we have developed a CEST-Dixon imaging sequence for fat free CEST imaging and applied it to human breast malignancy characterization at 3T. We have demonstrated that the CEST-Dixon sequence eliminates lipid contamination robustly in breast CEST imaging. The results display a potential for improved non-invasive characterization of human breast lesions at 3T using CEST, potentially differentiating more aggressive from less aggressive tumors.

  • March 20, 2018, at noon

    Biophysical Modeling of the White Matter: From Preclinical Validation to Clinical Perspective

    Jelle Veraart, PhD

    Champalimaud Center of the Unknown
    Lisbon, Portugal


    The morphology of the white matter&once referred to as nature’s finest masterpiece&is intricately coupled with brain function. Being able to measure the white matter structure, and its pathological changes, in vivo and non-invasively would promote the study of brain function and the more specific diagnosis of brain disorders. Despite a limited spatial resolution, the sensitivity of diffusion MRI to the Brownian motion of protons restricted by cellular structures, such as axons, provides an exciting avenue to reveal the microscopic architecture. However, bridging the length-scale gap requires the development and validation of a biophysical white matter model that decomposes the signal in components that probe specific features of the underlying microstructure, e.g. axon diameters. During this talk, I will focus on my recent work on the mapping of micrometer-thin axons using MRI, from preclinical validation to a clinical perspective.

  • March 13, 2018, at noon

    Computational Neuroanatomy of the Human Brain White Matter and Beyond

    Demian Wassermann, PhD

    Associate Research Professor
    Parietal Team
    INRIA Saclay Ile-de-France


    The motivation of this talk is the computational encoding of neuroanatomy in terms of tissue characteristics as well as classical neuroanatomical knowledge.

    The first problem to address will be the in vivo dissection of the human brain’s white matter from diffusion magnetic resonance imaging. We address this through representing current anatomical knowledge computationally. In this talk I will introduce computational tools to represent human anatomy. More precisely, I will introduce a domain specific programming language to represent and automatically extract the major white matter structures in the human brain’s white matter, the white matter query language (WMQL) as well as applications of these techniques to dyscalculia and schizophrenia.

    Then I will move on to presenting techniques to perform group-based studies to parcel the cortical mantle based on white matter connectivity. Specifically, I will show how leveraging a consistent mathematical model of axonal-based cortical connectivity we are able to separate subject and parcel-specific characteristics in a random effects model. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data.

    Finally, I will provide a prospective view on NeuroLang, project that has the goal of extending the concepts explored in the previous to takes to computational neuroanatomy of the white matter to the bulk of human neuroanatomy.

  • March 9, 2018, at noon

    Advances in Parallel Transmission and Temperature Reconstruction for High Field MRI

    Zhipeng Cao, PhD

    Research Assistant Professor
    Biomedical Engineering
    Vanderbilt University


    In recent years there is an increased interest to build massively parallel transmit (pTx) systems for high spatial-temperal resolution MR imaging. A novel pTx pulse compression method (array-compressed pTx, acpTx) is proposed, validated, and implemented that allows a many-element parallel transmit array to be driven by only a few power amplifiers without significantly degrading the pulse performance. AcpTx provides a new insight into pTx array design by synergistically integrating both Maxwell and Bloch principles. By maximizing the pulse performance for a pTx system with limited number of power amplifiers, acpTx can further improve highly accelerated MR imaging with multichannel reception. Specifically, acpTx is shown to improve the motion and phase errors of multi-shot EPI that would enable short TE fMRI and dMRI applications compared to single-shot EPI. In addition, the presentation will also include recent advances in improved multichannel compressed sensing reconstruction for PRF temperature imaging for MRgHIFU and high field RF heating monitoring, as well as a novel application of dielectric materials to accelerate abdominal imaging.

  • March 7, 2018, at noon

    Breaking the Mold of Conventional Gradients and Shims: New MRI Hardware for 70 mT and 7 Tesla

    Jason Stockmann, PhD

    Instructor in Radiology
    A. A. Martinos Center for Biomedical Imaging
    Massachusetts General Hospital & Harvard Medical School


    Conventional gradient and shim coils for MRI generate pure spherical harmonic “B0” magnetic fields for shimming and spatial encoding, simplifying the image acquisition process. Unfortunately, these coils are difficult to build and impose high demands for space, cooling, and power consumption. In this talk, I will discuss a recent trend toward flexible spatial encoding and field control methods that use non-orthogonal magnetic field basis sets or alternative encoding mechanisms. As an example of this trend, I will discuss progress at MGH on a portable brain scanner that uses a rotating permanent magnet array with a built-in spatial encoding field to perform imaging without conventional B0 gradient coils, greatly reducing cost, weight, and power consumption. In this approach, imperfections in the linear encoding field are accounted for in the encoding matrix during image reconstruction, shifting the engineering burden away from complex hardware and into software. As a second example of flexible spatial encoding, I will show how spatial encoding can also be performed using lightweight, tailored radiofrequency coils whose “B1+” transmit field has a linear phase variation over space, enabling phase-encoded imaging in spin echo train sequences. Finally, I will review progress on multi-coil arrays of shim coils that use a non-orthogonal basis set to dynamically null localized, high-spatial order patterns of B0 inhomogeneity in the body. I will further show how multi-coil shimming can be physically integrated with the RF receive array to save space near the body. I’ll then demonstrate how multi-coil shimming can benefit echo planar imaging, MR spectroscopy, and selective excitation, overcoming some of the limitations of conventional spherical harmonic shim coils.

  • February 28, 2018, at noon

    Update in Lung Transplantation and Predicting Chronic Rejection

    Luis Angel, MD

    Professor of Medicine
    Director of Lung Transplantation
    NYU Langone Medical Center

    No abstract was provided for this talk.

  • February 27, 2018, at noon

    Cerebral MR Thermometry in Neurovascular Ischemia

    Seena Dehkharghani, MD

    Associate Professor of Radiology
    Director, Stroke and Cerebrovascular Imaging
    Department of Radiology
    NYU Langone Health


    Cerebral thermoregulation is poorly understood but critical to brain homeostasis and viability. Temperature disturbances strongly potentiate cerebrovascular and other CNS injury, and represent potent targets for neuroprotection. Interrogation of brain temperature has historically been limited to costly and highly invasive implantable probes, and pragmatic approaches to measuring spatiotemporal temperature gradients are lacking. Cerebral MR thermometry may provide safe, non-invasive, and reproducible characterization of brain temperatures across physiologic, ischemic, and other pathologic disease states. This presentation will discuss initial experience with chemical shift thermometry as a biomarker of cerebrovascular injury in human and nonhuman primates, emphasizing the critical role of brain temperature at the intersection of perfusion, metabolism, and cytotoxic injury.

  • February 26, 2018, at noon

    Faster MRI through Optimized Encoding and Reconstruction

    Berkin Bilgic, PhD

    Instructor in Radiology
    Harvard Medical School


    Our research focuses on developing techniques that dramatically improve the efficiency of MRI by collecting/deriving more information from each unit time of data acquisition. The overarching goal in creating these strategies is twofold:

    • pushing the limits of spatial/temporal resolution and CNR of MRI to make it a better neuroscientific tool;
    • improving throughput, motion robustness and efficiency of clinical exams to make MRI more cost effective and more widely used in the clinic.

    We are pursuing these goals in a number of areas, including structural, diffusion and spectroscopic imaging, as well as the quantitative techniques of MR fingerprinting, susceptibility mapping and myelin imaging. The order of magnitude efficiency gain we achieved for these acquisitions has been possible through a joint design approach that combines new hardware capabilities, new sequence and readout design, and novel image reconstruction exploiting sparsity, mutual information and deep learning.

  • February 23, 2018, at noon

    RF Coil Designs for Ultra-High-Field Magnetic Resonance in Humans

    Özlem Îpek

    Postdoctoral Fellow & Managing Director RF Lab
    Centre d’imagerie biomédicale (CIBM)
    Ecole Polytechnique Federale de Lausanne (EPFL)
    Lausanne, Switzerland


    To acquire high-resolution and –sensitivity images at ultra-high field magnetic resonance (MR) scanner, various hardware solutions can be utilized: dedicated RF coil design for a certain anatomical region of the human brain, merging high dielectric constant materials with the existing RF coil concepts, use of multi-channel transmit RF coil arrays on a parallel transmit system to steer any signal amplitude or phase. Besides these solutions, MR safety limitations have been wisely investigated, i.e. simultaneous EEG-fMRI setup at 7 T MR is simulated with finite difference time domain method to assess its RF safety. My talk will address various RF hardware solutions for 7 Tesla human proton and multi nuclei MR imaging and spectroscopy.

  • February 16, 2018, at noon

    The Deep Learning: Revolution in Medical Imaging

    Fang Liu, PhD

    Assistant Scientist, Department of Radiology
    University of Wisconsin, Madison


    This talk will present an overview of Deep Learning (DL) and discuss some recent successful applications in medical imaging. One aim is to draw connections between DL methods such as convolutional neural network (CNN), convolutional encoder-decoder (CED), cycle-consistent adversarial neural network (Cycle-GAN) and medical applications including image reconstruction, multi-modality image synthesis and image analysis. Dr. Liu will present some of his recent work using DL for medical imaging applications and will discuss relevant DL methods and their strengths and limitations. The talk will conclude with a discussion of open problems in DL that are particularly relevant in medical imaging and the potential challenges of DL in this emerging field.

  • February 15, 2018, at noon

    Title: RF/Mixed-Signal Circuits and Systems toward Next-Generation MRI

    Sung-Min Sohn

    Assistant Professor
    Department of Radiology
    University of Minnesota Medical School


    Magnetic Resonance Imaging (MRI) is one of the most state-of-the-art technologies to non-invasively acquire structural, functional, and biochemical information in the human body. Dr. Sohn will present his research topics to overcome technical barriers to increase the accessibility of MRI and improve the quality of MR imaging for next-generation MRI that realizes ultra-low RF power, miniaturization, lightweight, low cost, and safety. Most of his researches are related to the oscillating field (B1) of RF coils and interface circuits between RF coils and RF signal chains in transmit (Tx) and receive (Rx). Especially, he is focusing on the development of RF/mixed-signal circuits for simultaneous transmit and receive (STAR) and automatic correction systems of frequency tuning, impedance matching, and RF coupling as well as novel RF coil structures. His research results show the ultra-low RF peak power capability and replacement of manual adjustments to obtain human MR images. These RF hardware-engineering approaches can contribute to a wide variety of MRI researches and industries.

  • February 5, 2018, at noon

    Making Every Photon Count: Photon Counting and Dose/Iodine Reduction at Mayo Clinic

    J.G. Fletcher, MD

    Consultant, Department of Radiology
    Professor of Radiology
    Medical Director of the CT Innovation Center
    Mayo Clinic

    No abstract was provided for this talk.

  • January 23, 2018, at noon

    Outcomes Research for Imaging: Theory and Design

    Stella Kang, MD

    Assistant Professor
    Department of Radiology
    NYU Langone Health


    Medical imaging has been targeted as a source of inordinate — and sometimes unnecessary — health care spending. Cross-sectional imaging use has increased markedly over recent decades, and yet the contribution of imaging to overall care has not been well characterized. Rather than parsimony, the goal of our system is to improve evidence-based clinical practice so that the right patients receive the right interventions. Outcomes research is necessary to drive this effort. In this talk, I will discuss the ways in which outcomes research can underscore the value of imaging, and review study designs that explore the best uses of imaging tests. MRI techniques that may perform better than available tests or offer similar performance at lower costs can be evaluated using intermediate health outcomes. Meanwhile, techniques for which larger, prospective studies are available can be evaluated for population-level benefits and harms. Finally, I will discuss some of the ongoing efforts at NYU to bridge the use of MRI to patient health outcomes and decision-making.

  • January 16, 2018, at noon

    PET and MR Imaging in Management of Medically Refractory Epilepsy

    Udunna Anazodo, PhD

    PET/MR Physicist, Lawson Health Research Institute
    St Joseph’s Healthcare, London, Ontario

    Assistant Professor, Department of Medical Biophysics
    Schulich School of Medicine and Dentistry
    Western University, London, Ontario, Canada


    Patients with epilepsy uncontrolled by medications are potential candidates for epilepsy surgery. Surgical removal of an epileptic lesion can lead to alleviation or elimination of seizures. Majority of epileptic lesion(s) can be detected as structural abnormalities on anatomical (1.5T) MRI scans. However, anatomical MRI scans in a significant proportion of medically refractory epilepsies can be ambiguous or negative. In these non-lesional patients, PET-FDG is indicated for detection of the epileptic focus. Recent technological advances in medical imaging have led to the development of hybrid PET/MRI scanners which combine the two versatile imaging modalities in one scanner. It is predicted that PET/MRI will allow higher rates of lesion localization in medically refractory epilepsies, leading to improved surgical outcomes. In Ontario, access to PET/MRI scanners have improved from one scanner in 2012 to four scanners by the end of 2018. In this talk, I will share experiences from the Epilepsy Imaging Program at London Health Sciences Center in establishing indications for the use of PET/MRI in clinical management of medically refractory epilepsy in Ontario. In addition, I will briefly discuss some of the technical developments in advanced MRI (7T, BOLD-fMRI, DTI) that are implemented in London for clinical epilepsy imaging.

To the top ↑

2017 Lectures

  • December 21, 2017, at noon

    Developing New Quantitative Imaging Markers to Assist Cancer Risk and Prognosis Assessment

    Bin Zheng, PhD

    School of Electrical and Computer Engineering and Stephenson Cancer Center
    University of Oklahoma


    Developing precision medicine requires accurate prediction markers and/or models to identify the personalized disease (e.g., cancer) risk and prognosis or response to the different treatment. Radiographic medical imaging is widely used in clinical practice and carries much useful information to phenotype disease risk and prognosis. However, how to reliably and quantitatively extract and compute the useful image features, which can be used to develop new and highly performed clinical prediction models remain a very challenged and hot research topic in the biomedical imaging and informatics field. In this presentation, I will discuss the general concept of applying the quantitative image feature analysis in this research field and report several research work recently conducted in our laboratory to identify new quantitative imaging markers and apply machine learning technology to develop new prediction models, which include (1) using a new imaging marker based on the bilateral mammographic density asymmetry computed from the negative mammograms to predict risk of cancer detection in the next subsequent mammography screening; (2) extracting image features from breast MR images to predict complete response (CR) of breast tumors to the neoadjuvant chemotherapy; (3) using tumor density heterogeneity features computed from lung CT images to build a prediction model to assess lung cancer recurrence risk after surgery; and (4) using image features computed from abdominal CT images to predict response of ovarian cancer patients to chemotherapy at the early stage of the clinical trials.

  • December 19, 2017 at noon

    Applying New Magnetic Resonance Concepts and Techniques to Human Scanning

    Andrew Webb, PhD

    Professor, Director C.J.Gorter Center for High Field MRI
    Leiden University Medical Center


    This talk will describe recent developments in several areas of magnetic resonance hardware and sequences which have been applied to clinical research and patient scanning at field strengths between 1.5 and 7 Tesla. Topics will include the design of very high permittivity materials/metamaterials for improved magnetic field homogeneity and lower power deposition, new ceramic-based resonators for multi-element transmit arrays, methods for the rapid non-invasive estimation of tissue conductivity, high resolution motion-free imaging of the eye, and whole-body optical-based measurement of temperature changes. Clinical applications include studies of patients with eye tumours, epilepsy, early-onset Alzheimers as well as muscular and neuromuscular dystrophies.

  • December 15, 2017, at noon

    Tipping points in network performance: Phase transitions in machine learning and distributed control

    Partha P. Mitra, PhD

    Professor at Cold Spring Harbor Laboratory
    Cold Spring Harbor, NY


    In 2016, it is estimated that internet IP traffic reached 10^21 bits – within striking distance of the Avogadro number. Given that data sizes are reaching thermodynamic proportions, and that relevant calculations have often to be performed in a distributed manner, it can be expected that phenomena and methods from the statistical physics of many particle systems are relevant.

    This talk will examine a couple of examples where phase-transition like phenomena occur, with network performance going from a “good” to a “bad” phase sharply as a function of a relevant global parameter. The examples include the so called network consensus problem, and feature selection in multivariate regression using an L1 norm.

  • December 14, 2017, at 11:00 a.m.

    Learning-Inspired Quantitative MRI: Acquisition, Estimation, and Application

    Gopal Nataraj

    PhD Candidate
    University of Michigan, Ann Arbor


    In quantitative MRI (QMRI), one seeks to accurately and rapidly localize biomarkers (i.e., measurable tissue properties) using MR data. One key challenge of QMRI is that ‘accurate’ and ‘rapid’ are often competing goals: more physically accurate MR signal models typically depend on more biomarkers, but estimating more markers usually requires longer acquisitions and greater computation. In this talk, I will discuss two recently developed methods to systematically limit these QMRI resource burdens. First, I will describe a method to assemble fast, statistically informative acquisitions that enable min-max optimally precise biomarker estimation. Second, I will describe a machine-learning inspired method to “learn” an extremely fast and scalable biomarker estimator from purely simulated training data. Finally, I will describe our ongoing efforts to apply these methods for fast, accurate myelin water fraction imaging. This talk discusses joint works with Prof. Jeffrey Fessler, Dr. Jon-Fredrik Nielsen, and Prof. Clayton Scott, all at the University of Michigan.

  • December 12, 2017, at noon

    Seminar: Deep Learning and Generative Adversarial Network for improved MRI Reconstruction

    Enhao Gong

    PhD Candidate in Electrical Engineering
    Stanford University


    Compressed sensing (CS) MRI enables fast imaging. Conventional CS MRI reconstruction algorithms are time-consuming and often lead in undesired over-smoothing or artifacts. Recently, various methods have been proposed to apply deep learning models for more efficient and accurate MRI reconstruction. However, there are still open question on how to ensure realistic and consistent Deep Reconstruction. In this talk, a MRI reconstruction technique using deep learning and generative adversarial network (GAN) is introduced. Evaluated on clinical MRI datasets with both quantitative metrics and radiologists’ ratings, the proposed method demonstrates superior performance compared with conventional iterative reconstruction and Deep Learning models trained with pixel-wise loss. Similar deep learning models can also be applied for PET reconstruction and quantitative MRI.

  • December 6, 2017, at noon

    Respiratory and cardiac PET/MR motion correction for the application in clinical practice

    Thomas Küstner

    Universität Stuttgart, Germany

    No abstract was provided for this talk.

  • December 5, 2017, at noon

    Multidimensional diffusion MRI: unraveling new features of microstructure by clever gradient waveform design

    Filip Szczepankiewicz, PhD

    Chief Research Coordinator and Technical Specialist
    Random Walk Imaging (RWI)


    Evidence that conventional (linear) diffusion encoding is not enough to probe all relevant features of microstructure has accumulated for 20 years. Recent developments have seen the canonical Stejskal-Tanner experiment complemented with techniques that all contribute more specific information about the underlying structure. The lecture will survey several methods based on diffusion encoding with non-conventional gradient waveforms, and what microstructural features that they can resolve.

  • November 14, 2017, at 12:30 p.m.

    Part II: Toward a universal decoder of linguistic meaning from brain activation

    Bin Lou, PhD

    Senior research Scientist
    Siemens Healthcare Technology Center
    Medical Imaging Technologies
    Siemens Medical Solutions USA, Inc.
    Siemens Healthineers
    Princeton, New Jersey


    Technology leaders have recently announced the goal of translating thoughts into text directly from brain recordings. Existing work on decoding linguistic meaning from imaging data has been largely limited to concrete nouns, and trained and tested with similar stimuli from a few semantic categories. I will present a new approach for building a brain decoding system, based on a procedure for broadly sampling a semantic space constructed from massive text corpora. By efficiently selecting training stimuli shown to subjects, we ensure the ability to generalize to new meanings from limited imaging data. To validate this approach, we trained the system on imaging data of individual concepts, and showed it can decode imaging data of sentences from a wide variety of concrete and abstract topics in two separate datasets.

  • November 14, 2017, at noon

    Part I: Overview of research activities at Siemens Medical Imaging Technologies in Princeton

    Carol L. Novak, PhD

    Siemens Healthcare Technology Center
    Medical Imaging Technologies
    Siemens Medical Solutions USA, Inc.
    Siemens Healthineers
    Princeton, New Jersey


    Brief overview of the current research activities at Siemens Healthcare Technology Center, Medical Imaging Technologies. Located in Princeton, NJ, we are the central research and development lab of Siemens Healthineers. Our team of over 80 research scientists and software engineers specializes in using large collections of data to build artificial intelligence solutions for healthcare. We also work closely with Siemens’ customers in submitting grant proposals to government funding agencies. Our research has resulted in multiple scientific contributions in the fields of medical imaging, modeling, and image-guided therapy and has been incorporated into many clinical products.

  • November 10, 2017, at noon

    Idealized Axon Phantom for Validation & Calibration of dMRI: Testing Compartmental Models and Fiber Tractography

    Sudhir Pathak, PhD
    Walter Schneider, PhD

    University of Pittsburgh


    The advancement of diffusion MR imaging (dMRI) acquisition, post-processing, and clinical diagnostic precision would be accelerated with a cross-laboratory anisotropic diffusion phantom providing paramedic control of shape geometry, packing density and routing. Our group is developing such a phantom matched to histology geometry on a 1 to 1 scale. We have created idealized axons (iAxons) that are textile-based hollow fibers at nanometer scale. They provide controlled geometrical configurations and packing density patterns. The iAxons have a diameter range from 0.2 to 36 microns filled with water covering and exceeding the biological range allowing parametric tests of dMRI precision. We create Standard iAxon Fasciculi (SIF) that contains 950-nanometer internal diameter water filled tubes with a density of a million per mm2. We can create cortical networks such as the eye to LGN of millions of iAxons with precise 50 micron routing positional control. We use non-MRI measurement with Micro CT, light, and electron microscope imaging of iAxons to to quantify dMRI precision. We are creating matched histology and phantoms for pig harvested and human cadaver tissue. We are testing bio-physical models like NODDI or spherical mean techniques (SMT) for packing density pattern and amount of iAxons. We have found the intra-cellular volume fraction correlates with a number of iAxons (r = 0.96). For geometrical configuration, we have tested Constrained Spherical Deconvolution techniques which show promising results to resolve more than 45-degree crossing. We will also present the effect of small/big delta on diffusivities at multiple packing densities of the iAxon bundle. We plan to provide phantoms across laboratories and release public data sets to drive MRI-based quantitative calibration and discovery of improved techniques. We have done cross instrument measurement and found large systematic errors in measurement (35%) across instruments at five sites. We are developing correction methods for clinical scanners. We expect the phantoms to provide a set of ground truth challenges to advance MRI diffusion physics and tractography.

  • October 31, 2017, at noon

    Past, Present and Future of MR-guided Focused Ultrasound

    Yoav Medan, PhD

    Science and Technology Explorer
    Focused Ultrasound Foundation


    Focused Ultrasound is a novel treatment modality that displaces (minimally) invasive surgery with a totally non-invasive approach using a focused beam of ultrasound energy. Depending on the parameters used, the effect at the focal point can be purely mechanical, thermal or a combination thereof. Coupled with real-time feedback of MRI enables to accomplish a spatio-thermal closed-loop procedure, which may lend itself to automation.

    In my talk I will review the history of MRgFUS, the current clinical indications it is being used for and some new emerging applications. I will also describe the role of the Focused Ultrasound Foundation, a non-profit aimed at accelerating clinical adoption, in how NYU may benefit from research grants provided by the Foundation.

  • October 27, 2017, at 9:00 a.m.

    Principles and progress in spatiotemporally encoded MRI

    Prof. Lucio Frydman

    Director, The Helen and Martin Kimmel Institute in Magnetic Resonance
    The Bertha and Isadore Gudelsky Professorial Chair
    Head, Department of Chemical and Biological Physics
    Weizmann Institute, Israel

    No abstract was provided for this talk.

  • December 17, 2017, at noon

    I: Peptide-based Molecular Imaging Probes
    II: Examining the structural variations in T1, T2 and ParaCEST MRI contrast agents

    Len Luyt, PhD

    Associate Professor
    University of Western Ontario, Canada

    Dr. Mark Milne

    Research Associate
    Lawson Health Research Institute

    No abstract was provided for this talk.

  • October 16, 2017, at noon

    Diffusive and Perfusive Effects in SPatio-temporal ENcoding (SPEN) Nuclear Magnetic Resonance Imaging

    Eddy Solomon, PhD

    Postdoctoral Fellow
    Weizmann Institute of Science, Israel

    No abstract was provided for this talk.

  • October 13, 2017, at noon

    Quantitative MRI of the spinal cord: challenges, feasibility and future perspectives

    Francesco Grussu, PhD

    University College London


    Quantitative Magnetic Resonance Imaging (qMRI) enables the non-invasive measurement of microstructural properties of living tissue, thus providing useful imaging biomarkers with strong clinical potential. In practice, while qMRI is rather popular and successful in the brain, qMRI of the spinal cord is more difficult due its proneness to noise, field inhomogeneity and phyisological artifacts, which hamper the clinical translation of most qMRI methods. In this talk, I will provide an overview of spinal cord qMRI and illustrate its challenges and report on recent developments. In particular, the talk will focus on recent spinal cord qMRI approaches for neuronal morphology and myelin measurement, which hold promise for more accurate diagnosis and prognosis in conditions such as multiple sclerosis.

  • October 10, 2017, at noon

    Multi-Parametric PET/CT and PET/MR Molecular Imaging: Towards Enhanced Quantification and Diagnosis in the Clinic

    Nicolas A. Karakatsanis, PhD, DABSNM

    Assistant Professor of Biomedical Engineering
    Department of Radiology, Weill Cornell Medicine, New York, NY


    Positron Emission Tomography (PET) has been nowadays established as a molecular imaging modality capable of providing non-invasive, diagnostic and treatment response assessments of the activity of specific molecular processes underlying a spectrum of oncologic, cardiovascular and neurologic diseases. In the first part of this talk we will introduce a clinically adoptable WB dynamic 18F-FDG PET/CT scan protocol coupled with a family of robust direct 4D PET image reconstruction methods to enable for the first time WB multi-parametric PET imaging in humans. The presented framework exploits current state-of-the-art clinical PET systems technologies, such as Time-of-Flight and Resolution modeling, to also support combined WB static and parametric PET imaging from only the standard-of-care scan time window to deliver to clinic additional and highly quantitative information content beyond the standardized uptake value (SUV) metric. Later in the talk, we will also present a novel dual-tracer 18F-FDG:18F-NaF PET/MR imaging framework designed to improve PET attenuation correction in PET/MR studies by robustly segmenting the bone tissues from the 18F-NaF kinetic analysis. Finally, we will demonstrate a clinically adoptable dual-tracer dual-modality imaging protocol for the simultaneous and co-registered anatomical and molecular assessment of both inflammation and micro-calcification, two major molecular mechanisms considered to be associated with atherosclerosis, in human carotid vessel walls.

  • October 3, 2017, at noon

    Stress and Atherosclerotic Plaque Macrophages—A Systems Biology Approach

    Zahi A. Fayad, MD

    Vice Chair for Research, Department of Radiology
    Professor of Radiology and Medicine (Cardiology)
    Director, Translational and Molecular Imaging Institute
    Director, Cardiovascular Imaging
    Icahn School of Medicine at Mount Sinai, New York, NY


    Chronic social stress is an integral part of our busy contemporary lives. Abundant data show that severe chronic psychosocial stress is a risk factor for cardiovascular disease and a predictor of myocardial infarction and stroke. The mechanisms by which stress contributes to the higher cardiovascular event rates are primarily attributed to secondary effects on behavior, including smoking or food intake. How stress’ effect on the brain can directly impact cardiovascular disease is uncharted territory.

    Preclinical data describe a direct causal link between social stress, neural signals, and atherosclerosis, the lipid-driven chronic inflammatory disease that is the underlying cause of myocardial infarction and stroke. The key connecting component is the macrophage, a large phagocytic leukocyte that originates in the bone marrow and accumulates in atherosclerotic lesions. Informed by abundant published and unpublished data, we hypothesize that chronic variable stress aggravates cardiovascular disease by interfering with macrophage dynamics.

    Specifically, we wish to (1) understand how stress biologically affects macrophage dynamics in atherosclerosis; (2) develop technology that monitors macrophage dynamics non-invasively; and (3) elucidate the mechanism by which post-traumatic stress disorder (PTSD) leads to atherosclerosis.

    This work is based on technological developments (such as motion compensation and fast imaging) in biomedical imaging and systems imaging using PET/MR and using novel targeted approaches (such as molecular imaging and nanomedicine) to study and treatment of inflammation in preclinical and clinical studies. I will describe our overarching and long-term goal is to collectively institute a sound scientific foundation for the biomedical and clinical community as how the link between stress and cardiovascular disease can be best approached and integrated in patient care.


    1. “Systems biology and noninvasive imaging of atherosclerosis.” Arteriosclerosis, Thrombosis, and Vascular Biology. 2016; 36:e1-e8. doi 10.1161/ATVBAHA.115.306350
    2. “Relation between resting amygdalar activity and cardiovascular events: a longitudinal and cohort study.” Lancet. 2017; 389: 834-845. doi 10.1016/S0140-6736(16)31714-7
    3. “Imaging systemic inflammatory networks in ischemic heart disease.” Journal of the American College of Radiology. 2015; 65: 1583-1591. doi 10.1016/j.jacc.2015.02.034

  • September 26, 2017, at noon

    Directed functional pathways of information flow in the visual and motor systems

    Gadi Goelman, PhD

    Hadassah Medical Center
    The Hebrew University of Jerusalem


    I will introduce a novel analytical method based on high order statistics and nonlinear coherences that enables to obtain directed pathways of signal progression among coupled time-series. Assuming a consistent phase relationship between neuronal and MRI signals, the method is demonstrated in the human brain with resting-state fMRI data. Pathways in the visual and the motor systems were characterized by appealing to a hierarchy based upon temporal or phase differences. I will describe the different organizations of the ventral and dorsal visual systems, the frequency dependency of the thalamo-cortical connections and how it changes with age.

  • September 19, 2017, at noon

    Hexa-modal integrated sub-mm PET-SPECT, sub-quarter mm SPECT, high performance X-ray CT and Optical imaging

    Prof. dr. Freek J. Beekman

    Section Leader, Delft University of Technology, Radboud University Nijmegen, Netherlands
    Founder & CEO MILabs


    In biomedical preclinical research we have dreamt about a magnifying glass that would allow us to e.g. see neurotransmitters in action, that would simultaneously quantify mechanical function, perfusion and various local cell functions in the heart, and in cancer research for (simultaneous) detailed dynamic distributions of pharmaceuticals and indicators of tumor response. In recent years many groups have been involved in the development of pinhole imaging SPECT systems for imaging rodents.

    At MILabs and TU-Delft, a ultra-high resolution Single Photon Emission Computed Tomography (U-SPECT-CT) has been developed that can quantify tracers in 0.15 mm structures, enable low dose imaging (sub-MBq), or visualize extremely fast tracer dynamics (sub-second time frames) by developing highly advanced imaging geometries, novel image acquisition and reconstruction. An option on this system to perform 0.6 mm Positron Emission Tomography (PET) simultaneous with 0.4mm SPECT (VECTorTM) was developed. It also enables for the first time ultra-high energy SPECT (up to 1MeV) and imaging of sub-mm resolution of theranostic isotopes to real time monitor and steer cancer therapy.

    In this presentation, scientific results recorded by worldwide users of a full integrated platform combining SPECT, PET, ultra-fast and ultra-high resolution CT, Cherenkov, bioluminescence and fluorescence imaging will be discussed. Finally the results of translating U-SPECT technology into a clinical device (G-SPECT: WMIS Innovation of the Year 2015), an Ultra-fast, Ultra-high resolution (< 3 mm resolution) will be presented.

  • September 8, 2017, at noon

    Multidimensional diffusion MRI

    Daniel Topgaard, PhD

    Division of Physical Chemistry
    Lund University, Sweden


    Diffusion MRI is an excellent method for detecting microscopic changes of the living human brain, but often fails in assigning the observed changes to a specific structural property such as cell density, size, shape, or orientation. When attempting to solve this problem, we have decided to simply ignore the entire field of diffusion MRI, and instead translate data acquisition and processing schemes from multidimensional solid-state NMR spectroscopy. Key elements of our approach are q-vector trajectories and correlations between isotropic and directional diffusion encoding. To emphasize the origin of the new methods, we have selected the name “Multidimensional diffusion MRI.” Assuming that the water molecules within a voxel can be divided into groups exhibiting approximately Gaussian anisotropic diffusion, the composition of the voxel can be reported as a diffusion tensor distribution where each component of the distribution is directly related to a specific tissue environment. Our new methods yield estimates of the complete diffusion tensor distribution or well-defined statistical properties thereof, such as the mean and variance of isotropic diffusivities, mean-square anisotropy, and orientational order parameter, which are straight-forwardly related to cell densities, shapes, and orientations. This presentation will give an overview of the multidimensional diffusion MRI methods, including basic physical principles, pulse sequences, data processing, and examples of applications in healthy and diseased brain.

  • September 5, 2017, at noon

    White matter and core neurocognitive deficits in schizophrenia

    Peter Kochunov, PhD

    Professor of Psychiatry
    University of Maryland School of Medicine


    Disconnections of cortical networks may underlie various cognitive deficits that take severe clinical tolls on patients with schizophrenia. Historically, the neuropsychopharmacology of cognitive deficits is mostly conceptualized and studied in terms of neurons, neurotransmitters and synaptic receptors. We hypothesized that the dynamics of the extended lifetime development trajectory of the brain’s white matter, and the consistency of connectivity deficits in schizophrenia, posit white matter as the key loci responsible for these cognitive deficits. Using novel diffusion weighted imaging (DWI) techniques and a milestone development of identifying key white matter tracks most relevant to schizophrenia, we are now able to show that specific white matter pathways are responsible for shared vs. unique contributions to some of the key cognitive deficits in schizophrenia.

  • August 16, 2017, at 10:00 a.m.

    Cardiac MRI in the era of compressed sensing and machine learning

    Davide Piccini, PhD

    Advanced Clinical Imaging Technology
    Siemens Healthineers, Lausanne, Switzerland

    No abstract was provided for this talk.

  • August 8, 2017, at noon

    Hyperpolarized Carbon-13 for Imaging of Perfusion and Metabolism

    Aaron K. Grant, PhD

    Department of Radiology, Beth Israel Deaconess Medical Center, Boston, MA


    Hyperpolarized contrast media prepared via dissolution dynamic nuclear polarization or parahydrogen-induced polarization provide tremendous in vivo signal enhancements for dilute tracer molecules labeled with nuclei such as 13C or 15N. These signal enhancements provide a tool for monitoring tissue function and metabolism, particularly in cancer and cardiac disease. In pre-clinical models of lung, prostate and breast cancer, hyperpolarized pyruvate can detect tumor response to therapy within hours of the onset of treatment, potentially providing a new tool for personalized medicine by rapidly identifying the best therapy for each patient. Clinical translation of hyperpolarized imaging will require new approaches to MR spectroscopic imaging. Spectroscopically selective balanced steady-state techniques offer improved sensitivity and speed relative to conventional echo-planar spectroscopic methods that can be leveraged for imaging in patients.

  • August 3, 2017, at noon

    Magnetic Resonance in the GP’s Clinic: A vision of low field NMR for medical screening and diagnosis

    Petrik Galvosas

    MacDiarmid Institute for Advanced Materials and Nanotechnology, SCPS
    Victoria University of Wellington, New Zealand


    Nuclear Magnetic Resonance (NMR) and Magnetic Resonance Imaging (MRI) is common in medical research and widely used for medical diagnosis. However, NMR and MRI systems are expensive to install and cause substantial maintenance costs. Its use is often restricted to radiology centres or hospitals in larger cities. Here we report on recent research which may help to turn inexpensive, mobile low field NMR systems into medical devices. One challenge in low field NMR is the magnetic field inhomogeneity. It introduces a distribution of Larmor frequencies and magnetic field gradients. However, field distributions can be determined (see Fig. 1 left) and may be corrected for, thus enabling these magnet systems for the use in NMR diffusometry [1]. Another challenge is the reduced signal-to-noise ratio at lower magnetic fields. Therefore, conventional imaging approaches may not be feasible. We have shown that the sample averaged fractional anisotropy (FA) can be determined without the use of imaging [2]. However, if imaging is needed, the amount of acquired data may be reduced dramatically using prior knowledge [3]. More recently we have also demonstrated that single sided NMR systems such as the NMR MOUSE [4] can be used (see Fig. 1 right) for the determination of the total volume-to-bone volume ratio, a parameter linked to the micro structure of bones and therefore to the risk factor for osteoporosis [5]. We anticipate the use of mobile low field NMR systems as diagnosis and screening tools, affordable for general practitioners as well as mobile point-of-care medical devices on the bedside, in ambulances, operational theatres and ICU’s.

  • August 1, 2017, at noon

    The Potential of MR Molecular Imaging for Investigation and Evaluation of Immunotherapies

    Kimberly Brewer, PhD

    Research Scientist, Biomedical Translational Imaging Centre (BIOTIC), IWK Health Centre and QEII Health Sciences Centre
    Assistant Professor, Departments of Diagnostic Radiology, Physics and Atmospheric Science, Microbiology and Immunology
    School of Biomedical Engineering, Dalhousie University, Halifax, Nova Scotia, Canada


    Immunotherapies are becoming increasingly important for improved treatment of a variety of cancer types. However, the development of these novel therapies has outstripped our understanding of underlying mechanisms and how best to apply them. It is therefore crucial that we use tools such as MRI, and other molecular imaging techniques, to evaluate immunotherapies in both the clinic and in preclinical studies, and develop new probes and biomarkers to increase their efficacy. Studies have shown high degrees of variability in individual response to cancer, increasing the necessity of a more personalized approach, and optimized methods for combinations of multiple therapies are not well understood.

    This talk will touch on a number of molecular imaging methods used to study immunotherapy response, including the use of MRI cell tracking for monitoring both adoptive cell immunotherapies, and immune cell population migration in response to other immunotherapy subtypes. Other techniques to be discussed include use of PET (using standard FDG imaging, and novel probes specifically developed for immunotherapies) and PET/MRI multimodal imaging for monitoring both anatomical and functional changes with MRI (using DCE, T1/T2-weighted imaging, etc.)

  • July 25, 2017, at noon

    Mechanisms of Primary Resistance to Cancer Immunotherapies

    Michelle Krogsgaard, PhD

    Associate Professor of Pathology
    Department of Pathology, Perlmutter Cancer Center
    NYU School of Medicine


    Although much clinical progress has been made in harnessing the immune system to recognize and target cancer, there is still a significant lack of an understanding of how tumors evade immune recognition and the mechanisms that drive tumor resistance to both T cell and checkpoint blockade immunotherapy. Our objective is to understand how tumor-mediated signaling through inhibitory receptors, including PD-1, combine to affect the process of T cell recognition of tumor antigen and activation signaling, with the goal of understanding the basis of resistance to PD-1 blockade and the potential identification of new molecular targets to enable T cells to overcome dysfunction mediated by multiple inhibitory receptors. Potential combinatorial immunotherapeutic strategies of combining T-cell therapy strategies with checkpoint blockade will also be discussed.

  • July 20, 2017, at noon

    Pushing the limits of spectroscopic imaging using low-rank based reconstruction

    Ipshita Bhattacharya

    Computational Biomedical Imaging Group
    University of Iowa

    No abstract was provided for this talk.

  • July 19, 2017, at noon

    On the Detection of High Frequency Connectivity and Development of Presurgical Mapping using High-Speed Resting State fMRI

    Stefan Posse, Dr. Phil. Nat. Habil.

    Professor of Neurology
    Professor of Electrical and Computer Engineering
    Professor of Physics and Astronomy
    University of New Mexico


    Functional connectomics using resting state fMRI (rsfMRI) is a rapidly expanding task-free approach, which has the potential to complement task-based fMRI for presurgical mapping in patients with neurological disease. However, high sensitivity to head movement and physiological noise, the low frequency range of rsfMRI (< 0.2 Hz), and considerable spatial-temporal non-stationarity compromise mapping of resting state networks (RSNs).

    Recently, several studies using volumetric and multi-band high-speed fMRI have reported resting state connectivity at much higher frequencies (up to 5 Hz). This approach has the potential for addressing principal limitations of mapping low frequency resting state connectivity, such as high sensitivity to signal drifts and long scan time necessary for separating major RSNs in single subjects. However, other studies have been more cautious regarding the possible signal sources or were unable to replicate the findings.

    The first part of this talk will discuss recently developed ultra-high speed fMRI and confound-tolerant seed-based resting-state fMRI analysis methodology that enabled sensitive detection of high frequency signal fluctuations in auditory cortex and default mode network. Experimental findings were validated by analyzing non-physiological signal sources using simulations of auto-correlations in Rician image noise. The second part of the talk will describe initial experience using high-speed resting state fMRI for presurgical mapping in patients with brain tumors, arteriovenous malformation and epilepsy, and integration of this approach with multi-modal diagnostic imaging.

  • July 18, 2017, at noon

    Advances in Rapid MRI: Magnetic Resonance Fingerprinting and Real-Time Imaging of the Heart and Abdomen

    Nicole Seiberlich, PhD

    Associate Professor
    Department of Biomedical Engineering
    Case Western Reserve University


    The focus of the Seiberlich Lab is to develop MR imaging techniques to capture structural and functional information from moving organs, specifically in the abdomen and heart. This lecture will cover the recent developments using Magnetic Resonance Fingerprinting for quantitative tissue property mapping of the myocardium. Additionally, work on real-time cardiac and abdominal imaging using non-Cartesian parallel imaging techniques in conjunction with Gadgetron will be discussed.

  • June 29, 2017, at 10:00 a.m.

    Visualizing the brain at 7 Tesla: Technical Developments and Clinical Applications

    Priti Balchandani, PhD

    Assistant Professor, Radiology and Neuroscience
    Translational and Molecular Imaging Institute
    Icahn School of Medicine at Mount Sinai


    This talk will cover some novel radio frequency pulse and pulse sequence designs to overcome some of the main limitations of operating at high magnetic fields, thereby enabling high-resolution whole-brain anatomical, spectroscopic and diffusion imaging. Translation of these techniques to improve diagnosis, treatment and surgical planning for a range of neurological diseases and disorders will be discussed. Specific clinical applications that will be covered include: Improved localization of epileptogenic foci; imaging to reveal the neurobiology of depression; and development of imaging methods to better guide neurosurgical resection of brain tumors.

  • June 27, 2017, at noon

    Optogenetic fMRI Dissection of Long-Range Brain Networks

    Ed X. Wu, PhD

    Lam Woo Professor and Chair of Biomedical Engineering
    University of Hong Kong


    Functional MRI (fMRI) provides the most versatile imaging platform for mapping the brain activities in vivo. More recently, resting-state fMRI (rsfMRI) has emerged as a valuable tool for mapping large-scale and long-range brain networks. However, both methods only reflect the gross outcome of the complex and cascaded activities of various cell types and networks, posing limitations when dissecting brain networks. Optogenetics technology can provide spatiotemporally precise modulation of genetically defined neuronal populations in vivo. Here we combine fMRI with optogenetic perturbations and electrophysiology to capture and analyze whole brain activity and long-range circuits with much improved specificity and causality. We deploy this capability to interrogate the spatiotemporal response properties of two distinct long-range networks, namely, thalamo-cortical and hippocampal-cortical networks. We examine the functional effects of low-frequency optogenetic stimulation within these two networks on brain responses to external sensory stimuli, on brain-wide functional connectivity at resting-state, and on cognitive behaviors. Our findings reveal that low frequency activity governs large-scale, brain-wide connectivity and interactions through long-range excitatory projections to coordinate the functional integration of remote brain regions. This low frequency phenomenon contributes to the neural basis of long-range functional connectivity as measured by rsfMRI. I this talk, I will also briefly introduce our recent diffusion MRI works in brain and MSK, including diffusion MR spectroscopy.

  • June 16, 2017, at 12:45 p.m.

    Functional testicular evaluation using PET/CT with 18F FDG?

    Laurence Dierickx, MD

    Institut Claudius Regaud
    Service de médicine nucléaire
    Toulouse, France


    The aim of this presentation is to evaluate the use of PET/CT with 18F-FDG for an assessment of the testicular function and to optimise and standardise the acquisition protocol and the testicular volume analysis in order to do that. By ways of introduction there will be a literature overview where we establish why the 18F-FDG uptake is correlated with the spermatogenesis. There will follow an overview of the public health problem of male infertility where the different possible clinical applications for testicular functional imaging with PET/CT will be addressed.

    In the second part of the talk we’ll discuss the correlation between 18F-FDG uptake in terms of intensity and volume of uptake and the testicular function via the parameters of the sperm analysis based on the published article of our group.

    The third part of the presentation will be on the subject of some of the technical issues where the focus will be on the standardisation of the acquisition protocol for this specific indication. In the last part of the presentation, we’ll address the overall important subject, and even more so in this andrological context, of the radioprotection related issues of a PET/CT with 18F-FDG.

    Finally, there’ll be an overview of some of the issues still to be addressed and the future perspectives.

  • June 12, 2017, at noon

    Pulmonary MRI—what’s new in context of multimodality approaches

    Edwin J.R. van Beek MD PhD MEd FRCPE FRCR

    SINAPSE Chair of Clinical Radiology
    University of Edinburgh

    No abstract was provided for this talk.

  • May 30, 2017, at noon

    X-ray scattering for investigating tissue microstructure: Collagen directionality in bone, neuronal directionality and myelin content in brain, and comparison with MRI, histology and CLARITY

    Marios Georgiadis, PhD

    Post-doctoral Fellow
    NYU School of Medicine


    Small-angle X-ray scattering (SAXS) occurs when part of the X-ray beam that probes a sample is scattered at small angles, due to differences in electron density distributions within the sample. Moreover, it gives a particularly strong signal in the presence of ordered and periodic systems. The recently developed small-angle X-ray scattering tensor tomography (SAXSTT) takes X-ray tomography a step further: it uses two sample rotation axes and an iterative reconstruction algorithm to tomographically reconstruct local tissue anisotropy. The method was demonstrated for reconstructing the orientation of mineralized collagen fibrils in bone trabeculae of human vertebrae, based on the 65-nm D-spacing of collagen. Similar experiments have also very recently been performed in mouse brain, taking advantage of the ~17.5 nm spacing of the myelin sheath. Providing directly structural information, SASTT was used to derive neuronal fiber directionality and myelin content in a quantitative way. The results are being compared with MRI methods such as diffusion-weighted imaging and magnetization transfer, as well as with 2D and 3D histology (CLARITY).

  • May 25, 2017, at 10:00 a.m.

    MRI of Ultra-fast relaxing spins for PET/MRI, Lung imaging, and Myelin Imaging

    Peder Larson, PhD

    Associate Professor, Principal Investagor
    University of California, San Francisco


    MRI has historically performed poorly when imaging ultra-fast relaxing tissues such as bone, lung tissue, and tendons as well as components of other connective tissues including cartilage and myelin. Specialized pulse sequences such as ultrashort echo time (UTE) and zero echo time (ZTE) MRI offer the potential to image these tissues, and have several promising new applications that will broaden the capability of MRI. These include

    1. PET/MRI – Hybrid PET/MRI systems require attenuation correction for accurate PET reconstructions, which should include estimates of bone density. This talk will present work using ZTE MRI for generating pseudo/synthetic CT images that include bone density estimates in the head and pelvis. Most recently, we have applied Deep Learning for this synthetic image generation task.
    2. Lung Imaging – Pulmonary MRI has been very challenging due to the short T2* of lung parenchyma and motion, but is important for assessing pulmonary nodules in PET/MRI and for dose reduction in pediatric populations. This talk will present an approach using UTE MRI, where self-navigation is achieved through a local low-rank reconstruction of dynamic 3D image navigators and motion-corrected images are reconstructed similarly to XD-GRASP.
    3. Myelin Imaging – Myelin facilitates crucial long-range communication across the brain, and is typically assessed in MRI through diffusion-weighting, magnetization transfer, and myelin water imaging. It has been shown through ex vivo studies that there are fast relaxing components in myelin associated with protons in the myelin phospholipid membranes, which are not captured in these conventional approaches. This talk will present in vivo characterizations of the ultrashort-T2* components in the brain that have the potential to provide a more direct measurement of myelination.

  • May 16, 2017, at noon

    Time-Dependent Diffusion in the Brain

    Hong-Hsi Lee, MD

    PhD Candidate
    Sackler Institute of GRaduate Biomedical Sciences
    NYU School of Medicine


    Diffusion MRI is sensitive to the length scale of tens of microns, which coincides to the scale of microstructure in the human brain tissue. By changing the diffusion time or diffusion gradient pulse width, we can probe the brain micro-geometry via time-dependent diffusion measurements. To increase the sensitivity to the microstructure, STEAM sequence is often used for extending the range of diffusion time. However, water exchange between myelin water and intra-/extra-axonal water may bias the parameter estimations. This talk will focus on the time dependence either along or perpendicular to white matter axons and corresponding micro-geometries, and the correction for the time dependence measured by STEAM.

  • May 9, 2017, at noon

    Time-Dependent Diffusion in the Body

    Gregory Lemberskiy

    PhD Candidate
    Sackler Institute of GRaduate Biomedical Sciences
    NYU School of Medicine


    Diffusion of water molecules is directly influenced by the mountainous landscape of biological tissue. By modeling time-dependent diffusion, it is possible to reverse engineer various features of this landscape. The proposed model will depend on the underlying tissue microstructure, which poses an additional challenge of model selection. This talk will focus on the efforts of modeling diffusion time-dependence in the prostate, which embodies modeling problems, which concern partial volume and model selection, as well as imaging problems, such as geometric distortion and low SNR.

  • April 18, 2017, at noon

    Novel agents for PET targeted imaging and theranostics

    Giuseppe Carlucci, PhD

    Assistant Professor of Radiology
    NYU School of Medicine


    PET radiochemistry can be a great resource for imaging, treatment and point-of-care response/monitoring in Cancer and Cardiovascular disease. A novel small molecule targeting the cyclin-dependent kinases CDK4/6 and a series of radiolabeled nanobodies and peptides for atherosclerotic plaques imaging will be presented. The seminar will also focus on the fundamentals of Radiochemistry and how the newly established CAI2R Radiochemistry facility will operate.

  • April 14, 2017, at noon

    To microstructural imaging and beyond: separating the signal from the noise

    Nikola Stikov, PhD

    Assistant Professor of Biomedical Engineering
    Co-director, NeuroPoly, École Polytechnique, University of Montreal
    Producer of MRM Highlights and founder of OHBM blog


    Over the past decade, the number of microstructural imaging papers has been doubling every 2.7 years. With such growth, it is becoming increasingly difficult to perform a fair comparison between competing approaches. Some simplify the tissue modelling and overlook physiological constraints. Others overparametrize the models and amplify the noise. The outcome is a field of research with great promise, but few checks and balances.

    This lecture will introduce several frameworks for interpreting, validating and communicating microstructural imaging data. Examples will be drawn from myelin imaging in the brain, focusing on the challenges associated with mapping the longitudinal relaxation time (T1), the axon caliber, and the myelin thickness (g-ratio). The last part of the lecture will put these frameworks in a broader science communication context, discussing how medical imaging researchers can set new standards for reviewing, publishing, and publicizing their findings.

  • April 10, 2017, at noon

    MRI-guided drug delivery without chemical labeling

    Guanshu Liu, PhD

    Assistant Professor of Radiology
    Johns Hopkins University


    Recently, Chemical Exchange Saturation Transfer (CEST) has emerged as an attractive MRI contrast mechanism. In CEST, the MRI contrast is generated by transferring the magnetic labeled water-exchanging protons (OH, NH, or NH2) from a CEST agent to its surrounding water molecules. Many natural biological compounds naturally carry exchangeable protons, making them possibly detected by CEST MRI directly in a “label-free” manner. In our studies, we utilized this unique feature to directly detect drugs and drugs carriers, which makes MRI-guided drug delivery possible even without any chemical labeling, a strategy we called “natural labeling”. This new MRI labeling strategy in principle can be tailored to many existing drug delivery systems, and portends a new path to safe, rapid clinical translation of image-guided drug delivery.

  • April 4, 2017, at noon

    A High Throughput, MEMRI-Based Imaging Pipeline to Study Mouse Models of Sporadic Human Cancer

    Harikrishna Rallapalli, BS

    Graduate Student
    Sackler Institute of Graduate Biomedical Sciences
    NYU School of Medicine


    A high-throughput imaging pipeline is presented to characterize the heterogeneity in longitudinal disease progression in mouse models of human brain cancer and to test the efficacy of novel anti-cancer therapeutics in accurate mouse models of sporadic human cancer.

  • March 28, 2017, at noon

    In vivo characterization of rat models of Huntington’s disease using Diffusion Imaging

    Ines Blockx, PhD

    Assistant Research Scientist
    Center for Biomedical Imaging
    Department of Radiology
    NYU Langone Medical Center


    Huntington disease (HD) is a dominantly inherited and progressive neurodegenerative disorder, caused by a CAG trinucleotide repeat expansion (≥ 39 repeats) within the HD gene. The median age at which HD occurs, is around 40 years and the disease progresses over time and is invariably fatal 15–20 years after the onset of the first symptoms. The major goals of current HD research are to improve early detection and monitor pathological changes in individuals both at early and advanced stages of the disease. Animal models of inherited neurological diseases provide an opportunity to test potential biomarkers of disease onset and progression and evaluate treatments for translation to clinical care. Using several diffusion MR techniques we studied two different rat models of HD. In this talk I will present data that shows that diffusion MRI is a sensitive and quantitative method to detect HD related neurodegenerative changes, at both microstructural and subcellular levels.

  • March 7, 2017, at noon

    Magnetic Resonance Relaxometry and Macromolecular Mapping: An Inverse Problem Framework

    Richard Spencer, MD, PhD

    Chief, Magnetic Resonance Imaging and Spectroscopy Section
    NIH/National Institute on Aging


    There is an ongoing need for non-invasive identification of macromolecular changes in tissue. An important application is to the diagnosis of early osteoarthritis (OA). Our work in this area combines basic science studies in magnetic resonance imaging and relaxometry with emerging methodologies that carry translational potential. We will discuss multi-exponential transverse relaxation analysis as a means to identify underlying macromolecular compartments in normal and degraded cartilage, as well as important extensions of this work, based on higher dimensional relaxometry and compressed sensing. We will describe the mathematical setting for this work as a linear inverse problem. Further work in human subjects requires introduction of a nonlinear model system. We will describe several approaches to these problems and indicate the potential for improved detection of early cartilage degradation. Our methods are also applicable to directly mapping myelin in the brain, and we have obtained results showing myelination pattern alterations with age and in cognitive impairment. All of these studies are centered around the clinical goal of improving the ability of magnetic resonance methods to diagnose pathology and to monitor disease progression.

  • February 14, 2017, at noon

    Simultaneous PET/MRI in Advanced Breast Cancer : Initial Experiences and Future Potential

    Eric E. Sigmund, PhD

    Associate Professor of Radiology
    New York University School of Medicine


    Separately, PET and MRI have longstanding roles in diagnosis, prognosis and monitoring of breast cancer. Since the recent advent of the simultaneous PET/MRI platform, intense research has taken place to identify unrealized applications of their fusion. Initial work around the world has included study of a range of practical advantages (feasibility, efficiency, patient retention, physiologic simultaneity, co-registration), but always with an eye toward future ‘breakout’ applications beyond those with separate scans. I will describe efforts within our breast cancer research group that pursue both practical and fundamental benefits with the unique capabilities in our research center. Whole body evaluation of metastatic breast cancer patients is nearly equivalently done with PET/MRI as with PET/CT but with half the radiation dose. Dynamic contrast enhanced (DCE) MRI and intra-voxel incoherent motion (IVIM) MRI offer a range of quantitative characterizations of the primary tumor microenvironment (cellularity, vascular volume, vascular permeability) that when combined with fluorodeoxyglucose (FDG) uptake provide a comprehensive characterization of malignancy in one imaging session. Simultaneity also supports detailed intralesional correlations that may increase classification ability even further. Finally, future planned work with more specific microenvironment tracers and integrated PET and MRI pharmacokinetic modeling holds remarkable potential for oncologic management with noninvasive imaging.

  • February 6, 2017, at noon

    Multi Cycle analysis of cardiac function in real-time

    Markus Hüllebrand

    Fraunhofer MEVIS
    Bremen, Germany


    Analyzing moving organs such as the heart in MRI is a challenging task. In clinical routine images are acquired over several heartbeats to reconstruct all contraction phases of one representative cardiac cycle using ECG-gating and breath-hold techniques.

    Real-time MRI techniques allow the acquisition of serial 2D images with a temporal resolution of up to 20 ms under free breathing. The analysis of real-time MRI sequences, however, requires adapted segmentation techniques as well as an advanced analysis providing information about temporal evolution of parameters during individual heart cycles in amulti cycle analysis workflow.

  • February 1, 2017, at noon

    Imaging the fetus and the neonate using MR

    Giulio Ferrazzi, PhD

    Research Associate
    Biomedical Engineering Department
    King’s College London, UK

    No abstract was provided for this talk.

  • January 24, 2017, at noon

    Integrated PET-MRI for current clinical neuroradiology dilemmas—a CAI2R perspective

    Timothy Shepherd, MD, PhD

    Assistant Professor, Director of Brain Mapping
    Department of Radiology
    New York University School of Medicine

    No abstract was provided for this talk.

  • January 23, 2017, at noon

    Optimal first-order convex minimization methods with applications to image reconstruction and machine learning

    Jeffrey Fessler, PhD

    William L. Root Professor of EECS
    University of Michigan


    Many problems in signal and image processing, machine learning, and estimation require optimization of convex cost functions. For convex cost functions with Lipschitz continuous gradients, Nesterov’s fast gradient method decreases the cost function at least as fast as the square of the number of iterations, a rate order that is optimal. This talk describes a new first-order optimization method called the optimized gradient method (OGM) that converges twice as fast as Nesterov’s famous method yet has a remarkably similar simple implementation. Interestingly, Drori recently showed that OGM has optimal complexity among first-order methods. I will discuss other recent extensions and show examples in machine learning and X-ray computed tomography (CT). Combining OGM with ordered subsets provides particularly fast reconstruction for CT. This work is joint with Donghwan Kim.

  • January 10, 2017, at noon

    In-vivo High Resolution Ocular Imaging – Innovative Technologies and Clinical Challenges

    Hiroshi Ishikawa, MD

    Professor of Ophthalmology
    Director, Ocular Imaging Center
    NYU Langone Medical Center

    Joel Schuman, MD

    Professor and Chairman of Ophthalmology
    Professor of Neuroscience and Physiology
    NYU Langone Medical Center
    Professor of Electrical and Computer Engineering
    NYU Tandon School of Engineering

    Chaim “Gadi” Wollstein, MD

    Professor of Ophthalmology
    Director, Ophthalmic Imaging Research Laboratory
    Vice Chair for Clinical Research
    NYU Langone Medical Center


    In recent years ocular imaging has become the cornerstone for clinical diagnosis and disease monitoring as well as a primary research tool in ophthalmology. In this presentation we will discuss state-of-the-art, in-vivo, high resolution ocular imaging technologies. We will present the utility and challenges of the technologies to advance clinical practice and research of glaucoma—a leading cause of blindness and visual morbidity.

  • January 6, 2017, at noon

    The Virtual Biopsy: Magnetic Resonance Spectroscopy of Traumatic Brain Injury

    Alexander P. Lin, PhD

    Director, Center for Clinical Spectroscopy
    Department of Radiology, Brigham and Women’s Hospital
    Assistant Professor, Harvard Medical School


    Advances in neuroimaging provide us with greater insight to brain injury than ever before. Magnetic resonance spectroscopy is a non-invasive method of measuring brain chemistry altered by bran injury using readily available MRI, thus providing a virtual biopsy of concussions. A review of the technology and current findings from the acute to chronic stages of mild brain injury, including the rising concern of chronic traumatic encephalopathy in sports and military-related repetitive brain trauma, will be discussed.

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