Abstract

STAGE is a rapid multi-contrast imaging technique that can provide not only standard T1 and T2 like contrasts but also proton spin density weighted images, T2* weighted images, SWI, R2* maps, QSM maps, water content maps, circle of Willis MRA, auto segmentation of white matter, gray matter and cerebrospinal fluid and simulated FLAIR in 3 to 6 minutes. Coupled with a new denoising algorithm called CROWN, STAGE can be used to create a new series of homogeneous images that have been corrected for both radiofrequency transmit and receive inhomogeneities. During the last few years, we have focused on measuring iron content and neuromelanin (NM) in the substantia nigra (SN) for comparing idiopathic Parkinson’s disease (PD) with healthy controls and patients with other movement disorders. We have found that the volume of NM, the iron content of the SN, volume of the SN and the N1 sign can together provide an area under the curve (AUC) of 95% in distinguishing PD from healthy controls. We have developed a template of the midbrain to allow for automatic detection and quantification of these properties. During the process, we used tSWI to enhance the N1 sign visibility. Our data have been acquired using STAGE which is a rapid, quantitative, multi-contrast data collection and processing that is vendor agnostic. As such we have created a protocol that can be used for PD studies globally.

More recently, we have been using fast low flip angle multi-echo (FLAME) spin density weighted imaging to map the white matter fiber tracts from the brainstem up to the thalamus in vivo. Although tractography from diffusion tensor imaging has been commonly used to map the WM fiber tracts connectivity, it is difficult to differentiate the complex WM tracts anatomically. With a clear delineation of major fiber tracts, it may be possible to use structurally constrained DTI fiber tracking to improve their visualization. Using the FLAME approach, we have been able to map out all the major fiber tracts in a reasonably short scan time of 10 minutes with 0.67×0.67×1.34 mm³ resolution at 3T.

Abstract

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 intracranial EEG neural recording, we can examine the underlying neural mechanisms of prior knowledge deployment from distinct sources of prior knowledge. By training artificial neural networks to perform similar image recognition tasks, we can identify plausible computations the human brain may use to achieve the same. Specifically, this project examines visual recognition aided by prior knowledge derived from lifelong learning as well as one-shot learning. This talk will present early results and plans to integrate data from multiple experiments.

About the Speaker

Jonathan Shor is a sixth-year PhD student advised by Biyu He, associate professor of neurology, neuroscience and physiology, and radiology. After receiving his BS in computer science, Jonathan spent 10 years in the private sector before returning to academia, earning an MS in computer science at Columbia University before joining the Vilcek Institute. He is interested in using neuroimaging and computational techniques to identify the neural correlates of conscious perception.

Abstract

The visual system develops abnormally when visual input is absent or degraded early in life. Restoration of the visual input past 7 or 8 years of age is generally thought to have limited benefit because the visual system will lack sufficient plasticity to adapt to and utilize the information from the eyes. Recent evidence, however, shows that congenitally blind adolescents can recover both low-level and higher-level visual function following surgery. In this talk, I will discuss recent work in our lab that links changes in behavioral performance to longitudinal changes in white matter integrity in congenitally blind patients with dense bilateral cataracts. Our results suggest that sufficient plasticity remains in adolescence to partially overcome abnormal visual development and help localize the sites of neural change underlying sight recovery.

Abstract

Multiple sclerosis (MS) is a neuroimmune disorder characterized by demyelination and neurodegeneration, leading to cognitive and motor impairments. This study investigates the effects of two MS-modifying medications, interferon beta and dimethyl fumarate, on functional brain connectivity using graph theoretical network analysis. We recruited 107 relapsing-remitting MS (RRMS) patients and 62 healthy controls, collecting resting-state functional MRI data to construct brain networks based on regional blood-oxygen-level-dependent signals. The study consisted of two parts: region-based and connection-based analysis. In the region-based analysis, we assessed graph theoretical measures, including degree centrality, nodal efficiency, and betweenness centrality, to evaluate alterations in brain networks and to explore hubs as highly connected regions in healthy controls and patient groups under different medications. The connection-based analysis employed an edge-centric approach to examine metrics such as entropy, co-fluctuation, and edge strength. Results from the region-based analysis showed differences in network metrics, particularly in the default mode, dorsal attention, and salience networks, among patients on different medications. The connection-based analysis revealed changes in motor, sensory, visual, and attention networks, alongside variations in the brain network strength. Entropy and changes in edge functional connectivity (eFC) revealed distinct effects of each medication. Interferon beta caused hemisphere-wide alterations in the somatomotor network, with opposite changes in each hemisphere. Conversely, dimethyl fumarate’s effect was marked by decreased entropy in the left visual and right dorsal attention networks, indicating reduced diversity in network connectivity. The study highlights potential biomarkers for the treatment effect and demonstrates the nuanced effects of interferon beta and dimethyl fumarate on brain function in patients with MS.

About the Speaker

Sara Hejazi is a brain network scientist with a diverse background in engineering and neuroscience. She received her PhD from the engineering college at the University of Central Florida in May 2024. Her doctoral research utilized brain network analysis to investigate changes in brain connectivity in patients with multiple sclerosis (MS) using fMRI data. During her internship at the Mayo Clinic’s department of artificial intelligence and informatics from summer 2022 to spring 2023, she contributed to a project involving network analysis aimed at studying the development of mild cognitive impairment (MCI) and its progression to dementia. In the summer of 2023, she joined the University of Florida College of Medicine to further expand her expertise in brain network analysis in preclinical research, specifically focusing on traumatic brain injury (TBI).

Abstract

Epileptogenic foci often organize into networks, making the diagnosis of focal epilepsy challenging due to the spontaneous nature of the disease. While triggering seizures with intra-cranial stimulation aids in diagnosis, concerns about invasiveness drive the research for non-invasive alternatives. In this talk, Chloé will highlight the potential of temporal interference (TI) stimulation in diagnosing refractory epilepsy by identifying epileptogenic zone networks (EZN) in silico. Crucial model parameters, linked to seizure onset, are inferred from patient-specific brain models to quantify brain regions’ epileptogenicity and diagnose the EZN.

About the Speaker

Chloé Duprat is a PhD candidate at the Institut des Neurosciences des Systèmes at Aix Marseille Université. She holds a master’s degree in mechanical and biomedical engineering and in computational neuroscience and neuro-engineering. Her work primarily focuses on dynamic modeling and neural stimulation modeling in patient-specific whole-brain models.

Abstract

T1ρ mapping is emerging as a promising contrast-free alternative to LGE imaging for myocardial tissue characterization. However, the high sensitivity of conventional spin-lock preparations to B0 and B1+ inhomogeneities renders it ineffective at high field strengths. Adiabatic spin-lock preparations, consisting of trains of hyperbolic-secant pulses, can overcome this limitation. During this talk I will discuss how we deigned and optimized adiabatic T1ρ preparations for in vivo mapping at 3T. I will compare the results of adiabatic T1ρ mapping with conventional non-adiabatic techniques and show how adiabatic spin-locks yielded improved precision and reproducibility. Results from a small cohort of patients indicated that adiabatic T1ρ mapping could potentially be used to discriminate between scar and healthy myocardium. Finally, I will discuss how we used a combination of selective and non-selective adiabatic RF pulses to achieve robust adiabatic T1ρ maps with dark-blood contrast.

About the Speaker

Chiara holds a BSc in biomedical engineering from Politecnico di Milano and a double MSc in biomedical engineering from Politecnico di Milano and Politecnico di Torino, with honors. She completed her master’s studies with a thesis project on image filtering and compression for super high frame rate electron microscopy at Lawrence Berkeley National Laboratory. She is currently a PhD candidate at the Magnetic Resonance Systems Lab in the department of imaging physics at TU Delft and expects to graduate in September. During her PhD Chiara has worked on designing novel RF preparations for robust spin-lock imaging at 3T, with a special focus on cardiac MR applications.

Abstract

Accurate interpretation of medical images is crucial for disease diagnosis and treatment, and AI has the potential to minimize errors, reduce delays, and improve accessibility. The focal point of this presentation lies in a grand ambition: the development of ‘Generalist Medical AI’ systems that can closely resemble doctors in their ability to reason through a wide range of medical tasks, incorporate multiple data modalities, and communicate in natural language. Starting with pioneering algorithms that have already demonstrated their potential in diagnosing diseases from chest X-rays or electrocardiograms, matching the proficiency of expert radiologists and cardiologists, I will delve into the core challenges and advancements in the field. The discussion will navigate towards the topic of label-efficient AI models: with a scarcity of meticulously annotated data in healthcare, the development of AI systems capable of learning effectively from limited labels has become a key concern. In this vein, I’ll delve into how the innovative use of self-supervision and pre-training methods has led to algorithmic advancements that can perform high-level diagnostic tasks using significantly less annotated data. Additionally, I will talk about initiatives in data curation, human-AI collaboration, and the creation of open benchmarks to evaluate the generalizability of medical AI algorithms. In sum, this talk aims to deliver a comprehensive picture of the state of ‘Generalist Medical AI,’ the advancements made, the challenges faced, and the prospects lying ahead.

Abstract

Contrast-enhanced T1-weighted imaging is an important component of many clinical protocols, such as for the detection of brain tumors and metastases. A widely used acquisition scheme is the magnetization-prepared rapid gradient-echo (MP-RAGE) sequence, which depicts both enhancing lesions and blood vessels with bright signal intensity. As an alternative, a novel family of sequences has been recently proposed, named T1 Relaxation-Enhanced Steady-State (T1RESS), which suppress the contrast of background tissue and signal of flowing blood, resulting in improved conspicuity of enhancing lesions. The improved sensitivity is especially valuable for detecting small lesions, which can have a major impact on the prognosis of a patient. In this work, radial stack-of-stars T1RESS sequences were developed, which offer improved motion robustness and enable advanced reconstructions such as GRASP for dynamic imaging. Three sequence variants were introduced, including (a) balanced T1RESS, which can potentially be used at low field (0.55T) for high-SNR T1-weighted imaging; (b) unbalanced T1RESS-FISP, which suppresses the signal of blood vessels, and (c) unbalanced T1RESS-PSIF, which provides dark-blood imaging. To broaden the clinical applicability, the combination with GRASP reconstruction, DIXON fat/water separation, and 1D GRAPPA acceleration in the Cartesian direction of the stack-of-stars geometry were implemented. The radial T1RESS sequences have been tested for brain imaging and applications in the body in healthy volunteers and patients.

About the Speaker

Ruoxun Zi is a fourth-year graduate student in the Vilcek Institute’s Biomedical Imaging and Technology training program working with Kai Tobias Block, PhD, and Riccardo Lattanzi, PhD. She holds an MS in biomedical engineering from Johns Hopkins University.

Abstract

The study of hyperuniform states of matter is an emerging multidisciplinary field, influencing and linking developments across the physical sciences, mathematics and biology [1,2]. Hyperuniform many-particle systems in d-dimensional Euclidean space are characterized by an unusual suppression of density fluctuations at large lengths scales.

The hyperuniformity concept generalizes the traditional notion of long-range order, and provides a unified theoretical framework to categorize crystals, quasicrystals and exotic disordered systems. Disordered hyperuniform many-particle systems can be regarded to be new states of disordered matter in that they behave more like crystals or quasicrystals in the manner in which they suppress large-scale density fluctuations, and yet are also like liquids and glasses because they are statistically isotropic structures with no Bragg peaks.

I will provide an overview of the hyperuniformity concept and its generalizations. Subsequently, I will discuss the diffusion spreadability S(t), which is a measure of the spreadability of diffusion information as a function of time.

Exact formulas for the spreadability in any Euclidean space dimension are derived in terms of two-point statistics that characterize the microstructure.

Further, closed-form general formulas are derived for the short- , intermediate- and long-time behaviors of S(t) in terms of crucial small-, intermediate- and large-scale structural information, respectively. The long-time behavior of S(t) enables one to distinguish the entire spectrum of translationally invariant microstructures that span from hyperuniform to nonhyperuniform media.

For hyperuniform media, disordered or not, the “excess spreadability”, S(∞) – S(t), decays to its long-time behavior exponentially faster than that of any nonhyperuniform medium, the slowest being “antihyperuniform media”. The stealthy hyperuniform class is described by an excess spreadability with the fastest decay rate among all translationally invariant structures.

We establish remarkable connections between the spreadability and problems in discrete geometry, NMR pulsed field gradient spin-echo amplitude as well as diffusion MRI measurements.

References

1. Torquato S and Stillinger FH. Local density fluctuations, hyperuniformity, and order metrics. Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Oct;68(4 Pt 1):041113. doi: 10.1103/PhysRevE.68.041113.
2. Torquato S. Hyperuniform States of Matter. Phys Reports. 2018 Jun;745:1-96. doi:
10.1016/j.physrep.2018.03.0012.
3. Torquato S. Diffusion Spreadability as a Probe of the Microstructure of Complex Media Across Length Scales. Phys Rev E. 2021 Nov;104(5-1):054102. doi: 10.1103/PhysRevE.104.054102.

Abstract

Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models analyze each modality independently, missing opportunities to integrate information across imaging modalities and time. In this study, we present Multi-modal Transformer (MMT), a neural network that utilizes mammography and ultrasound synergistically, to identify patients who currently have cancer and estimate the risk of future cancer for patients who are currently cancer-free. MMT aggregates multi-modal data through self-attention and tracks temporal tissue changes by comparing current exams to prior imaging. Trained on 1.3 million exams, MMT achieves an AUROC of 0.943 in detecting existing cancers, surpassing strong uni-modal baselines. For 5-year risk prediction, MMT attains an AUROC of 0.826, outperforming prior mammography-based risk models. Our research highlights the value of multi-modal and longitudinal imaging in cancer diagnosis and risk stratification.

Abstract

Inflammation of the central nervous system (CNS), driven by the innate immune system, plays a crucial role in the pathophysiology of multiple sclerosis (MS). Critical cell types involved in this process include CNS resident microglia and blood-derived macrophages. Imaging methods such as MRI and PET can be used to assess CNS inflammation in MS, and we will discuss our work focusing on these two approaches.

In measuring innate immune activity via MRI, we observe that chronic CNS inflammation in MS lesions is maintained in part by iron-laden pro-inflammatory microglia/macrophages at the rim of chronic active lesions (CALs). These paramagnetic rim lesions (PRLs) are believed to contribute to a more aggressive phenotype of the disease and thus represent a novel target for treatment to reduce disease progression in MS. Utilizing quantitative susceptibility mapping (QSM) to measure PRLs, our group has concentrated on creating tools to identify and quantify lesion-based chronic innate immune activity. We have been investigating the impact of chronic lesion-based inflammation on the disease course. Moreover, we have expanded our work to propose a novel application of QSM to quantify the inflammatory trajectory within PRLs, providing a new target for treatment in MS with either current or novel immune modulators.

Regarding the measurement of chronic inflammation via PET, [11C]-PK11195-PET (PK-PET) is a first-generation ligand that binds to the 18 kDa translocator protein (TSPO), expressed on the outer mitochondrial membrane of activated myeloid cells, and it has been used to demonstrate increased innate immune activity throughout the brains of MS patients. We have employed PK-PET to validate in vivo that PRLs on QSM exhibit higher inflammation than rimless lesions. Newer generation TSPO ligands, with enhanced specificity and brain penetration compared to PK-PET, which may enhance PET’s ability to identify and compare inflammatory activity across individual chronic lesions. We are currently focusing our research on a second-generation TSPO ligand, [11C]-DPA 713. To circumvent the challenges of arterial sampling, we have developed a supervised clustering algorithm (SVCA). This algorithm serves as a reliable non-invasive method for quantifying this ligand’s presence. Additionally, we are applying this innovative technique to investigate the dynamics of individual MS lesions.

Abstract

Cerebral cortical neural architecture is an important biomarker for prognosis and diagnosis of a variety of brain disorders. At present, assessing soma and neurite (axons and dendrites projecting from cell body) architecture in the cerebral cortex requires neuropathology. However, neuropathology is invasive and provides only local neuronal architecture information. Diffusion MRI (dMRI) has been the method of choice for noninvasively measuring brain microstructure. Cerebral cortical architecture is complicated consisting of somas of neurons and glia as well as neurites. In this talk, I will introduce our efforts towards developing dMRI-based techniques to accurately and reproducibly measure both soma and neurite densities in complicated cerebral cortical architecture by harnessing both a “top-down” and a “bottom-up” approach. Using the “top-down” approach, we modeled the complex cellular architecture of the cerebral cortex by establishing the non-Gaussian dMRI signal modeling for measuring the cortical micro-architecture based on theoretical modelling and diffusion simulations. In the “bottom-up” approach, we quantified the cortical micro-architecture using deep learning by mapping dMRI signal to ground-truth.

Abstract

Early detection and treatment of Alzheimer’s disease (AD) and related disorders is paramount in the prevention of neuronal degeneration and dementia. AD is characterized by a long preclinical stage noted by the deposition of amyloid plaques and tau tangles, followed by a destructive cascade of neurodegeneration typically beginning in the medial temporal lobe, leading to mild cognitive impairment and progression to dementia. However, only 40% of patients with preclinical AD progress to mild cognitive impairment, and only 21% of patients with mild cognitive impairment progress to dementia. Further, current procedures for detecting amyloid and tau deposition are either biofluid (CSF and blood) biomarkers that lack spatial information thus limiting their utility in prognosis, or PET scans which are prohibitively expensive and involve IV injection of radiotracers. Thus, there is a need to develop more accessible, spatially specific biomarkers and to differentiate patients with amyloid and tau depositions who will progress to dementia from those who will not. A promising avenue for early detection of neurodegeneration is subjective cognitive decline (SCD). SCD describes patients who complain of memory difficulties but score normally on cognitive testing. Multicenter studies and meta-analyses show increased rates of dementia in participants with SCD. Many previous studies in SCD have focused on volume and cortical thickness, with inconsistent results. Multimodal MRI provides an opportunity to develop biologically sensitive biomarkers that are less expensive than PET, do not involve IV injection of radiotracers, and provide in vivo spatial information of pathology deposition. While amyloid and tau deposits are necessary for the diagnosis of AD pathology, they are not sufficient for clinical prognosis. Further, their role in the biological pathway leading to the neurodegenerative cascade is unclear. Amyloid in particular correlates poorly with clinical symptoms. Scientific consensus at the 2022 Clinical Trials on Alzheimer’s Disease Conference concluded that it is important to explore other potential therapeutic targets alongside amyloid and tau, although the lack of biomarkers pose a significant barrier. While prior DTI research in SCD is promising for prognostication, studies in the medial temporal lobe and cortical gray matter have been limited, despite their importance in Alzheimer’s disease. Here, we use advanced diffusion MRI preprocessing and modeling to study the microstructure of the medial temporal lobe and cortical gray matter of this group with heightened dementia risk. We present several new potential diffusion MRI biomarkers of SCD.

Abstract

The advent of deep neural networks (DNNs) has marked considerable breakthroughs in magnetic resonance imaging (MRI) analysis. These state-of-the-art methodologies are increasingly deployed to resolve intricate predicaments in quantitative MRI such as diffusion-weighted MRI, and quantitative cardiac T1 and T2 distribution mapping, delivering superior speed and precision over conventional techniques. Nonetheless, existing challenges such as mitigation of motion artifacts and enhancement of resilience against extremely low signal-to-noise ratios still remain, restricting their clinical utility. To overcome these limitations, we introduce an innovative strategy integrating a physically-primed DNN architecture. This unique architecture embeds the signal decay model directly within the neural network, augmenting the network’s generalization capability and fostering the development of stable algorithms, which, in turn, produce refined predictions. Our advanced methodology reveals extensive potential applications including early assessment of response to neoadjuvant chemotherapy in breast cancer patients, establishing motion-robust quantitative cardiac T1 mapping, and T2 distribution mapping for evaluating inflammation in animal models. The proposed approach opens new vistas for more nuanced and clinically viable solutions in the realm of quantitative MRI analysis, paving the way for enhanced diagnostic precision and patient outcomes.

Abstract

Astrocytes are non-neuronal glial cells in the central nervous system with myriad functions: they maintain the blood brain barrier, recycle neurotransmitters, and are even thought to be a component of every neuronal synapse. We have found that astrocytes also form gap junctional networks that help neurons survive degeneration. Brain regions connected by astrocytes support one another metabolically, but these connections also make linked regions collectively vulnerable to degenerative stress. Astrocyte networks have never been mapped, a critical need considering that these networks may shape the course of degeneration. Here, we use a combination of novel biomolecular tools, tissue clearing, and light sheet imaging to reveal for the first time the shape, extent, and potential plasticity of astrocytic networks in intact brains.

Abstract

The goal of the MRI4ALL Hackathon 2023, which took place last October around the i2i Workshop, was to create a fully-fledged low-field MRI scanner in just four days and to share all developments as open-source resources. Fifty two scientists from 16 institutions participated in the event and worked in four teams on the construction of the main magnet, gradient coils, RF hardware, and software platform. In this talk, we will discuss how the idea for the hackathon came together, present the created scanner design and components, and summarize our experience from the hackathon week. We will also give a live demo of the scanner, which is currently located at our 22nd Street laboratory.

Abstract

Low-field magnetic resonance imaging (LFMRI) offers greater accessibility to MR scanners by reduced manufacturing and maintenance costs. However, the signal-to-noise ratio of the acquired images is inherently diminished by the use of low field strengths. The standard technique of averaging multiple acquisitions (to increase SNR) reduces LFMRI accessibility for patients by increasing scan time and cost. Hence, one of the recent trends in LFMRI research focuses on employing advanced image processing techniques to enhance few-average, and even single-average, LFMR image quality and enable greater scanner accessibility.

In this talk, we will cover several strategies for self-supervised learning of deep neural networks (DNNs) for MRI denoising, given only unlabeled noisy data. We begin by reviewing standard observation models for parallel (multi-coil) MRI contaminated with additive white Gaussian noise. We will then cover state-of-the-art loss functions for training DNNs when a single, or potentially multiple, noisy observations of each subject are available for training (ex. SURE, Noise2Noise, Coil2Coil). We use labeled MRI datasets with synthetic degradation to allow for quantitative comparison of the different methods. Finally, we will show applications of these techniques to unlabeled noisy MRI datasets of the lung and the prostate acquired at 0.55T. Complications arising in the translation from synthetic to real-world experiments, such as coil-correlation and noise-level estimation, will be highlighted.

Abstract

In this talk we highlight the importance of biophysical modeling of the diffusion MRI (dMRI) signal in order to obtain metrics that are sensitive and specific to brain microstructure. We will focus on the standard model of diffusion in white matter (WM), its validation, parameter estimation, and clinical applications. First, Ricardo will present a comprehensive histological validation of standard-model parameters by characterizing WM microstructure in sham and injured rat brains using 3D electron microscopy and ex vivo dMRI. This validation reveals that the standard model is sensitive and specific to microscopic properties. Next, Ying will discuss several different approaches aimed at resolving the degeneracy issue commonly encountered in parameter estimation using clinically feasible dMRI protocols. He will compare the model constraints of existing methods and an unconstrained, data-driven approach. He will present in vivo data regarding early development, multiple sclerosis, and stroke for validation of these diffusion model parameter estimators.

Abstract

Magnetic Resonance Imaging (MRI) below 1 Tesla is a recently emerging area, driven by the integration of modern MRI infrastructures (gradient system, RF coils, software, etc.) and the unique physical prosperities of the low magnetic field. These modern low-field systems provide lower susceptibility, improved B0 homogeneities, lower specific absorption rate (SAR), lower acoustic noise, and favorably scaled relaxivities (lower T1 and longer T2/2*). These advances unlock new clinical possibilities, particularly in lung imaging, dynamic imaging, and imaging near metallic implants. In this talk, I will give an overview of research conducted using a prototype whole-body 0.55 T MRI (MAGNETOM Aera, Siemens Healthineers) at the Dynamic Imaging Science Center (DISC) of USC since 2021. Covering applications from head to toe, this talk highlights the promising outcomes and potential clinical implications of this innovative 0.55 T MRI approach in cardiac, lung, body, musculoskeletal imaging, and so on.

Abstract

First, Dr. Asslaender will discuss the connection between longitudinal relaxation and magnetization transfer. He will explain recent advances in the biophysical modeling of these effects, outline implications for our understanding of longitudinal relaxation in biological tissue, such as brain white matter, and touch on new avenues for diagnostic imaging of neurodegenerative diseases such as multiple sclerosis. Advanced biophysical models commonly involve a plethora of parameters, which hampers time-efficient mapping of these parameters. In the second part of the talk, Dr. Flassbeck will discuss approaches to overcome these challenges. He will discuss pulse sequence design and optimization in the realm of MR fingerprinting, discuss the concept of the hybrid state, and touch on image reconstruction of highly undersampled dynamic imaging.

Abstract

Cerebral palsy (CP) is a neuromusculoskeletal condition arising from a neural lesion of the central nervous system which occurs before, during or soon after birth. The neural lesion leads to progressive muscular degeneration making the condition the most common cause of physical disability in paediatric populations. MRI and Diffusion Tensor Imaging (DTI) provides great potential for an in vivo assessment of muscle structural and architectural properties of lower-limb muscles. This allows for an understanding of the structure-function relationship in lower-limb muscles. Previous research has investigated structural and architectural properties of lower-limb muscles including muscle volume and pennation angle in paediatric populations with CP. In this research, anatomical MRI was used to extract volumes, i.e. structures, of lower-limb muscles. Additionally, DTI was used to generate voxel-based fibre orientations based on the diffusion properties of water molecules within lower-limb muscles, and a deterministic tracking algorithm was used to reconstruct 3D muscle architecture. Structure and architecture datasets were combined to develop longitudinal, statistical models of lower-limb muscles morphology and 3D fibre orientations. The developed models characterised the dominant variations of lower-limb muscles over a specific period of time in paediatric populations with CP and healthy counterparts. The developed models offered a longitudinal, quantitative analysis of muscle structure and architecture providing insights into the progression of muscle degeneration in paediatric populations with CP, which may assist in the design of targeted clinical interventions for motor dysfunctions associated with this condition.

Abstract

Recent development of time-dependent diffusion MRI (TDDMRI) has demonstrated unique advantages in depicting cellular microstructures by characterizing the time-dependence of restricted water diffusion. Previous simulation and animal studies have shown that TDDMRI is sensitive to microscopic pathology in tumors, yet clinical translation of this technique has been challenging due to the hardware requirement for high gradient strength. Nevertheless, initial tests in human studies are emerging. This talk will first introduce TDDMRI, from theory to animal and human studies, and then describe our recent endeavor in developing new pulse sequences for clinical translation of TDDMRI. I will then talk about our clinical studies in prostate cancer, glioblastoma, and breast cancer, which showed promising clinical value. Last, I will also touch upon our recent work on developing an ultra-high-gradient system for better potential of TDDMRI.

Abstract

The choroid plexus (ChP), a highly vascularized structure in the brain ventricular system, is increasingly recognized for its vital role in cerebrospinal fluid (CSF) hemostasis and waste clearance, particularly in relation to aging and dementia. While some imaging studies have investigated its volumetric changes, the majority of ChP studies have relied on histopathological approach. Limited MRI research has been conducted on age-related vascular degeneration, primarily due to resolution and tissue contrast constraints. This presentation will focus on the in vivo exploration of vascular aging in the ChP utilizing multimodal MRI data, including USPIO-enhanced susceptibility-sensitive imaging on 7T as well as dynamic-contrast enhanced (DCE) and arterial spin labeling (ASL) perfusion MRI on 3T; These modalities allow for detailed characterization of both vascular anatomical and blood flow alterations in normal aging process. We observed significant age-related vascular degenerative changes in three adult lifespan study cohorts. These changes include reduced vascular enhancement and blood flow, accompanied by structural alterations, such as increased ChP stromal volume, cysts formation and changes in water mean diffusivity on diffusion MRI. Combined, the observed vascular aging in the ChP through MRI may play a role in compromised ChP functions, including CS secretion, filtration, and waste clearance. These in vivo insights are anticipated to enhance our understanding of the ChP’s involvement in cognitive dysfunction.