We bring people together to create new ways of seeing.
CAI2R creates technologies for better acquisition, reconstruction, and analysis of medical images.
Our innovations advance research in biomedicine and our best technologies become leading-edge tools in clinical radiology.
CAI2R (pronounced care) is a National Center for Biomedical Imaging and Bioengineering supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) and operated by NYU Langone Health.
A Unique Model for Academic Medical Research
Research and development in biomedical imaging are extraordinarily complex. We assemble translational research teams that meet the challenge.
CAI2R research leads the field in fast imaging, machine learning for image acquisition and reconstruction, ultraflexible biomorphic hardware, complementing MRI data with novel sensing strategies, mapping of tissue microstructure, and artificial intelligence methods for early detection of disease.
Our Center brings together basic scientists, engineers, clinical radiologists, physicians, computer scientists, and specialists from the medical imaging industry. We form innovative research partnerships with institutions in medicine, academia, and tech.
CAI2R innovations advance knowledge, diagnosis, and therapy of neurological conditions, musculoskeletal conditions, cardiovascular conditions, and cancer. Our research is focused on scientific and clinical applications of new biomedical imaging technologies.
Ilias Giannakopoulos, postdoctoral fellow in MRI, talks about how electromagnetic waves interact with the body, why matrix compression matters, and where he finds inspiration.
A team of scientists is using a battery of imaging methods to visualize cells, tissues, and joints in a quest for early noninvasive imaging biomarkers of PTOA.
Radhika Tibrewala, graduate student in biomedical imaging, talks about the new fastMRI prostate dataset, deep learning in MRI, and how she started a PhD remotely in 2020.
MRI data from suspensions of spherical polystyrene microbeads for research on gradient-echo and spin-echo signal decay in the presence of microstructural sources of magnetic susceptibility.
MATLAB scripts for data-driven optimization of magnetization-prepared gradient echo sequences used in T1ρ mapping.
MATLAB software for robust standard-model parameter estimation from diffusion MRI data
A platform for integrating algorithms, AI models, and post-processing tools into clinical practice.
Simultaneous machine learning optimization of parallel MRI sampling pattern and variational-network image reconstruction parameters.
By the Numbers
years since founding