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.
NYU researchers won a deep learning challenge to detect lesions in digital breast tomosynthesis (DBT) images. We discuss the obstacles that DBT poses to deep learning and look at how our team navigated them.
Hong Hsi Lee, alumnus of the Biomedical Imaging & Technology PhD Program at NYU Grossman School of Medicine, has just completed postdoctoral training at our Center and is headed to a postdoctoral fellowship at Massachusetts General Hospital. We take a look at his journey.
An interest in machine learning in medicine sparked by a college project has taken Vatsal Sodha from Gujarat to Arizona to a bicoastal telecommute.
A convolutional neural network for anatomically-guided PET reconstruction.
A compact, stand-alone device for tracking and correcting patient motion in MRI exams.
Machine learning optimization of k-space sampling for accelerated MRI.
A non-uniform fast Fourier transform with Kaiser-Bessel gridding for machine learning applications in PyTorch.
Code for low-rank plus sparse decomposition of orientation distribution functions.
By the Numbers
years since founding