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.
Technological Innovation
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.
Interdisciplinary Collaboration
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.
Clinical Translation
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.
Open Science
CAI2R shares research software and data resources in order to encourage progress throughout the field. Our training activities include hosting visiting scientists, a regular radiology research forum, and a biennial workshop devoted to emergent imaging technologies.
Latest Posts
Krzysztof Geras and Jan Witowski, machine learning researchers in medical imaging, talk about understanding one's data, working across disciplines, and radiologists' "new colleague."
A combination of radiologists and AI reduced overdiagnosis and more accurately identified breast cancer in ultrasound exams.
Florian Knoll, incoming chair of imaging at University of Erlangen–Nuremberg, talks about his background, not being greedy, and why he does what he does.
Latest Resources
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.
A project by the Diffusion Study Group of the International Society for Magnetic Resonance in Medicine (ISMRM)
Compare machine learning performance across classifiers and datasets.
By the Numbers
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