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.
Patricia Johnson, who researches machine learning image reconstruction, talks about faster MRI, visual preferences, and diagnostic interchangeability.
Recent research shows that sodium MRI can predict cancer response to chemotherapy. The National Cancer Institute is funding NYU Grossman School of Medicine to develop and validate the method.
Anna Chen, PhD candidate in biomedical imaging, talks about what led her to research, why she's working with MR spectroscopy, and how she's learning about business.
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
years since founding