Research
My research is focused to the development of rapid, motion-tolerant, and quantitative MRI methods using advances in mathematical, physical, and computational models. My research group has a rich history in the development of compressed sensing techniques to speed up the acquisition of dynamic images and the combination of radial acquisition and compressed sensing to enable fast free-breathing MRI in the body. This effort has evolved to the application of artificial intelligence techniques to further accelerate MRI acquisition and produce live images that resolve motion without increasing scan time. One of the main applications of Movienet is to improve motion tolerance in pediatric MRI to remove anesthesia in small kids. I am also interested in the application of MRI to guide radiation therapy using a combined MR-Linac system and my research group has developed a low-latency 3D MRI technique called MRSIGMA that can follow anatomical changes due to respiratory motion in real-time. Another research interest is the acquisition of MRI-based physical biomarkers to improve diagnosis and perform early treatment response assessment.
Specific research topics include:
- Non-Cartesian k-space acquisitions for motion-resistant and motion navigated imaging
- Deep learning reconstruction of undersampled k-space data for fast 3D and 4D MRI
- Real-time 3D MRI for radiotherapy guidance
- Fast motion-resistant pediatric MRI with reduced anesthesia
- Deep learning quantification of dynamic contrast-enhanced MRI
- High-resolution distortion-free diffusion MRI in the body
- Fast motion-resistant T1-rho mapping
Current Projects:
- Fast MRI using artificial intelligence
- Motion-tolerant pediatric MRI with reduced anesthesia
- Real-time 4D MRI for radiation therapy guidance on a MR-linac system
- Robust quantitative imaging of physical parameters
Bio
Ricardo received a BS in Electrical Engineering from the Universidad Católica Nuestra Señora de la Asunción in Paraguay in 2001. This was followed by an M.Sc. and Ph.D. in 2005 and 2007 from the University of New Mexico. He developed parallel MR imaging reconstruction algorithms and fast spectroscopic imaging techniques during his Ph.D. work. He then became a post-doctoral research fellow at NYU Langone Health in 2008 followed by an Assistant and Associate Professor of Radiology in 2010 and 2016 respectively. During his time at NYU, Ricardo developed his seminal work on compressed sensing for dynamic MRI. In 2018, he accepted the position of Chief of Magnetic Resonance Imaging and Spectroscopy Physics at Memorial Sloan Kettering Cancer center until present. He then became Professor (Member) in Medical Physics and Radiology as well as Vice-Chair for Research in the Department of Medical Physics in 2022.
Distinctions:
- NIH R01 grant to develop real-time 4D MRI for adaptive radiotherapy of pancreatic cancer
- NIH R01 grant to develop quantitative MRI for assessment of cervical cancer
- Distinguished Investigator Award from the Academy of Radiology and Biomedical Imaging Research in December 2022
- Senior Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM) in June 2021
- The paper on XD-GRASP was the most cited paper in Magnetic Resonance in Medicine (MRM) for 2016 - MRM is the premier journal of MRI research
Selected Publications:
Otazo R, Kim D, Axel L, Sodickson DK. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med. 2010; 64(3):767-76. https://pubmed.ncbi.nlm.nih.gov/20535813/
Feng L, Block KT, Grimm R, Chandarana H, Kim S, Xu J, Axel L, Sodickson DK, Otazo R. Golden-Angle Radial Sparse Parallel MRI: Combination of compressed sensing, parallel imaging and golden-angle radial sampling for fast and flexible dynamic volumetric MRI. Magn Reson Med. 2014 Sep;72(3):707-17 https://pubmed.ncbi.nlm.nih.gov/24142845/
Otazo R, Candès E, Sodickson DK. Low-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components. Magn Reson Med. 2015 Mar;73(3):1125-36. https://pubmed.ncbi.nlm.nih.gov/24760724/
Wu C, Murray V, Siddiq SS, Tyagi N, Reyngold M, Crane C, Otazo R. Real-time 4D MRI using MR signature matching (MRSIGMA) on a 1.5T MR-Linac system. Phys Med Biol. 2023 Sep 12;68(18): 10.1088/1361-6560/acf3cc. https://pubmed.ncbi.nlm.nih.gov/37619588/
Murray V, Siddiq S, Crane C, El Homsi M, Kim TH, Wu C, Otazo R. Movienet: Deep space-time-coil reconstruction network without k-space data consistency for fast motion-resolved 4D MRI. Magn Reson Med. 2024 Feb;91(2):600-614. https://pubmed.ncbi.nlm.nih.gov/37849064/