The broad mission of our Center for Advanced Imaging Innovation and Research (CAI2R) is to bring together collaborative translational research teams for the development of high-impact biomedical imaging technologies, with the ultimate goal of changing day-to-day clinical practice. Technology Research and Development (TR&D) Project 1 aims to replace traditional complex and inefficient imaging protocols with simple, comprehensive acquisitions that also yield quantitative parameters sensitive to specific disease processes. In the first funding period of this P41 Center, our project team led the way in establishing rapid, continuous, comprehensive imaging methods, which are now available on a growing number of commercial magnetic resonance imaging (MRI) scanners worldwide. This foundation will allow us, in the proposed research plan for the next period, to enrich our data streams, to advance the extraction of actionable information from those data streams, and to feed the resulting information back into the design of our acquisition software and hardware. Thanks to developments during our first funding period, we are now in a position to question long-established assumptions about scanner design, originating from the classical imaging pipeline of human radiologists interpreting multiple series of qualitative images. We will reimagine the process of MR scanning, leveraging our core expertise in pulse-sequence design, parallel imaging, compressed sensing, model-based image reconstruction and machine learning. We will also extend our methods to complex multifaceted data streams, arising not only from MRI but also from Positron Emission Tomography (PET) and other imaging modalities, as well as from diverse arrays of complementary sensors.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
2P41EB017183-06
Application #
9804440
Study Section
Special Emphasis Panel (ZEB1)
Project Start
2014-09-30
Project End
2024-07-31
Budget Start
2019-08-01
Budget End
2020-07-31
Support Year
6
Fiscal Year
2019
Total Cost
Indirect Cost
Name
New York University
Department
Type
DUNS #
121911077
City
New York
State
NY
Country
United States
Zip Code
10016
Feng, Li; Coppo, Simone; Piccini, Davide et al. (2018) 5D whole-heart sparse MRI. Magn Reson Med 79:826-838
Benkert, Thomas; Tian, Ye; Huang, Chenchan et al. (2018) Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding. Magn Reson Med 80:286-293
Wake, Nicole; Chandarana, Hersh; Rusinek, Henry et al. (2018) Accuracy and precision of quantitative DCE-MRI parameters: How should one estimate contrast concentration? Magn Reson Imaging 52:16-23
Lee, Hong-Hsi; Sodickson, Daniel K; Lattanzi, Riccardo (2018) An analytic expression for the ultimate intrinsic SNR in a uniform sphere. Magn Reson Med 80:2256-2266
Lattanzi, Riccardo; Zhang, Bei; Knoll, Florian et al. (2018) Phase unwinding for dictionary compression with multiple channel transmission in magnetic resonance fingerprinting. Magn Reson Imaging 49:32-38
Madelin, Guillaume; Xia, Ding; Brown, Ryan et al. (2018) Longitudinal study of sodium MRI of articular cartilage in patients with knee osteoarthritis: initial experience with 16-month follow-up. Eur Radiol 28:133-142
Sbrizzi, Alessandro; Heide, Oscar van der; Cloos, Martijn et al. (2018) Fast quantitative MRI as a nonlinear tomography problem. Magn Reson Imaging 46:56-63
Lakshmanan, Karthik; Brown, Ryan; Madelin, Guillaume et al. (2018) An eight-channel sodium/proton coil for brain MRI at 3 T. NMR Biomed 31:
Winters, Kerryanne V; Reynaud, Olivier; Novikov, Dmitry S et al. (2018) Quantifying myofiber integrity using diffusion MRI and random permeable barrier modeling in skeletal muscle growth and Duchenne muscular dystrophy model in mice. Magn Reson Med 80:2094-2108
Hammernik, Kerstin; Klatzer, Teresa; Kobler, Erich et al. (2018) Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 79:3055-3071

Showing the most recent 10 out of 168 publications