TR&D 1: Reimagining the Future of Scanning: Intelligent Image Acquisition, Reconstruction and Analysis

NIH RePORTER · NIH · P41 · $365,970 · view on reporter.nih.gov ↗

Abstract

TRD1 Project Summary 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 (TRD) 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. Working with a diverse set of collaborators around the world, TRD1 investigators have made substantial progress and significant contributions over the previous two funding periods since 2014. Building upon the foundation established in the previous funding periods, we are now poised to expand TRD1 into exciting new areas that we believe have the potential to help shape the future of rapid MRI. 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 to enable intelligent quantification in MRI. We will also diversify our rapid MRI methods to integrate multifaceted sampling schemes, such as spiral or rosette trajectories, to achieve more efficient and effective sampling for new MRI applications such as low-field imaging and diffusion imaging. Additionally, we will also explore a new area of rapid MRI exploiting longitudinal image memory, with a focus on emerging new applications such as MR-guided radiotherapy and longitudinal disease monitoring. These new developments will be closely connected with the other two TRDs and will provide key enabling technologies to our CPs and SPs. Successful completion of the projects proposed in TRD1 will result in new MRI techniques that enable rapid and multifaced data acquisition, as well as more intelligent image reconstruction, quantification and interpretation. These techniques, in turn, will help advance imaging research and enhance patient care.

Key facts

NIH application ID
10939347
Project number
2P41EB017183-11
Recipient
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
Principal Investigator
Li Feng
Activity code
P41
Funding institute
NIH
Fiscal year
2024
Award amount
$365,970
Award type
2
Project period
2014-09-30 → 2029-05-31