GRASP-Pro+: GRASP reconstruction with locally low-rank subspace constraint for DCE-MRI
Eddy Solomon1, Jonghyun Bae1,2, Elcin Zan2, Linda Moy2, Yulin Ge2, Li Feng3, and Gene Sungheon Kim1
1Department of Radiology, MRI Research Institute, Weill Cornell Medicine, New York, NY, United States, 2Department of Radiology, New York University, New York, NY, United States, 3BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
While the globally low-rank (GLR) model has been demonstrated to be effective in representing global contrast change, it is expected to be less effective for spatially localized signal dynamics. In this work, we propose an improved reconstruction framework, which extends GRASP-Pro using a locally low-rank (LLR) model to represent spatially localized dynamics based on clusters or patches information. This approach has been tested in multiple DCE applications including both cancer and healthy subjects. In addition, we propose an anatomical cluster-based reconstruction approach for brain DCE-MRI.
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