EigenGRASP: Subject-Specific Temporal-Learning Radial Sampling Image Reconstruction in Dynamic Contrast-Enhanced MRI
Ramin Jafari1, Masoud Zarepisheh1, Richard Kinh Gian Do2, and Ricardo Otazo1
1Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Memorial Sloan Kettering Cancer Center, New york, NY, United States
GRASP is an image reconstruction algorithm for free-breathing dynamic contrast-enhanced MRI which uses universal L1-type regularization to suppress undersampling artifacts. We propose to replace it with a subject-specific data-driven L2-type regularization which can improve image quality and decrease reconstruction time.
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