Abstract #1474

Highly accelerated T1Ρ imaging using kernel-based low-rank compressed sensing reconstruction in knees with and without osteoarthritis

Jeehun Kim^1,2, Chaoyi Zhang³, Mingrui Yang¹, Hongyu Li³, Mei Li¹, Richard Lartey¹, Leslie Ying^3,4, and Xiaojuan Li¹

¹Department of Biomedical Engineering, Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, ²Department of Electrical Engineering, Case Western Reserve University, Cleveland, OH, United States, ³Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States, ⁴Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States

The T_1ρ imaging is a promising biomarker for early diagnosis of osteoarthritis, but the application of the method is hindered by its long scan time. In this work, a novel compressed sensing algorithm based on kernel-based low-rank was proposed. The algorithm was evaluated with numerical simulation and volunteer scans, where the volunteers with and without osteoarthritis was scanned with prospective downsampling to evaluate the algorithm performance regarding the presence of pathology.

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