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Abstract #5635

SCOPE-T1ρ: Signal Compensation for Low-rank Plus Sparse Matrix Decomposition for Fast T1ρ Mapping

Yuanyuan Liu1, Yanjie Zhu1,2, Leslie Ying3, Yi-Xiang J Wang4, Jing Yuan5, Xin Liu1, and Dong Liang1

1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, United States, 3Department of Biomedical Engineering and Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, NY, United States, 4Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China, 5Medical Physics and Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, China

Quantitative T mapping typically requires the acquisition of multiple images with different spin-lock times, which greatly prolongs the scanning time, limiting its clinical applications. We developed a novel reconstruction method using a low-rank plus sparse model to obtain the parameter-weighted images from highly undersampled k-space data. This method exploited both the parameter-weighted image properties and priori information from the parameter model. Specifically, a signal compensation strategy was introduced to promote the low rankness along the parametric direction. The proposed method achieved a five-fold acceleration in the acquisition time and obtained more accurate T maps than the existing methods.

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