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Abstract #3490

Low-rank based compressed sesning in low-field MRI for stroke

Fangge Chen1, Zheng Xu1, Yucheng He1, and Liang Tan2
1State Key Laboratory of Power Transmission Equipment and System Security and New Technology, Chongqing University, Chongqing, China, 2Department of Neurosurgery, Southwest Hospital, Chongqing, China

While being of great worth in convenience and timely scanning for severe disease, low-field and accessible MRI suffer from long scanning time. To speed up the scanning process in low-field MRI, a low-rank based compressed sensing method with a non-convex reconstruction model is proposed and solved by weighted SVT and gradient descent method iteratively. The in vivo data acquired from a Hemorrhage patient at a 0.05T MRI scanner is used for simulation. The reconstructed image (20% sampling rate) reveals the same hemorrhage shape as CT image shows, demonstrating the ability of compressed sensing applied in low-field MRI cliniclly.

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