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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