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Abstract #2816

Automatic Selection of Optimal Regularization Parameters in Compressed Sensing using No Reference Magnetic Resonance Image Quality Assessment.

Kihun Bang1,2, Jinseong Jang1, Yohan Jun1,2, Hanbyol Jang1,2, Hojoon Lee3, and Dosik Hwang1

1Yonsei University, Seoul, Republic of Korea, 2Philips Korea, Seoul, Republic of Korea, 3Department of Radiology and Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul, Republic of Korea

Compressed Sensing can reconstruct image without artifacts from the undersampled data, however setting the regularization parameters in CS optimization problem is difficult. Empirically selected parameters or extracted from L-curve method have less reliability. This abstract proposes CS reconstructed MR image quality assessment without ground truth and it can select proper regularization parameters automatically much faster and much reliable.

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