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Abstract #2250

Coherence Regularization for Compressed Sensing MRI Reconstruction with a Nonlocal Operator

Xiao Wang1, Enhao Gong1, Zhengwei Zhou1, Sheng Fang2, Kui Ying3, Shi Wang3

1Biomedical Engineering, Tsinghua University, Beijing, China; 2Institute of nuclear and new energy technology, Tsinghua University, Beijing, China; 3Engineering physics, Tsinghua University, Beijing, China

Compressed sensing (CS) is a newly developed method which can reduce the scan time of MR imaging. A new method based on coherence assumption and nonlocal operator (CORNOL) is proposed. We implemented CORNOL instead of Total-Variation (TV) on the reconstruction of CS. Thus only intrastructure intensity changes are penalized while the interstructure intensity changes are preserved. The result demonstrates both noise penalization and detail structure preserving character. Phantom simulation and in-vivo data shows the validity and advantages of our method.