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Abstract #1166

Sampling Pattern Optimization for Joint Acceleration of Multi-contrast MRI using Deep Learning

Sunghun Seo1, Huan Minh Luu1, Seung Hong Choi2, and Sung-Hong Park1
1Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea, Republic of


Usage of multiple-acquisition MRI is one field of study that proved its effectiveness and practicality since routine MR scan protocol typically acquires multiple information for the same anatomical structures. In this study, we propose simultaneous optimization of sampling pattern and reconstruction for joint acceleration of multi-contrast MRI. The simultaneous optimization of sampling pattern and reconstruction provided superior performance over single contrast imaging and over single sampling pattern for multi-contrast MRI. The proposed technique can be adopted in routine clinical scan without forcing extra scans during acquisition.

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