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Abstract #0673

DeepRespi: Retrospective correction for respiration-induced B0 fluctuation artifacts using deep learning

Hongjun An1, Hyeong-Geol Shin1, Woojin Jung1, and Jongho Lee1
1Department of Electrical and computer Engineering, Seoul National University, Seoul, Korea, Republic of

B0 fluctuation from respiration can induce significant artifacts in MRI images. In this study, a new retrospective correction method that requires no modification in sequences (e.g. no navigator) is proposed. This method utilizes a convolution neural network (CNN), DeepRespi, to extract a respiration pattern from a corrupted image. The respiration pattern is applied back to the corrupted image for phase compensation. When tested, the CNN successfully extracted the respiration pattern (correlation coefficient = 0.94 ± 0.04) and the corrected images showed on average 68.9 ± 13.2% reduction in NRMSE when comparing the corrupted vs. corrected images.

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