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Abstract #3982

Neural network for fast quantitative magnetization transfer imaging

Donghyun Kim1, Jae-Woong Kim1, Seung Hong Choi2, and Sung-Hong Park1

1Korea Advanced Institute of Science and Technology, Daejeon, Korea, Republic of, 2Seoul National University Hospital, Seoul, Korea, Republic of

Quantitative magnetization transfer (qMT) imaging provides quantitative information of macromolecular properties of tissues, but requires a long scan time. In this study, a neural network is proposed for the acceleration of qMT imaging. The network was trained to output the full 12 MT images (6 offset frequencies, 2 RF powers) from an input of only 4 MT images (2 offset frequencies, 2 RF powers). The qMT imaging with the neural network showed results comparable to those from the conventional qMT imaging with the full 12 MT images, indicating reduction in scan time by a factor of 3.

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