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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