Abstract #0425

Real-time processing of myelin water imaging using artificial neural network

Jieun Lee¹, Doohee Lee¹, Joon Yul Choi¹, Dongmyung Shin¹, Hyeong-Geol Shin¹, and Jongho Lee¹

¹Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of

In this study, a real-time processing method for GRASE myelin water imaging is proposed by using an artificial neural network. Two different networks, one pairing multi-echo measurement with myelin water fraction and the other pairing multi-echo measurement with T₂ distribution, were developed. Both networks took <1.5 sec for the whole brain processing (FOV = 240×180×112 mm³ and matrix = 160×120×28) with less than 5% error in white matter.

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