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


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


    ISMRM 2019

    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.