The mGRE(multi-echo gradient echo) sequence has previously been used for MWF(Myelin water fraction) imaging. Such mGRE observes signal decay by obtaining multiple echo signals, but scan time increases as more echoes are obtained. To solve this trade-off, we developed a deep learning model based on a LSTM model that can reduce scan time by predicting the later echoes using only the early echoes. Looking at the in vivo results and various performance test, our network has lower RMSE and higher PSNR than NLLS(Non-linear least squares), a conventional fitting algorithm.
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