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Abstract #4899

Feasibility study on artificial neural network based myelin water fraction mapping

Soozy Jung1, Hongpyo Lee1, Kanghyun Ryu1, Jaeeun Song1, Yoonho Nam2, Hojoon Lee3,4, and Donghyun Kim1

1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Korea, Republic of, 2Seoul St.Mary's Hospital, The Catholic University of korea, Seoul, Korea, Republic of, 3Department of Radiology and Research Institure of Radiological Science, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea, Republic of, 4Department of Radiology, Inje University College of Medicine, Haeundae Paik Hospital, Busan, Korea, Republic of

We developed an artificial neural network (ANN) using magnitude 3-pool signal model based training sets. Simulations were performed for evaluation with various SNR and slice inhomogeneity (GZ) levels. Two in-vivo data sets were tested. The results show decreased mean error and standard deviation when using the ANN model. The ANN model was more stable than the fitting method for different GZ values. Moreover, the processing time of the ANN model took 140 times less than the fitting method.

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