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Abstract #2794

Synthetic FLAIR image from multi-echo GRE using U-Net

Jiyong Park1, Kanghyun Ryu1, Yoonho Nam2, Jaewook Shin1, Jaeho Lee1, and Dong-Hyun Kim1

1School of Electrical and & Electronic Engineering, Yonsei University, Seoul, Republic of Korea, 2Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

The fluid-attenuated inversion recovery(FLAIR) image is one of the most frequently scanned images useful for detecting and diagnosing various lesions. The FLAIR technique suppresses cerebrospinal fluid(CSF) signal by using specific TR and long TE. The WM-GM contrast is similar to the T2-weighted image, except that CSF signal is suppressed. Multi-echo GRE(mGRE) has increasingly been used for medical diagnosis. Here, we used the mGRE images to create a synthetic FLAIR image using deep learning.

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