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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