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Abstract #0328

DeepTSE-T2: Deep learning-powered T2 mapping with B1+ estimation using a product double-echo Turbo Spin Echo sequence

Hwihun Jeong1, Hyeong-Geol Shin1, Sooyeon Ji1, Jinhee Jang2, Hyun-Soo Lee3, Yoonho Nam4, and Jongho Lee1
1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea, Republic of, 2Department of Radiology, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea, Republic of, 3Siemens healthineers Ltd, Seoul, Korea, Republic of, 4Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin, Korea, Republic of

We developed DeepTSE-T2, a deep learning-based T2 mapping algorithm with retrospective B1+ estimation for a product double-echo TSE sequence. DeepTSE-T2 enables T2 mapping by retrospectively estimating B1+ information, reconstructing T2 in high-accuracy (NRMSE = 8.26 ± 0.30%). The proposed method is useful in a clinical setting since it utilizes a fast imaging product sequence. The training dataset consists of simulation-based data, providing flexibility in parameter setting. Applications to χ-separation and an MS patient are included.

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