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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