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Abstract #2277

SafeNet: Artificial Neural Network for Real-Time T2 Mapping with Quality Assurance

Doohee Lee1, Woojin Jung1, Jingu Lee1, Jingyu Ko1, Hyeong-Geol Shin1, Hyunsung Eun1, Yoonho Nam2, and Jongho Lee1

1Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea, 2Department of Radiology, Seoul St.Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

Accurate T2 mapping using multi-echo spin-echo data is a time-consuming process due to stimulated echo correction. In this study, we developed an artificial neural network for real-time T2 mapping. The training dataset using both in-vivo data and model-based synthetic data demonstrated the best performance. The resulting T2 map shows mean T2 errors of less than 0.3 ms with minimal computation time (less than 1 sec as opposed to 8.3 hours for conventional method). An additional algorithm was developed to ensure the fidelity of the T2 map at the cost of slightly increased computation time.

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