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Abstract #4132

Improving SASHA T1 precision via a deep-learning approach

Xiaofeng Qian1, Yingwei Fan1, Dongyue Si2, Yongsheng Jin3, Haiyan Ding2, and Rui Guo1
1Shool of Medical Technology, Beijing Institute of Technology, Beijing, China, 2Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 3Department of Infectious Diseases, The Affiliated Hospital of Yan’an University, Shanxi, China

Synopsis

Keywords: Myocardium, Machine Learning/Artificial Intelligence, SASHA

SASHA with a 3-parameter fitting model has high T1 accuracy but low precision due to low SNR in saturation-recovery T1-weighted images. Alternatively, a two-parameter model could improve the precision with the penalty of losing accuracy. In this study, we developed a 1D neural network (DeepFittingNet) to predict SASHA T1 and alleviate the impaction from noise. We trained DeepFittingNet using simulation of SASHA with different Rician noise levels and tested it using in-vivo MR data. Results showed that T1 from DeepFittingNet had high precision and comparable accuracy to that of the 3-parameter fitting.

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Abstract Presentation Video

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