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Abstract #2991

T2WI liver MRI with deep learning-based reconstruction: a clinical feasibility study in comparison to conventional T2WI liver MRI

Ruofan Sheng1, Liyun Zheng2, Shu Liao3, Yongming Dai2, and Mengsu Zeng1
1Department of Radiology, Zhongshan Hospital, Shanghai, China, 2United Imaging Healthcare, Shanghai, China, 3Shanghai United Imaging Intelligence, Shanghai, China

Liver magnetic resonance imaging (MRI) is limited by several technical challenges, including relatively long acquisition time and respiratory motion artifacts. Recently, deep learning methods have been proposed to reconstruct undersampled k-space data by training deep neural networks. In this study, we raised a U-net convolutional neural network architecture to improve the reconstruction speed and image quality of liver T2-weighted MRI. This technique was able to cover the whole liver during one breath hold and showed promising performance in image quality and lesion detectability.

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