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Abstract #1881

Zero-shot self-supervised deep learning reconstruction for abdominal DCE MR multitasking

Zihao Chen1,2,3, Ruofan Sheng4, Kaipu Jin4, Shihong Han5, Jian Xu1, Mengsu Zeng4, Debiao Li2,3, and Qi Liu1
1UIH America, Inc., Houston, TX, United States, 2Cedars-Sinai Medical Center, Los Angeles, CA, United States, 3University of California, Los Angeles, Los Angeles, CA, United States, 4Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China, 5United Imaging Healthcare, Shanghai, China

Synopsis

Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, MR Multitasking, DCE MRI, self-supervised learning, zero-shot learning

Motivation: Deep learning (DL) MR multitasking reconstruction can reduce the reconstruction time, but previous methods are supervised learning, which may learn artifacts from the reference images.

Goal(s): Our goal was to develop a DL reconstruction method that can improve image quality beyond supervised DL and conventional iterative reconstruction.

Approach: We developed a zero-shot self-supervised deep learning method for DCE MR multitasking reconstruction.

Results: With shorter reconstruction time than conventional iterative reconstruction, the proposed method obtained better image quality than both supervised DL and conventional iterative reconstruction methods.

Impact: With the proposed method, DCE MR multitasking can have better image quality with shorter reconstruction time than previous iterative reconstruction, which is essential for the potential clinical application of the motion-resolved and high spatial-temporal-resolution abdominal DCE MR multitasking.

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Keywords