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Abstract #4307

Accelerate Pulmonary Hyperpolarized Gas MRI with Multi-Task Learning

Zimeng Li1,2, Sa Xiao2, Cheng Wang2, Chaohui Ye1,2, and Xin Zhou2
1School of Physics, Huazhong University of Science and Technology, Wuhan, China, 2Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, National Center for Magnetic Resonance in Wuhan, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China


Hyperpolarized gas MRI is a non-invasive and non-radiation imaging modality that can provide lung structure and function information. However, the problem of long imaging time limits its clinical application. Deep learning-based methods have shown great potential to accelerate MRI. In this work, we proposed a multi-task network to perform pulmonary hyperpolarized gas MRI reconstruction and lung region segmentation simultaneously, which allows sharing representations between two tasks. The results show that the proposed multi-task network has better reconstruction performance, stronger robustness and fewer model parameters than the comparison method.

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