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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