Abstract
Magnetic resonance imaging (MRI) is known to be a slow imaging modality and undersampling in k-space has been used to increase the imaging speed. However, image reconstruction from undersampled k-space data is an ill-posed inverse problem. Iterative algorithms based on compressed sensing have been used to address the issue. In this work, we unroll the iterations of the primal-dual hybrid gradient algorithm to a learnable deep network architecture, and gradually relax the constraints to reconstruct MR images from highly undersampled k-space data. The proposed method combines the theoretical convergence guarantee of optimization methods with the powerful learning capability of deep networks. As the constraints are gradually relaxed, the reconstruction model is finally learned from the training data by updating in k-space and image domain alternatively. Experiments on in vivo MR data demonstrate that the proposed method achieves superior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.
Jing Cheng and Haifeng Wang contribute equally to this paper.
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Acknowledgement
This work was supported in part by the National Science Foundation of China (U1805261, 81729003 and 61871373), Natural Science Foundation of Guangdong Province (2018A0303130132), and Strategic Priority Research Program of Chinese Academy of Sciences (XDB25000000).
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Cheng, J., Wang, H., Ying, L., Liang, D. (2019). Model Learning: Primal Dual Networks for Fast MR Imaging. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham. https://doi.org/10.1007/978-3-030-32248-9_3
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DOI: https://doi.org/10.1007/978-3-030-32248-9_3
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