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arXiv · 1905.09525

Accelerating MR Imaging via Deep Chambolle-Pock Network

Haifeng Wang·Jing Cheng·Sen Jia·Zhilang Qiu·Caiyun Shi·Lixian Zou·Shi Su·Yuchou Chang·Yanjie Zhu·Leslie Ying·Dong Liang
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Abstract

Compressed sensing (CS) has been introduced to accelerate data acquisition in MR Imaging. However, CS-MRI methods suffer from detail loss with large acceleration and complicated parameter selection. To address the limitations of existing CS-MRI methods, a model-driven MR reconstruction is proposed that trains a deep network, named CP-net, which is derived from the Chambolle-Pock algorithm to reconstruct the in vivo MR images of human brains from highly undersampled complex k-space data acquired on different types of MR scanners. The proposed deep network can learn the proximal operator and parameters among the Chambolle-Pock algorithm. All of the experiments show that the proposed CP-net achieves more accurate MR reconstruction results, outperforming state-of-the-art methods across various quantitative metrics.

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Haifeng Wang, Jing Cheng, Sen Jia, Zhilang Qiu, Caiyun Shi, Lixian Zou, Shi Su, Yuchou Chang, Yanjie Zhu, Leslie Ying, Dong Liang. 2019-05-23. Accelerating MR Imaging via Deep Chambolle-Pock Network. https://arxiv.org/abs/1905.09525

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