Accelerating MR Imaging via Deep Chambolle-Pock Network

Autor:	Wang, Haifeng, Cheng, Jing, Jia, Sen, Qiu, Zhilang, Shi, Caiyun, Zou, Lixian, Su, Shi, Chang, Yuchou, Zhu, Yanjie, Ying, Leslie, Liang, Dong
Rok vydání:	2019
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 4 pages, 5 figures, 1 table, Accepted at 2019 IEEE 41st Engineering in Medicine and Biology Conference (EMBC 2019)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1905.09525 Zobrazit plný text záznamu View this record from Arxiv