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 optimi-zation 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. Experi-ments on in vivo MR data demonstrate that the proposed method achieves supe-rior MR reconstructions from highly undersampled k-space data over other state-of-the-art image reconstruction methods.
@article{arxiv.1908.02426,
title = {Model Learning: Primal Dual Networks for Fast MR imaging},
author = {Jing Cheng and Haifeng Wang and Leslie Ying and Dong Liang},
journal= {arXiv preprint arXiv:1908.02426},
year = {2019}
}
Comments
accepted in MICCAI2019. arXiv admin note: text overlap with arXiv:1906.08143