Model Learning: Primal Dual Networks for Fast MR imaging
This addresses the slow imaging speed in MRI, which is a critical issue for medical diagnostics, though it appears incremental as it builds on existing optimization and deep learning techniques.
The paper tackles the problem of slow MRI by reconstructing images from highly undersampled k-space data, achieving superior reconstructions over state-of-the-art methods.
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.