Dual Domain Motion Artifacts Correction for MR Imaging Under Guidance of K-space Uncertainty

Magnetic resonance imaging (MRI) may degrade with motion artifacts in the reconstructed MR images due to the long acquisition time. In this paper, we propose a dual domain motion correction network ((DMC)-M-2-Net) to correct the motion artifacts in 2D multi-slice MRI. Instead of explicitly estimating the motion parameters, we model the motion corruption by k-space uncertainty to guide the MRI reconstruction in an unfolded deep reconstruction network. Specifically, we model the motion correction task as a dual domain regularized model with an uncertainty-guided data consistency term. Inspired by its alternating iterative optimization algorithm, the (DMC)-M-2-Net is composed of multiple stages, and each stage consists of a k-space uncertainty module (KU-Module) and a dual domain reconstruction module (DDR-Module). The KU-Module quantifies the uncertainty of k-space corruption by motion. The DDR-Module reconstructs motion-free k-space data and MR image in both k-space and image domain, under the guidance of the k-space uncertainty. Extensive experiments on fastMRI dataset demonstrate that the proposed (DMC)-M-2-Net outperforms state-of-the-art methods under different motion trajectories and motion severities.