Abstract
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 (D\(^2\)MC-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 D\(^2\)MC-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 D\(^2\)MC-Net outperforms state-of-the-art methods under different motion trajectories and motion severities.
J. Wang and Y. Yang—Both authors contributed equally to this work.
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Acknowledgements
This work is supported by National Key R &D Program of China (2022YFA1004201), National Natural Science Foundation of China (12090021, 12125104, 61721002, U20B2075).
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Wang, J., Yang, Y., Yang, Y., Sun, J. (2023). Dual Domain Motion Artifacts Correction for MR Imaging Under Guidance of K-space Uncertainty. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14229. Springer, Cham. https://doi.org/10.1007/978-3-031-43999-5_28
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