TY - GEN
T1 - Dual Domain Motion Artifacts Correction for MR Imaging Under Guidance of K-space Uncertainty
AU - Wang, Jiazhen
AU - Yang, Yizhe
AU - Yang, Yan
AU - Sun, Jian
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Dual domain reconstruction
KW - K-space uncertainty
KW - Magnetic resonance imaging
KW - Motion artifacts correction
UR - https://www.scopus.com/pages/publications/85174734528
U2 - 10.1007/978-3-031-43999-5_28
DO - 10.1007/978-3-031-43999-5_28
M3 - 会议稿件
AN - SCOPUS:85174734528
SN - 9783031439988
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 302
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings
A2 - Greenspan, Hayit
A2 - Greenspan, Hayit
A2 - Madabhushi, Anant
A2 - Mousavi, Parvin
A2 - Salcudean, Septimiu
A2 - Duncan, James
A2 - Syeda-Mahmood, Tanveer
A2 - Taylor, Russell
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -