MRI motion correction via efficient residual-guided denoising diffusion probabilistic models

• URL: http://arxiv.org/abs/2505.03498v1
• Date: Tue, 06 May 2025 13:02:40 GMT
• Title: MRI motion correction via efficient residual-guided denoising diffusion probabilistic models
• Authors: Mojtaba Safari, Shansong Wang, Qiang Li, Zach Eidex, Richard L. J. Qiu, Chih-Wei Chang, Hui Mao, Xiaofeng Yang,
• Abstract summary: Methods: Res-MoCoDiff incorporates a novel residual error shifting mechanism in the forward diffusion process.<n>Training employs a combined l1+l2 loss, which promotes image sharpness and reduces pixel-level errors.<n>Results: The proposed method demonstrated superior performance in removing motion artifacts across all motion severity levels.
• Score: 4.304746362090954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Purpose: Motion artifacts in magnetic resonance imaging (MRI) significantly degrade image quality and impair quantitative analysis. Conventional mitigation strategies, such as repeated acquisitions or motion tracking, are costly and workflow-intensive. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model tailored for MRI motion artifact correction. Methods: Res-MoCoDiff incorporates a novel residual error shifting mechanism in the forward diffusion process, aligning the noise distribution with motion-corrupted data and enabling an efficient four-step reverse diffusion. A U-net backbone enhanced with Swin-Transformer blocks conventional attention layers, improving adaptability across resolutions. Training employs a combined l1+l2 loss, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on synthetic dataset generated using a realistic motion simulation framework and on an in-vivo dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and MT-DDPM using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE). Results: The proposed method demonstrated superior performance in removing motion artifacts across all motion severity levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to 41.91+-2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 seconds per batch of two image slices, compared with 101.74 seconds for conventional approaches.

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