Objective.Motion artifacts (ARTs) in brain magnetic resonance imaging (MRI), mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these ARTs, including repeated acquisitions or motion tracking, impose workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion ART correction.Approach.Res-MoCoDiff exploits a novel residual error shifting mechanism during the forward diffusion process to incorporate information from motion-corrupted images. This mechanism allows the model to simulate the evolution of noise with a probability distribution closely matching that of the corrupted data, enabling a reverse diffusion process that requires only four steps. The model employs a U-net backbone, with attention layers replaced by Swin Transformer blocks, to enhance robustness across resolutions. Furthermore, the training process integrates a combinedℓ1+ℓ2loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both anin-silicodataset generated using a realistic motion simulation framework and anin-vivomovement-related ARTs dataset. Comparative analyses were conducted against established methods, including cycle generative adversarial network, Pix2pix, and a diffusion model with a vision transformer backbone, using quantitative metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and normalized mean squared error (NMSE).Main results.The proposed method demonstrated superior performance in removing motion ARTs across minor, moderate, and heavy distortion levels. Res-MoCoDiff consistently achieved the highest SSIM and the lowest NMSE values, with a PSNR of up to41.91±2.94 dB for minor distortions. Notably, the average sampling time was reduced to 0.37 s per batch of two image slices, compared with 101.74 s for conventional approaches.Significance.Res-MoCoDiff offers a robust and efficient solution for correcting MRI motion ARTs, preserving fine structural details while significantly reducing computational overhead. Its speed and restoration fidelity underscore its potential for integration into clinical workflows, enhancing diagnostic accuracy and patient care.
Keywords: MRI; MoCo; deep learning; diffusion model; efficient; motion correction.
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