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

Abstract

Purpose: Motion artifacts in magnetic resonance imaging (MRI) significantly degrade image quality and hinder quantitative downstream applications. Conventional methods to mitigate these artifacts, including repeated acquisitions or motion tracking, impose substantial financial and workflow burdens. This study introduces Res-MoCoDiff, an efficient denoising diffusion probabilistic model specifically designed for MRI motion artifact correction.

Methods: 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 conventional attention layers replaced by Swin Transformer blocks, to enhance robustness across various image resolutions. Furthermore, the training process integrates a combined ${\ell }_1+{\ell }_2$ loss function, which promotes image sharpness and reduces pixel-level errors. Res-MoCoDiff was evaluated on both an in-silico dataset generated using a realistic motion simulation framework and an in-vivo MR-ART dataset. Comparative analyses were conducted against established methods, including CycleGAN, Pix2pix, and a diffusion model with a vision transformer backbone (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 minor, moderate, and heavy distortion 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.

Conclusion: Res-MoCoDiff offers a robust and efficient solution for correcting MRI motion artifacts, preserving fine structural details while significantly reducing computational overhead. Its rapid processing speed and high restoration fidelity underscore its potential for seamless integration into clinical workflows, ultimately enhancing diagnostic and treatment accuracy and patient care.