Res-MoCoDiff: Residual-guided diffusion models for motion artifact correction in brain MRI

TL;DR

Res-MoCoDiff is a novel diffusion-based model that effectively corrects motion artifacts in brain MRI images with high efficiency, outperforming existing methods in quality and speed.

Contribution

The paper introduces Res-MoCoDiff, a diffusion model with a residual error shifting mechanism and Swin Transformer blocks, offering a fast and robust solution for MRI motion artifact correction.

Findings

Outperforms existing methods in SSIM, NMSE, and PSNR metrics.

Achieves correction in just 0.37 seconds per batch, significantly faster than traditional approaches.

Demonstrates superior artifact removal across various distortion levels.

Abstract

Objective. Motion artifacts in brain MRI, mainly from rigid head motion, degrade image quality and hinder downstream applications. Conventional methods to mitigate these artifacts, 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 artifact 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 combined l1+l2 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, using quantitative metrics such as PSNR, SSIM, and NMSE.Main 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.