Correction of Motion-Affected MRI Images via Motion-Adaptive Diffusion Model

Motivation: Motion artifacts in MRI scans hinder diagnostic accuracy, especially in populations like infants and Parkinson's patients. Existing correction methods struggle to restore image quality in such cases fully. Goal(s): This study aims to develop a Motion-Adaptive Diffusion Model (MADM) to correct motion artifacts in MR, improving image quality. Approach: MADM is based on a diffusion model. Gaussian noise was added in the forward process, and a U-Net progressively denoises the images in reverse process. The model was trained on the MR-ART dataset. Results: MADM significantly outperformed traditional methods, reducing NMSE by 0.0226 and improving PSNR, SSIM, and CCC by 5.5558, 0.1160, and 0.0141. Impact: This project significantly improves MRI diagnostic accuracy by effectively correcting motion artifacts. It provides a more efficient and reliable solution for both clinical and research applications by reducing the need for repeat scans and enhancing image quality.