Work

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

Year: 2025

Type: erratum

Abstract: 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.

Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition

DOI: 10.58530/2025/3980

Authors: Quanhao Sun, Yaqiong Chai, Michael Khoo, Hosung Kim

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W4414235797

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