DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
Journal:
arXiv
Published Date:
Apr 9, 2025
Abstract
Motion artifacts remain a significant challenge in Magnetic Resonance Imaging
(MRI), compromising diagnostic quality and potentially leading to misdiagnosis
or repeated scans. Existing deep learning approaches for motion artifact
correction typically require paired motion-free and motion-affected images for
training, which are rarely available in clinical settings. To overcome this
requirement, we present DIMA (DIffusing Motion Artifacts), a novel framework
that leverages diffusion models to enable unsupervised motion artifact
correction in brain MRI. Our two-phase approach first trains a diffusion model
on unpaired motion-affected images to learn the distribution of motion
artifacts. This model then generates realistic motion artifacts on clean
images, creating paired datasets suitable for supervised training of correction
networks. Unlike existing methods, DIMA operates without requiring k-space
manipulation or detailed knowledge of MRI sequence parameters, making it
adaptable across different scanning protocols and hardware. Comprehensive
evaluations across multiple datasets and anatomical planes demonstrate that our
method achieves comparable performance to state-of-the-art supervised
approaches while offering superior generalizability to real clinical data. DIMA
represents a significant advancement in making motion artifact correction more
accessible for routine clinical use, potentially reducing the need for repeat
scans and improving diagnostic accuracy.
