Assessing the use of Diffusion models for motion artifact correction in brain MRI
Journal:
arXiv
Published Date:
Feb 3, 2025
Abstract
Magnetic Resonance Imaging generally requires long exposure times, while
being sensitive to patient motion, resulting in artifacts in the acquired
images, which may hinder their diagnostic relevance. Despite research efforts
to decrease the acquisition time, and designing efficient acquisition
sequences, motion artifacts are still a persistent problem, pushing toward the
need for the development of automatic motion artifact correction techniques.
Recently, diffusion models have been proposed as a solution for the task at
hand. While diffusion models can produce high-quality reconstructions, they are
also susceptible to hallucination, which poses risks in diagnostic
applications. In this study, we critically evaluate the use of diffusion models
for correcting motion artifacts in 2D brain MRI scans. Using a popular
benchmark dataset, we compare a diffusion model-based approach with
state-of-the-art methods consisting of Unets trained in a supervised fashion on
motion-affected images to reconstruct ground truth motion-free images. Our
findings reveal mixed results: diffusion models can produce accurate
predictions or generate harmful hallucinations in this context, depending on
data heterogeneity and the acquisition planes considered as input.
