MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
MRecover is a conditional generative model that synthesizes T2w TSE images from routinely acquired T1w MRI to recover motion-corrupted hippocampal subfield segmentation. Trained on 7T data (n=577), it achieved high in-domain fidelity (SSIM=0.84, FSIM=0.94) and generalized well to 3T data. In the ADNI3 dataset, it increased analyzable subjects by 31.8% (593 vs 450) and improved effect sizes for diagnostic group differences.
[2605.21669] MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
[Submitted on 20 May 2026]
Title:MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
View a PDF of the paper titled MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast, by Jinghang Li and 15 other authors
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Abstract:Hippocampal subfield segmentation requires high-resolution T2w turbo spin echo (TSE) MRI, yet this sequence is susceptible to motion artifacts, leading to substantial data loss. We developed a conditional generative model (MRecover) that synthesizes routinely acquired T1w images to create TSE images with autoregressive slice conditioning for volumetric consistency. Trained on 7T MRI data (n=577), the model achieved high in-domain fidelity (n=148, SSIM=0.84, FSIM=0.94) and generalized well to out-of-domain 3T data: subfield volumes from synthesized and the as-acquired images closely matched: (n=416, r=0.87-0.97) and yielded 31.8% more analyzable subjects in the motion-affected ADNI3 dataset after quality control (593 vs 450). The synthesized images also achieved larger effect sizes due to increasing the sample size for diagnostic group differences in hippocampal subfield atrophy (whole hippocampus $\epsilon^2$= 0.121-0.100 vs. 0.086-0.062, left-right hemispheres). Project page: this https URL
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.21669 [cs.CV]
(or arXiv:2605.21669v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.21669
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jinghang Li [view email] [v1] Wed, 20 May 2026 19:23:03 UTC (18,550 KB)
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