MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
Authors:
Jinghang Li,
Tales Santini,
Courtney Clark,
Bruno de Almeida,
Cong Chu,
Salem Alkhateeb,
Andrea Sajewski,
Jacob Berardinelli,
Hecheng Jin,
Tobias Campos,
Jeremy J. Berardo,
Joseph Mettenburg,
Ariel Gildengers,
Howard J. Aizenstein,
Minjie Wu,
Tamer S. Ibrahim
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),…
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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 $ε^2$= 0.121-0.100 vs. 0.086-0.062, left-right hemispheres). Project page: https://jinghangli98.github.io/MRecover/
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Submitted 20 May, 2026;
originally announced May 2026.