用AI生成对比增强，修复运动伪影导致的脑部MRI数据丢失
MRecover: A Conditional Generative Model for Recovering Motion-Corrupted MR images Using AI Generated Contrast
- 基于条件生成模型，用T1w图像合成高质量TSE图像
- 在7T数据上训练，3T数据上验证，分析受试者数提升31.8%
- 适合神经影像研究者，尤其关注阿尔茨海默病早期检测
海马亚区分割需要高分辨率的T2w涡旋自旋回波（TSE）MRI，但该序列易受运动伪影影响，导致大量数据丢失。我们开发了条件生成模型MRecover，利用常规采集的T1w图像，通过自回归切片条件实现体积一致性，合成TSE图像。模型在7T MRI数据（n=577）上训练，域内保真度高（n=148，SSIM=0.84，FSIM=0.94），并在域外3T数据上表现良好：合成与原始图像的亚区体积相关性达r=0.87–0.97（n=416）。在运动干扰的ADNI3数据集中，经质量控制后可分析受试者从450增至593，提升31.8%。合成图像还因样本量增加，使诊断组间海马亚区萎缩效应量更大（全海马$ε^2$=0.121–0.100 vs. 0.086–0.062，左右半球）。
原文摘要 · Abstract (English)
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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