发表机构
The University of Chicago; Emory University; Georgia Institute of Technology; Memorial Sloan Kettering Cancer Center（芝加哥大学; 埃默里大学; 佐治亚理工学院; 纪念斯隆凯特琳癌症中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SSRL-MAR自监督框架，无需配对数据或运动标签，在模拟和活体数据集上实现3D脑部MRI运动伪影去除，性能接近理想监督模型，可提升神经解剖学一致性。
AI 中文摘要
患者运动仍是脑部MRI中图像退化的一个来源，会导致信号丢失、模糊和几何失真，损害定量分析。现有的运动校正深度学习方法通常依赖配对的干净-损坏数据或k空间采集，而这些在临床环境中很少可用。我们提出SSRL-MAR，一种感知运动伪影的无配对表示学习框架，用于运动伪影去除，既不需要配对训练数据，也不需要显式运动标签。SSRL-MAR采用三阶段训练策略：（1）对3D块进行对比学习，通过对比干净图像和合成损坏图像来提取运动表示；（2）一个感知运动伪影的合成网络，用于从干净扫描中生成运动伪影；（3）一个感知运动伪影的生成器，使用学习到的退化器进行自监督监督来恢复干净体积。在计算机模拟数据集上，SSRL-MAR达到PSNR 23.81dB，SSIM 91.55%，NMSE 0.79%。在活体MR-ART数据集上，预训练模型减少了运动失真，无监督域适应进一步提高了解剖保真度。与在相同模拟配对数据上训练的仅源域监督模型相比，SSRL-MAR在无监督域适应后在MR-ART上的PSNR提升了最多2.0dB，且与需要实际配对数据（实际中不可用）的理想监督模型的差距在0.25-0.47dB以内。在更轻度的运动水平下，胼胝体和脑室系统等结构的体积误差降低了50%以上，证实神经解剖学一致性得到改善。这些结果表明，SSRL-MAR为3D脑部MRI运动校正提供了一种稳健且可扩展的图像域解决方案，无需前瞻性采集配对数据或特定采集校准即可实现大规模神经影像学研究中可靠的结构量化。
英文摘要
Patient motion remains a source of image degradation in brain MRI, leading to signal loss, blurring, and geometric distortion that compromise quantitative analysis. Existing deep learning methods for motion correction typically rely on paired clean-corrupted data or k-space acquisitions, which are rarely available in clinical settings. We propose SSRL-MAR, a motion artifact-aware unpaired representation learning framework for motion artifact reduction that requires neither paired training data nor explicit motion labels. SSRL-MAR employed a three-stage training strategy: (1) contrastive learning on 3D patches to extract motion representations by contrasting clean and synthetically corrupted images, (2) a motion artifact-aware synthesis network to generate motion artifacts from clean scans, and (3) a motion artifact-aware generator to restore clean volumes using the learned degrader for self-supervised supervision. On in-silico dataset, SSRL-MAR achieved PSNR 23.81dB, SSIM 91.55%, and NMSE 0.79%. On in-vivo MR-ART dataset, the pretrained model reduced motion distortion, and unsupervised domain adaptation further improved anatomical fidelity. Against a source-only supervised model trained on the same simulated pairs, SSRL-MAR improved PSNR by up to 2.0 dB on MR-ART after unsupervised domain adaptation, and remained within 0.25-0.47 dB of an oracle supervised model that requires real paired data unavailable in practice. At the milder motion level, volumetric error in structures such as the corpus callosum and ventricular system decreased by more than 50%, confirming improved neuroanatomical consistency. These results indicate that SSRL-MAR provides a robust and scalable image-domain solution for 3D brain MRI motion correction, enabling reliable structural quantification in large-scale neuroimaging studies without requiring prospectively acquired pairs or acquisition-specific calibration.
