用对比学习统一不同扫描仪的脑部MRI图像，提升跨机构研究可比性。
Contrastive Anatomy-Contrast Disentanglement: A Domain-General MRI Harmonization Method
- 基于条件扩散自编码器与对比损失，分离解剖结构与成像对比度。
- 在未知扫描仪上实现+18%年龄预测准确率提升，旅行受试者数据上+7% PSNR。
- 无需微调即可适配新扫描仪，适合多中心和长期临床研究使用。
磁共振成像（MRI）在临床和研究中至关重要，但不同扫描仪及采集参数导致图像对比度差异，影响数据可比性和研究可重复性。现有方法需携带受试者或难以泛化到新场景。本文提出一种新方法：利用带对比损失的条件扩散自编码器与领域无关的对比增强，实现跨扫描仪的脑部MRI和谐化，同时保留个体解剖特征。该方法仅需单张参考图像即可合成目标扫描仪图像。在旅行受试者数据集上，相比基线提升+7% PSNR；在未见域的年龄回归任务中，性能提升+18%。模型无需微调即可稳健适配新扫描仪，显著提升多中心与纵向临床研究的数据可比性、可重复性与泛化能力，有助于改善医疗结果。
原文摘要 · Abstract (English)
Magnetic resonance imaging (MRI) is an invaluable tool for clinical and research applications. Yet, variations in scanners and acquisition parameters cause inconsistencies in image contrast, hindering data comparability and reproducibility across datasets and clinical studies. Existing scanner harmonization methods, designed to address this challenge, face limitations, such as requiring traveling subjects or struggling to generalize to unseen domains. We propose a novel approach using a conditioned diffusion autoencoder with a contrastive loss and domain-agnostic contrast augmentation to harmonize MR images across scanners while preserving subject-specific anatomy. Our method enables brain MRI synthesis from a single reference image. It outperforms baseline techniques, achieving a +7% PSNR improvement on a traveling subjects dataset and +18% improvement on age regression in unseen. Our model provides robust, effective harmonization of brain MRIs to target scanners without requiring fine-tuning. This advancement promises to enhance comparability, reproducibility, and generalizability in multi-site and longitudinal clinical studies, ultimately contributing to improved healthcare outcomes.
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