用大规模预训练提升DWI图像畸变校正效果，适合临床高通量场景。
Large-Scale Pretraining for Improving Deep Learning-Based Geometric Distortion Correction of Diffusion-Weighted Imaging
- 将畸变校正建模为图像重建任务，采用自监督与生成式预训练方法。
- 预训练模型在定量和定性评估中均优于无预训练基线，最优模型为cWDM。
- 在低收入国家数据上存在迁移挑战，需统一预处理以提升跨域适用性。
扩散加权成像（DWI）广泛应用于临床，但易受几何畸变影响。传统校正方法常需额外扫描或厂商专用方案，限制了其在高通量、资源受限环境中的可行性。本研究探讨大规模预训练策略是否能提升单相编码DWI的深度学习畸变校正性能。将任务定义为图像重建，对比非预训练基线与自监督、生成式预训练模型，通过定量图像相似性指标与定性专家评估进行验证。最佳模型在定量与定性评估中表现最优，即cWDM。进一步测试其在低收入国家（LMIC）采集数据上的可迁移性，发现存在对比度改变与过度依赖T1加权解剖结构的问题。将图像注册到标准空间后预测性能提升，表明标准化预处理有助于跨域部署。
原文摘要 · Abstract (English)
Diffusion-weighted imaging (DWI) is widely used in clinical settings but remains vulnerable to geometric distortion. Conventional correction methods often require additional acquisitions or vendor-specific solutions, limiting their feasibility in high-throughput, resource-constrained settings. This study investigates whether large-scale pretraining strategies can improve deep learning-based distortion correction for single-phase-encoding DWI. We formulate the task as image reconstruction, and compare a non-pretrained baseline against a self-supervised and a generative pretrained model, evaluated using both quantitative image-similarity metrics and qualitative expert assessment. The best-performing model was further tested for transferability on data collected in an LMIC setting with acquisition shift. Pretrained models outperformed the non-pretrained baseline, with cWDM achieving the strongest results across both quantitative and qualitative evaluation. However, application to LMIC data revealed transferability challenges, including contrast alteration and over-reliance on T1-weighted anatomical structure. Registering images to a common standard space improved predictions, suggesting that harmonized preprocessing may enhance cross-domain deployment.
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