OrthoDiffusion：一种通用的多任务扩散基础模型用于骨科MRI解释
OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation
AI总结：
OrthoDiffusion是一种基于扩散的多任务基础模型，通过自监督学习在膝关节MRI上预训练，实现11种结构分割和8种异常检测，具有跨关节的高泛化能力。
摘要：
骨科疾病代表了全球健康负担的重大问题，并是全球残疾的主要原因之一。尽管MRI对准确诊断至关重要，但其解释仍然极具挑战性。放射科医生必须在复杂的解剖结构中识别多种潜在异常，这需要大量专业知识且容易产生变异。我们开发了OrthoDiffusion，一种统一的扩散基础模型，用于多任务骨科MRI解释。该框架利用三个针对特定方向的3D扩散模型，在15948个未标记的膝关节MRI扫描中以自监督方式预训练，以学习从冠状面、矢状面和轴向面中稳健的解剖特征。这些视图特定的表示被整合以支持多种临床任务，包括解剖分割和多标签诊断。我们的评估表明，OrthoDiffusion在11种膝关节结构的分割和8种膝关节异常的检测中表现出色。该模型在不同临床中心和MRI磁场强度下表现出显著的鲁棒性，始终优于传统监督模型。值得注意的是，在标记数据稀缺的设置中，OrthoDiffusion仅使用10%的训练标记就能保持高诊断精度。此外，从膝关节成像中学习的解剖表示在转移到其他关节时表现出高度可转移性，在11种踝关节和肩关节疾病中实现了强劲的诊断性能。这些发现表明，基于扩散的基础模型可以作为多疾病诊断和解剖分割的统一平台，可能提高骨科MRI解释在真实临床工作流程中的效率和准确性。
英文摘要：
Musculoskeletal disorders represent a significant global health burden and are a leading cause of disability worldwide. While MRI is essential for accurate diagnosis, its interpretation remains exceptionally challenging. Radiologists must identify multiple potential abnormalities within complex anatomical structures across different imaging planes, a process that requires significant expertise and is prone to variability. We developed OrthoDiffusion, a unified diffusion-based foundation model designed for multi-task musculoskeletal MRI interpretation. The framework utilizes three orientation-specific 3D diffusion models, pre-trained in a self-supervised manner on 15,948 unlabeled knee MRI scans, to learn robust anatomical features from sagittal, coronal, and axial views. These view-specific representations are integrated to support diverse clinical tasks, including anatomical segmentation and multi-label diagnosis. Our evaluation demonstrates that OrthoDiffusion achieves excellent performance in the segmentation of 11 knee structures and the detection of 8 knee abnormalities. The model exhibited remarkable robustness across different clinical centers and MRI field strengths, consistently outperforming traditional supervised models. Notably, in settings where labeled data was scarce, OrthoDiffusion maintained high diagnostic precision using only 10\% of training labels. Furthermore, the anatomical representations learned from knee imaging proved highly transferable to other joints, achieving strong diagnostic performance across 11 diseases of the ankle and shoulder. These findings suggest that diffusion-based foundation models can serve as a unified platform for multi-disease diagnosis and anatomical segmentation, potentially improving the efficiency and accuracy of musculoskeletal MRI interpretation in real-world clinical workflows.
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