AI 中文总结
针对胸部X光片中骨骼重叠掩盖异常及配对数据稀缺的问题，提出基于CT的DRR框架，通过骨骼分割和组件投影生成配对监督，实现无需真实配对数据的骨骼抑制，并在多个数据集上验证其有效性。
AI 中文摘要
骨骼重叠可能掩盖胸部X光片中的异常，而稀缺的配对训练数据限制了有监督的骨骼抑制。我们通过一个数字重建放射影像（DRR）框架来解决这一挑战，该框架将胸部计算机断层扫描（CT）转换为用于组件抑制的配对监督。一种新颖的骨骼分割算法使得CT能够分解为骨骼、非肺软组织以及肺组件，这些组件被分别投影。它们的加权组合生成合成X光片，其中像素配准的组件图像精确地相加为完整的DRR。在这些数据上训练的模型通过预测目标组件并通过减法恢复剩余部分来抑制骨骼或肺组件，无需真实配对训练数据即可迁移到真实X光片。作为扩展，它们在真实X光片上的输出为无配对、组件级DRR翻译提供了目标域，在保留解剖细节的同时缩小了外观差距。在多个公共数据集上，下游检测实验证明了骨骼抑制的实用性，其增益集中在与骨骼重叠显著的异常上。与应用于相同CT的开源DRR引擎相比，我们未修改的DRR实现了相当的逼真度和标签相关解剖结构的保留，而翻译后的DRR在评估方法中实现了最佳的弗雷歇初始距离（FID）、肺野清晰度以及与源CT解剖结构的一致性。模型和推理代码：此https URL 翻译后的投影：此https URL。
英文摘要
Bone overlap can obscure abnormalities in chest radiographs, while scarce paired training data limit supervised bone suppression. We address this challenge with a digitally reconstructed radiograph (DRR) framework that converts chest computed tomography (CT) into paired supervision for component suppression. A novel bone segmentation algorithm enables CT decomposition into bone, non-lung soft-tissue, and lung components, which are projected separately. Their weighted combination yields synthetic radiographs with pixel-registered component images that sum exactly to the full DRR. Models trained on these data suppress bone or lung components by predicting the target component and recovering the remainder by subtraction, transferring to real radiographs without real paired training data. As an extension, their outputs on real radiographs provide target domains for unpaired, component-wise DRR translation, reducing the appearance gap while retaining anatomical details. Across multiple public datasets, downstream detection experiments demonstrate the utility of bone suppression, with gains concentrated on abnormalities with substantial bone overlap. Compared with open-source DRR engines applied to the same CTs, our unmodified DRRs achieve comparable realism and preservation of label-relevant anatomy, while translated DRRs achieve the best Fréchet inception distance (FID), lung-field sharpness, and agreement with source-CT anatomy among the evaluated methods. Models and inference code: https://huggingface.co/qureaiorg/bone-suppression ; Translated projections: https://huggingface.co/datasets/qureaiorg/ct2xr-projections.
