Anatomy-Decomposed Chest Computed Tomography (CT) Projections as Scalable Supervision for Bone Suppression in Chest Radiographs

2026-09-21

AI Summary

为解决胸部X光片中骨骼遮挡问题，本文提出了一种基于CT分解的DRR框架生成配对监督数据，训练模型以抑制骨骼或肺部成分。

Abstract

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.

Problem

• bone suppression
• chest radiographs
• paired training data
• computed tomography
• digitally reconstructed radiograph

Innovation

• Digitally Reconstructed Radiograph (DRR)
• Bone Segmentation
• Component Suppression
• Unpaired Translation
• Chest Radiographs