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There is clinical evidence that suppressing the bone structures in X-rays (e.g., Chest X-rays (CXRs), pelvic X-rays (PXRs)) improves diagnostic value, either for radiologists or computer-aided diagnosis. However, bone-free CXRs are not always accessible. In this chapter, we explore the integration of 3D CT prior knowledge into X-ray imaging using generative adversarial networks (GANs) to address challenges posed by 2D projection superposition and improve diagnostic accuracy. First, we introduce the Decomposition GAN (DecGAN) designed for the anatomical decomposition of CXR images, leveraging unpaired CT data. DecGAN utilizes decomposition loss, adversarial loss, cycle consistency loss, and mask loss to ensure realistic anatomical separation of components such as bone, lung, and soft tissue. We can remove the bone components and get the bone-suppressed CXRs. Next, we propose a coarse-to-fine High-Resolution CXRs Suppression (HRCS) approach to suppress bone structures in high-resolution CXRs. By leveraging digitally reconstructed radiographs (DRRs) and domain adaptation techniques, this method mitigates domain differences between CXRs and CT-derived images. Experiments on benchmark datasets show that this method outperforms existing unsupervised bone suppression techniques and significantly reduces false-negative rates in lung disease diagnoses. Finally, we address the superposition problem in PXRs by introducing the Pelvis Extraction (PELE) module. This module, comprising a decomposition network, a domain adaptation network, and an enhancement module, utilizes 3D anatomical knowledge from CT scans to isolate the pelvis from PXR images, enhancing landmark detection. Evaluations of public and private datasets demonstrate that the PELE module significantly improves landmark detection accuracy, achieving state-of-the-art performance across several metrics. These approaches are based on similar principles but evaluated across different scenarios. The results demonstrate the potential to improve X-ray diagnosis at no extra cost by leveraging generative models enriched with CT knowledge.

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Embedding 3D CT Prior into X-ray Imaging Using Generative Adversarial Networks

  • 章节

  • 发表日期：

    2025年

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  • Han Li,
  • Zhen Huang,
  • S. Kevin Zhou

摘要

There is clinical evidence that suppressing the bone structures in X-rays (e.g., Chest X-rays (CXRs), pelvic X-rays (PXRs)) improves diagnostic value, either for radiologists or computer-aided diagnosis. However, bone-free CXRs are not always accessible. In this chapter, we explore the integration of 3D CT prior knowledge into X-ray imaging using generative adversarial networks (GANs) to address challenges posed by 2D projection superposition and improve diagnostic accuracy. First, we introduce the Decomposition GAN (DecGAN) designed for the anatomical decomposition of CXR images, leveraging unpaired CT data. DecGAN utilizes decomposition loss, adversarial loss, cycle consistency loss, and mask loss to ensure realistic anatomical separation of components such as bone, lung, and soft tissue. We can remove the bone components and get the bone-suppressed CXRs. Next, we propose a coarse-to-fine High-Resolution CXRs Suppression (HRCS) approach to suppress bone structures in high-resolution CXRs. By leveraging digitally reconstructed radiographs (DRRs) and domain adaptation techniques, this method mitigates domain differences between CXRs and CT-derived images. Experiments on benchmark datasets show that this method outperforms existing unsupervised bone suppression techniques and significantly reduces false-negative rates in lung disease diagnoses. Finally, we address the superposition problem in PXRs by introducing the Pelvis Extraction (PELE) module. This module, comprising a decomposition network, a domain adaptation network, and an enhancement module, utilizes 3D anatomical knowledge from CT scans to isolate the pelvis from PXR images, enhancing landmark detection. Evaluations of public and private datasets demonstrate that the PELE module significantly improves landmark detection accuracy, achieving state-of-the-art performance across several metrics. These approaches are based on similar principles but evaluated across different scenarios. The results demonstrate the potential to improve X-ray diagnosis at no extra cost by leveraging generative models enriched with CT knowledge.