Chest X-rays (CXRs) are commonly utilized as a low-dose modality for lung
screening. Nonetheless, the efficacy of CXRs is somewhat impeded, given that
approximately 75% of the lung area overlaps with bone, which in turn hampers
the detection and diagnosis of diseases. As a remedial measure, bone
suppression techniques have been introduced. The current dual-energy
subtraction imaging technique in the clinic requires costly equipment and
subjects being exposed to high radiation. To circumvent these issues, deep
learning-based image generation algorithms have been proposed. However,
existing methods fall short in terms of producing high-quality images and
capturing texture details, particularly with pulmonary vessels. To address
these issues, this paper proposes a new bone suppression framework, termed
BS-Diff, that comprises a conditional diffusion model equipped with a U-Net
architecture and a simple enhancement module to incorporate an autoencoder. Our
proposed network cannot only generate soft tissue images with a high bone
suppression rate but also possesses the capability to capture fine image
details. Additionally, we compiled the largest dataset since 2010, including
data from 120 patients with high-definition, high-resolution paired CXRs and
soft tissue images collected by our affiliated hospital. Extensive experiments,
comparative analyses, ablation studies, and clinical evaluations indicate that
the proposed BS-Diff outperforms several bone-suppression models across
multiple metrics.

该论文提出了一种新的骨抑制框架，称为 BS-Diff，该框架采用条件扩散模型和 U-Net 架构，利用自编码器进行增强，能够生成高骨抑制率的软组织图像，并能够捕捉细微的图像细节，经过广泛的实验、比较分析、消融研究和临床评估，该模型在多个指标上优于其他骨抑制模型。