通过配准技术分析呼吸状态间肺部暗场信号变化，助力动态肺功能评估。
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
- 采用图像配准对不同呼吸状态的暗场胸片进行空间对齐
- 在慢阻肺患者数据中验证了配准框架的有效性
- 为肺功能动态评估提供新方法，适合影像组学与呼吸病研究者
人胸部暗场放射成像展现出评估肺微结构及诊断呼吸系统疾病的重要潜力。然而，以往研究仅在吸气状态下分析肺部信号。本研究旨在通过局部比较不同呼吸状态下的暗场肺信号，拓展先前评估视角。为此，我们探讨适用于暗场胸片的图像配准方法，以实现不同呼吸状态下肺部的精确空间对齐。基于一项慢性阻塞性肺疾病临床研究中的全吸气与呼气扫描数据，我们评估了所提配准框架性能，并提出相应的评价方法。区域化分析显示，不同呼吸状态下肺暗场信号的变化具有可检测性，证明了结合注册后的暗场图像与常规胸片进行动态放射学肺功能评估的可行性。
原文摘要 · Abstract (English)
Dark-field radiography of the human chest has been demonstrated to have promising potential for the analysis of the lung microstructure and the diagnosis of respiratory diseases. However, previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state. Our work aims to add a new perspective to these previous assessments by locally comparing dark-field lung information between different respiratory states. To this end, we discuss suitable image registration methods for dark-field chest radiographs to enable consistent spatial alignment of the lung in distinct breathing states. Utilizing full inspiration and expiration scans from a clinical chronic obstructive pulmonary disease study, we assess the performance of the proposed registration framework and outline applicable evaluation approaches. Our regional characterization of lung dark-field signal changes between the breathing states provides a proof-of-principle that dynamic radiography-based lung function assessment approaches may benefit from considering registered dark-field images in addition to standard plain chest radiographs.
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