暗场胸部X光片的可变形图像配准用于局部肺信号变化评估
Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
AI总结：
本研究提出暗场胸部X光片的可变形配准方法，实现不同呼吸状态下肺信号的局部比较，为动态肺功能评估提供新视角。
摘要：
人体胸部的暗场X光摄影已被证明在肺微结构分析和呼吸系统疾病诊断方面具有巨大潜力。然而，以往关于暗场胸部X光片的研究仅评估了吸气呼吸状态下的肺信号。我们的工作旨在通过局部比较不同呼吸状态之间的暗场肺信息，为这些以往的评估增加新的视角。为此，我们探讨了适用于暗场胸部X光片的图像配准方法，以实现肺在不同呼吸状态下的一致空间对齐。利用来自一项临床慢性阻塞性肺疾病研究的完全吸气和呼气扫描，我们评估了所提出的配准框架的性能，并概述了适用的评估方法。我们对呼吸状态之间肺暗场信号变化的区域特征描述提供了一个原理验证，表明基于动态X光摄影的肺功能评估方法可能受益于在标准平片胸部X光片之外考虑配准后的暗场图像。
英文摘要：
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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