Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
This work addresses the need for localized lung signal analysis in respiratory disease diagnosis, but it is incremental as it builds on existing dark-field radiography methods by adding registration for dynamic states.
The study tackled the problem of assessing lung microstructure changes between different respiratory states using dark-field chest radiographs, by developing an image registration framework to align images from inspiration and expiration scans, demonstrating a proof-of-principle for improved lung function assessment.
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.