Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment
Journal:
arXiv
Published Date:
Jan 18, 2025
Abstract
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.
