Deformable Image Registration of Dark-Field Chest Radiographs for Local Lung Signal Change Assessment

TL;DR

This paper develops a deformable image registration method for dark-field chest radiographs to compare lung signals between different respiratory states, aiding lung function assessment.

Contribution

It introduces a novel registration framework specifically designed for dark-field radiographs to enable local lung signal comparison across breathing states.

Findings

Registration framework successfully aligns dark-field images from different respiratory states.

Regional lung signal changes can be reliably characterized using registered images.

Potential for improved dynamic lung function assessment using dark-field imaging.

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.