Peer-Reviewed Publication
Med Phys2025;52(8):e18023.August 1, 2025Journal Article
Deformable image registration of dark-field chest radiographs for functional lung assessment.
Fabian Drexel1,2,3, Vasiliki Sideri-Lampretsa4, Henriette Bast1,2,3, Alexander W Marka3, Thomas Koehler5,6, Florian T Gassert3, Daniela Pfeiffer3,6, Daniel Rueckert4,7,8, Franz Pfeiffer1,2,3,6
1Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, Germany.
2Munich Institute of Biomedical Engineering, Technical University of Munich, Garching, Germany.
3Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, TUM Klinikum, Technical University of Munich (TUM), Munich, Germany.
4Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital, Munich, Germany.
5Philips Innovative Technologies, Hamburg, Germany.
6Institute for Advanced Study, Technical University of Munich, Garching, Germany.
7Munich Center for Machine Learning (MCML), Munich, Germany.
8Department of Computing, Imperial College London, London, UK.
Abstract
BACKGROUND: 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, most previous studies of dark-field chest radiographs evaluated the lung signal only in the inspiratory breathing state.
PURPOSE: Our work aims to add a new perspective to these previous assessments…
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