Abstract
Micro-computed tomography derived functional biomarkers used in lung disease research can significantly complement end-stage histomorphometric measures while also allowing for longitudinal studies. However, no approach for visualizing lung dynamics across a full respiratory cycle has yet been described. Using bleomycin-induced lung fibrosis and the antifibrotic drug nintedanib as a test model, we implemented a four-dimensional (4D) micro-CT imaging approach consisting of 30 reconstructed volumes per respiratory cycle, coupled with deep-learning-assisted segmentation of lung volumes. 4D micro-CT provided an accurate description of inhalatory and exhalatory lung dynamics under resting conditions and revealed an inflammation-related obstructive pattern at day 7, followed by a restrictive pattern associated with fibrosis development at day 21. A milder restriction and fibrotic pathology resulted from nintedanib treatment. The similarity of 4D micro-CT data with those produced by diagnostic measurements, also points to its great potential as an exploratory tool for the discovery of clinically relevant therapeutic compounds.