Cytoarchitecture of SARS-CoV-2 infected hamster lungs by X-ray phase contrast tomography: imaging workflow and classification for drug testing

X-ray Phase Contrast Tomography (XPCT) based on wavefield propagation has been established as a high resolution three-dimensional (3D) imaging modality, suitable to reconstruct the intricate structure of soft tissues, and the corresponding pathological alterations. However, for biomedical research, more is needed than 3D visualisation and rendering of the cytoarchitecture in a few selected cases. First, the throughput needs to be increased to cover a statistically relevant number of samples. Second, the cytoarchitecture has to be quantified in terms of morphometric parameters, independent of visual impression. Third, dimensionality reduction and classification are required for identification of effects and interpretation of results. In this work, we present a workflow implemented at a laboratory CT setup, using semi-automated data acquisition, reconstruction and statistical quantification of lung tissue in an early screen of Covid-19 drug candidates. Different drugs were tested in a hamster model after SARS-CoV-2 infection. To make full use of the recorded high-throughput XPCT data, we then used morphometric parameter determination followed by a dimensionality reduction and classification based on optimal transport. This approach allows efficient discrimination between physiological and pathological lung structure, thereby providing invaluable insights into the pathological progression and partial recovery due to drug treatment.