Longitudinal mesoscopic photoacoustic imaging of vascular networks requires accurate image co-registration to assess local changes in growing tumours, but remains challenging due to sparsity of data and scan-to-scan variability. Here, we compared a set of 5 curated co-registration methods applied to 49 pairs of vascular images of mouse ears and breast cancer xenografts. Images were segmented using a generative adversarial network and pairs of images and/or segmentations were fed into the 5 tested algorithms. We show the feasibility of co-registering vascular networks accurately using a range of quality metrics, taking a step towards longitudinal characterization of those complex structures.
Here, we assess the capabilities of photoacoustic imaging (PAI) biomarkers to shed light into perfusion-limited hypoxia, a key driver of tumor malignancy. Using two breast cancer xenograft models, we found that photoacoustic tomography could detect higher fluctuations in oxygen saturation (sO2MSOT) in models with higher disease aggressiveness, supported by an overall lower sO2MSOT and greater spatial heterogeneity in sO2MSOT. Photoacoustic mesoscopy revealed differences in vascular architecture and perfusion dynamics between the models. The results were validated using immunohistochemistry and RNA sequencing, highlighting the potential of PAI to provide non-invasive insight on dynamic phenomena associated with perfusion-limited hypoxia in vivo.
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