@article{PB-VRVis-2021-028,
author = {Zopf, Lydia M. and Heimel, Patrick and Geyer, Stefan H. and Kavirayani, Anoop and Reier, Susanne and Fr{\"o}hlich, Vanessa and Stiglbauer-Tscholakoff, Alexander and Chen, Zhe and Nics, Lukas and Zinnanti, Jelena and Drexler, Wolfgang and Mitterhauser, Markus and Helbich, Thomas and Weninger, Wolfgang J. and Slezak, Paul and Obenauf, Anna and B{\"u}hler, Katja and Walter, Andreas},
title = {Cross-Modality Imaging of Murine Tumor Vasculature—a Feasibility Study},
year = {2021},
journaltitle = {Molecular Imaging and Biology},
doi = {10.1007/s11307-021-01615-y},
url = {https://www.vrvis.at/publications/PB-VRVis-2021-028},
abstract = {Tumor vasculature and angiogenesis play a crucial role in tumor progression. Their visualization
is therefore of utmost importance to the community. In this proof-of-principle study, we have
established a novel cross-modality imaging (CMI) pipeline to characterize exactly the same
murine tumors across scales and penetration depths, using orthotopic models of melanoma
cancer. This allowed the acquisition of a comprehensive set of vascular parameters for a single
tumor. The workflow visualizes capillaries at different length scales, puts them into the context of the overall tumor vessel network and allows quantification and comparison of vessel densities and morphologies by different modalities. The workflow adds information about hypoxia and blood flow rates. The CMI approach includes well-established technologies such as magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and ultrasound (US), and modalities that are recent entrants into preclinical discovery such as optical coherence tomography (OCT) and high-resolution episcopic microscopy (HREM). This novel CMI platform establishes the feasibility of combining these technologies using an
extensive image processing pipeline. Despite the challenges pertaining to the integration of
microscopic and macroscopic data across spatial resolutions, we also established an opensource pipeline for the semi-automated co-registration of the diverse multiscale datasets, which enables truly correlative vascular imaging. Although focused on tumor vasculature, our CMI platform can be used to tackle a multitude of research questions in cancer biology.},
keywords = {Mulitmodal imaging, Correlative imaging, Bioimaging, Tumor vasculature, Angiogenesis, Acquired resistance, Preclinical imaging},
}