Abstract
Cancer is a major cause of death worldwide and becomes particularly threatening once it begins to metastasize. During metastasis, the blood vessels serve as pathways for cancerous cell transportation and hence are crucial for understanding cancer growth. Existing medical imaging modalities can provide 3-D contrast images of the vascular tissues but with limited quality and detailedness. A much-needed tool for cancer research is thus one that can reconstruct vascular networks from low-quality clinical images. To this end, we developed a computational framework that takes 3-D medical images as input and reconstructs complete, patient-specific vascular network models using a mathematical optimization procedure. Our framework extracts major vessels from the images and uses the organ geometry to select vessel termination points. Then, it generates the remainder network based on physiological optimality principles. Using the framework, we obtained a set of network models with over 3000 terminal segments from a brain MRA scan. We analyzed the Strahler order, vessel radius, and branch length distributions of the models, which match with actual human data. We also performed fluid dynamics simulation inside the reconstructed vessels and showed that the pressure and shear stress distributions agree with existing in vivo measurements. The qualitative and quantitative agreements in vessel morphometry and hemodynamics demonstrate the effectiveness of the framework. Our method bridges the gap between image-based vessel models, accuracy of which is limited by the resolution of clinical images, and hypothetical models.
Highlights
In metastasis, cancer cells detach from a pre-existing primary tumor, intravasate into the bloodstream, flow through blood vessels avoiding immune protection, extravasate out of the vessels, and eventually form secondary tumors at other sites [1]
The flowchart of the entire process involved in the framework is presented in Fig. 11 in the Appendix, where we summarize essential intermediate steps of the framework, including clinical image segmentation, root and leaf node selection, as well as global constructive optimization (GCO) Forest optimization
To show the performance of our proposed framework, we apply it to reconstruct patient-specific cerebral vascular network models from clinical image data collected in the brain of a patient
Summary
Cancer cells detach from a pre-existing primary tumor, intravasate into the bloodstream, flow through blood vessels avoiding immune protection, extravasate out of the vessels, and eventually form secondary tumors at other sites [1]. Because blood vessels are vital links in the journey of the tumor cells, delineating the vessel structures may aid the development of novel methods for cancer diagnosis and metastatic growth prediction. An imperative tool for cancer research is a computational framework that generates patient-specific vascular models efficiently. We focus on vascular network reconstruction in human brains. Similar research on cerebral vascular networks is scarce due to the non-convex geometry of the vascular territories and the multiple blood flow inlets in the brain which complicate the network structure
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