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Parallel generation of extensive vascular networks with application to an archetypal human kidney model

Citation

Cury, Luis Fernando Mendes; Younes-Ibrahim, Maurício; Blanco, Pablo Javier; Maso Talou, Gonzalo Daniel (2022), Parallel generation of extensive vascular networks with application to an archetypal human kidney model, Dryad, Dataset, https://doi.org/10.5061/dryad.t4b8gtj25

Abstract

Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in-vivo morphometry is feasible through algorithms such as constrained constructive optimisation (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200,000 vascular segments. Finally, we apply our method to vascularise a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extent vascular networks (e.g., obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.

Methods

This dataset was generated by the DCCO and PDCCO vascularization algorithms implemented in the VItA library.
The .vtk geometry files were manually crafted using Blender and converting the resulting .stl files through VTK. The resulting .vtp files were visualized using ParaView. These .vtp files were also converted to .csv and analysed through plots created using the matplotlib library for Python.

Usage Notes

To run the source code contained in this dataset we need a working installation of the VTK 8.1.2 and VItA libraries.
To analyse the results ParaView and Python with matplotlib are required.
These are described in detail in README.txt.

Funding

Li Ka Shing Foundation, Award: 9077/31/8402

Conselho Nacional de Desenvolvimento Científico e Tecnológico, Award: 407751/2018- 1

Fundação de Amparo à Pesquisa do Estado de São Paulo, Award: 2014/50889-7