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

Author(s)

Cury, L. F. M. ^{[1, 2]} ; Talou, G. D. Maso ^[3] ; Younes-Ibrahim, M. ^{[1, 4]} ; Blanco, P. J. ^{[1, 2]}

Affiliation

^[1] Natl Inst Sci & Technol Med Assisted Sci Comp, INCT MACC, Petropolis, RJ - Brazil

^[2] LNCC MCTI, Natl Lab Sci Comp, Petropolis, RJ - Brazil

^[3] Univ Auckland, Auckland Bioengn Inst, Auckland - New Zealand

^[4] Univ Estado Rio De Janeiro, UERJ, Fac Med Sci, Rio De Janeiro - Brazil

• Document type: Journal article
• Source: ROYAL SOCIETY OPEN SCIENCE; v. 8, n. 12 DEC 1 2021.
• Web of Science Citations: 0

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 optimization (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 vascularize 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 extend 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. (AU)

• FAPESP's process: 14/50889-7 - National Institute of Science and Technology Medicine Assisted by Scientific Computing (INCT-MACC)
• Grantee: José Eduardo Krieger
• Support Opportunities: Research Projects - Thematic Grants