Department of Mathematical and Computational Methods, National Laboratory for Scientific Computing, Petrópolis, Brazil.
National Institute of Science and Technology in Medicine Assisted by Scientific Computing (INCT-MACC), São Paulo, Brazil.
Sci Rep. 2021 Mar 17;11(1):6180. doi: 10.1038/s41598-021-85434-9.
Mimicking angiogenetic processes in vascular territories acquires importance in the analysis of the multi-scale circulatory cascade and the coupling between blood flow and cell function. The present work extends, in several aspects, the Constrained Constructive Optimisation (CCO) algorithm to tackle complex automatic vascularisation tasks. The main extensions are based on the integration of adaptive optimisation criteria and multi-staged space-filling strategies which enhance the modelling capabilities of CCO for specific vascular architectures. Moreover, this vascular outgrowth can be performed either from scratch or from an existing network of vessels. Hence, the vascular territory is defined as a partition of vascular, avascular and carriage domains (the last one contains vessels but not terminals) allowing one to model complex vascular domains. In turn, the multi-staged space-filling approach allows one to delineate a sequence of biologically-inspired stages during the vascularisation process by exploiting different constraints, optimisation strategies and domain partitions stage by stage, improving the consistency with the architectural hierarchy observed in anatomical structures. With these features, the aDaptive CCO (DCCO) algorithm proposed here aims at improving the modelled network anatomy. The capabilities of the DCCO algorithm are assessed with a number of anatomically realistic scenarios.
在分析多尺度循环级联和血流与细胞功能之间的耦合时,模拟血管区域的血管生成过程变得尤为重要。本工作在几个方面扩展了受限构造优化(Constrained Constructive Optimisation,CCO)算法,以解决复杂的自动血管化任务。主要扩展基于自适应优化标准和多阶段空间填充策略的集成,这些扩展增强了 CCO 对特定血管结构的建模能力。此外,这种血管生长可以从头开始,也可以从现有的血管网络开始。因此,血管区域被定义为血管、无血管和载体区域的分区(最后一个区域包含血管但不含末端),允许对复杂的血管区域进行建模。反过来,多阶段空间填充方法允许通过在不同的阶段逐步利用不同的约束、优化策略和域分区来描绘血管化过程中的一系列具有生物学启发的阶段,从而提高与解剖结构中观察到的结构层次的一致性。有了这些特性,本文提出的自适应 CCO(aDaptive CCO,DCCO)算法旨在改善所建模的网络解剖结构。使用一些具有解剖学意义的场景来评估 DCCO 算法的功能。