Tissue-growth-based synthetic tree generation and perfusion simulation

Abstract

Biological tissues receive oxygen and nutrients from blood vessels by developing an indispensable supply and demand relationship with the blood vessels. We implemented a synthetic tree generation algorithm by considering the interactions between the tissues and blood vessels. We first segment major arteries using medical image data and synthetic trees are generated originating from these segmented arteries. They grow into extensive networks of small vessels to fill the supplied tissues and satisfy the metabolic demand of them. Further, the algorithm is optimized to be executed in parallel without affecting the generated tree volumes. The generated vascular trees are used to simulate blood perfusion in the tissues by performing multiscale blood flow simulations. One-dimensional blood flow equations were used to solve for blood flow and pressure in the generated vascular trees and Darcy flow equations were solved for blood perfusion in the tissues using a porous model assumption. Both equations are coupled at terminal segments explicitly. The proposed methods were applied to idealized models with different tree resolutions and metabolic demands for validation. The methods demonstrated that realistic synthetic trees were generated with significantly less computational expense compared to that of a constrained constructive optimization method. The methods were then applied to cerebrovascular arteries supplying a human brain and coronary arteries supplying the left and right ventricles to demonstrate the capabilities of the proposed methods. The proposed methods can be utilized to quantify tissue perfusion and predict areas prone to ischemia in patient-specific geometries.

OpenCCO: An Implementation of Constrained Constructive Optimization for Generating 2D and 3D Vascular Trees

“…Such optimizations could nd inspiration in recent works dedicated to extend / build upon CCO or similar algorithms, e.g. [12,8].…”

Cardiovascular fluid dynamics: a journey through our circulation

“…These models have been applied to study capillary flows, oxygen or nutrient exchange, as well as the transport of therapeutics in various organs such as in alveoli within the lungs (Erbertseder et al 2012;Zurita & Hurtado 2022), within tumours (Secomb et al 2004;Stylianopoulos & Jain 2013;d'Esposito et al 2018), in the brain (Sweeney, Walker-Samuel & Shipley 2018;Kim et al 2023) and in the heart (Chapelle et al 2010;Cookson et al 2012;Michler et al 2013;Di Gregorio et al 2021Papamanolis et al 2021;Kim et al 2023). While the development of tissue perfusion models is relatively nascent compared with the 3-D and lumped-parameter models discussed above, in the context of flow through cardiac tissue, porous medium flow models have been coupled with high-fidelity and reduced-order models of flow in upstream coronary arteries (Hyde et al 2014;Di Gregorio et al 2021;Papamanolis et al 2021;Kim et al 2023;Menon et al 2024) as well as the elasto-mechanics describing cardiac contraction and its effect on the pressure driving coronary flow within the cardiac tissue (Chapelle et al 2010;Cookson et al 2012;Di Gregorio et al 2021). Figure 3(b) shows an example of a Darcy myocardial flow model coupled to upstream 3-D flow in coronary arteries and 0-D distal circulation models.…”

“…2004; Stylianopoulos & Jain 2013; d'Esposito et al. 2018), in the brain (Sweeney, Walker-Samuel & Shipley 2018; Kim et al. 2023) and in the heart (Chapelle et al.…”

Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees

“…This allows us to create more physiological coronary artery models by combining image-based anatomical models of epicardial vessels with synthetic vasculature for distal vessels. While similar approaches have been used previously 48 – 50 , they have not incorporated high-fidelity flow simulations, closed-loop circulation models, or personalized boundary conditions. Here, we demonstrate a novel technique to tune such vascular trees, as well as the parameters of closed-loop lumped parameter circulatory networks, based on patient-specific measurements.…”