Generation of Patient-Specific Cardiac Vascular Networks: A Hybrid Image-Based and Synthetic Geometric Model

Abstract

Abstract-Objective: In this paper, we propose an algorithm for the generation of a patient-specific cardiac vascular network starting from segmented epicardial vessels down to the arterioles.Method: We extend a tree generation method based on satisfaction of functional principles, named Constrained Constructive Optimization (CCO), to account for multiple, competing vascular trees. The algorithm simulates angiogenesis under vascular volume minimization with flow-related and geometrical constraints adapting the simultaneous tree growths to patient priors. The generated trees fill the entire left ventricle myocardium up to the arterioles.Results: From actual vascular tree models segmented from CT images, we generated networks with 6000 terminal segments for 6 patients. These networks contain between 33 and 62 synthetic trees. All vascular models match morphometry properties previously described.Conclusion and significance: Image-based models derived from CT angiography are being used clinically to simulate blood flow in the coronary arteries of individual patients to aid in the diagnosis of disease and planning treatments. However, image resolution limits vessel segmentation to larger epicardial arteries. The generated model can be used to simulate blood flow and derived quantities from the aorta into the myocardium. This is an important step for diagnosis and treatment planning of coronary artery disease.

The effects of clinically‐derived parametric data uncertainty in patient‐specific coronary simulations with deformable walls

“…Taylor et al 4 used m = 3 instead for boundary conditions in aorto-coronary models. Jaquet et al 71 and other recent studies 11,30 adopted m = 2.6 for coronary arteries. In our study, we assume the morphometry exponent as a uniformly distributed random variable, m(ω) between 2.4 and 2.8, that is, m $ U 2:4, 2:8 ð Þ.…”

Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and a mathematical algorithm

“…Although the data-driven approach is strong when sufficient data are available, it is difficult to apply to cases in which there is less a priori morphological information and a patient-specific morphology must be addressed. The second approach uses an image-based geometry, whereby the vascular model is reconstructed according to an observed image [29] or small-scale vasculatures are created from the terminal ends of the image-based large-scale geometry [30]. This approach can easily incorporate a patient-specific (or personalized) geometry into the modeling, and does not require a priori statistical information of the vascular morphology.…”

A computational model applied to myocardial perfusion in the human heart: From large coronaries to microvasculature

“…For example, in [38] the authors generated a coronary network by using the anatomical data from [39] and with a bifurcation angle taken from [81,82]. Instead, in [65] the authors, starting again from the data reported in [39], used a bifurcation angle based on the minimization of the shear rate [80], whereas in [36] physiological constraints are introduced by means of the Constraint Constructive Optimization (CCO) method [62], which relies on hemodynamics principles. Here, for the sake of simplicity we preferred to use a pure geometric criterion to generate the network.…”