Semi-automated pipeline for generating personalised cerebrovascular models.

Abstract

Subject-specific cerebrovascular models predict individual unmeasurable vessel haemodynamics using principles of physics, assumed constitutive laws, and measurement-deduced boundary conditions. However, the process of generating these models can be time-consuming, which is a barrier for use in time-sensitive clinical applications. In this work, we developed a semi-automated pipeline to generate anatomically and functionally personalised 0D cerebrovascular models from vasculature geometry and blood flow data. The pipeline extracts the vessel connectivity and geometric parameters from vessel segmentation to automatically generate a bond graph-based (linear and time-dependent) model of subject vasculature. Then, using a neurofuzzy control scheme, the peripheral resistances of the model are calibrated to minimise the discrepancy between measured and predicted blood flow distributions. We validated the pipeline by generating subject-specific models of the Circle of Willis (CoW) for 10 cases and compared haemodynamic predictions against acquired 4D flow MRI data. The results showed a relative error of $0.25\pm 0.66\%$ for flow and $13.87\pm 18.24\%$ for pulsatility, with a higher error for smaller vessels. We then demonstrated a use case of the model by simulating the blood flow redistribution during vascular occlusion for different CoW geometries. The results highlighted the benefit of a completely connected CoW to redistribute flow. The modular nature and rapid model generation time of this pipeline make it a promising tool for research and clinical use, where the type and structure of data are variable, and computing resources may be limited.