Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments

Abstract

The development of in silico trials based on high-fidelity simulations of clinical procedures requires the availability of large cohorts of three-dimensional (3D) patient-specific anatomy models, which are often hard to collect due to limited availability and/or accessibility and imaging quality. Statistical shape modeling (SSM) allows one to identify the main modes of shape variation and to generate new samples based on the variability observed in a training dataset. In this work, a method for the automatic 3D reconstruction of vascular anatomies based on SSM is used for the generation of a virtual cohort of cerebrovascular models suitable for computational simulations, useful for in silico stroke trials. Starting from 88 cerebrovascular anatomies segmented from stroke patients’ images, an SSM algorithm was developed to generate a virtual population of 100 vascular anatomies, defined by centerlines and diameters. An acceptance criterion was defined based on geometric parameters, resulting in the acceptance of 83 generated anatomies. The 3D reconstruction method was validated by reconstructing a cerebrovascular phantom lumen and comparing the result with an STL geometry obtained from a computed tomography scan. In conclusion, the final 3D models of the generated anatomies show that the proposed methodology can produce a reliable cohort of cerebral arteries.