Computational modeling of predicting cerebrovascular injury in traumatic brain injury patients

Abstract

Despite significant progress in understanding traumatic brain injury (TBI) and subsequent outcomes, predicting and preventing cerebrovascular injury (CVI) remains challenging.This study introduces a novel computational modeling approach using advanced fluid-structure interaction (FSI) models, which incorporate high-resolution magnetic resonance imaging (MRI) data to analyze changes in intracranial pressure and blood flow in cerebral arteries post-TBI. The approach uniquely integrates detailed patient-specific brain vasculature geometries and dynamic boundary conditions to enhance predictive accuracy.Results indicate a significant increase in peak pressure within the first hour post-injury, particularly in the anterior cerebral artery (ACA).This finding underscores the necessity for early intervention in TBI patients to mitigate the risk of CVI. The study demonstrates the potential of FSI modeling to provide insights into the biomechanics of CVI in TBI patients, although it currently lacks model validation and relies solely on simulations, suggesting future work should include comparisons with benchmark models.