Computational modeling of predicting cerebrovascular injury in traumatic brain injury patients

Despite significant progress in understanding traumatic brain injury (TBI) and subsequent outcomes, predicting and preventing cerebrovascular injury (CVI) remains challenging. We 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. We analyzed the mechanical fields between the PCOMA, ICA, PCA, and ACA areas by conducting a region of interest analysis in this research. The approach uniquely integrates detailed patient-specific brain vasculature geometries and dynamic boundary conditions to enhance predictive accuracy. The model indicates that peak pressure in the anterior cerebral artery (ACA) reaches 130 kPa within the first hour post-injury, with a 20% increase observed at 2 milliseconds. Wall shear stress (WSS) in the posterior communicating artery (PCOMA) reaches 140 kPa, which exceeds the typical physiological range of 10–20 kPa and may lead to endothelial damage. Regions such as the PCOMA and ACA are identified as particularly vulnerable to high mechanical stress and strain, which suggests the need for timely medical intervention to reduce the risk of CVI. Further validation with clinical data is required to improve the predictive accuracy of the model.