Description

In China, the annual incidence of ischemic stroke is estimated to be between 2.5 to 3 million cases, making it the leading cause of death and disability among the population. Among these cases, cervical artery stenosis is a significant independent risk factor for ischemic stroke. Approximately 50% of patients with cervical artery stenosis are prone to develop vascular-related cognitive impairment due to cerebral hypoperfusion, severely affecting human health and quality of life.

There are currently two main surgical approaches for treating severe cervical artery stenosis: carotid endarterectomy (CEA) and carotid artery stenting (CAS). The effects of these two surgical methods on preoperative and postoperative intracranial and extracranial hemodynamic changes, the mechanisms underlying perioperative complications, the establishment of collateral circulation, and long-term prognosis remain unclear. Therefore, researching perioperative risk assessment and clinical efficacy of different surgical approaches is of great significance for patient outcomes.

This project aims to collect multimodal imaging data from patients with cervical artery stenosis, including brain CT perfusion imaging and CT angiography. Using artificial intelligence algorithms, three-dimensional models of cervical artery stenosis will be reconstructed, and computational fluid dynamics will be employed to automatically or semi-automatically analyze the hemodynamic characteristics of patients' carotid arteries. By monitoring cerebral blood flow velocity, local cerebral oxygen metabolism, and assessing postoperative stroke, ischemia-reperfusion injury, and collateral circulation both intracranially and extracranially, precise evaluations will be conducted.

Based on individual patient characteristics, the surgical approach can be optimized to prevent cerebral ischemia-reperfusion injury, improve clinical prognosis, and enhance the quality of life for patients.