Ischemic stroke affects 2.5 to 3 million people annually in China, ranking as the leading cause of death and disability. Cervical artery stenosis is a significant contributor to this problem, with about 50% of patients experiencing cognitive impairment due to reduced cerebral blood flow. Two main surgical approaches, carotid endarterectomy (CEA) and carotid artery stenting (CAS), are used to treat severe cervical artery stenosis, but their effects on various factors remain unclear. This project collects multimodal imaging data, including CT perfusion and angiography, to create 3D models of cervical artery stenosis. Computational fluid dynamics and AI analysis are used to assess hemodynamics. By monitoring blood flow, oxygen levels, and evaluating postoperative outcomes, the goal is to tailor surgical approaches for better patient outcomes and improved quality of life.