PET/MR Multimodal Quantitative Analysis of CBF and CMRGlc in Cerebral Ischemia

Research Purpose: This project focuses on clinical research issues in the diagnosis, treatment, and evaluation of patients with cephalic and carotid atherosclerosis. Through multimodal imaging technology, it aims to optimize non-invasive quantitative algorithm models for cerebral blood flow and cerebral metabolism, explore new imaging markers, and investigate quantitative indicators of cerebral blood flow and metabolism for the diagnosis, treatment, and evaluation of patients with cephalic and carotid atherosclerosis undergoing drug therapy or surgical treatment. Research Design: 1. Study Type: Observational study 2. Study Subjects: In accordance with the \*Chinese Clinical Management Guidelines for Cerebrovascular Diseases (2nd Edition)\*, patients with cephalic and carotid atherosclerotic stenosis admitted to the department of neurosurgery will be enrolled. Data collection time points are at enrollment, 6-month follow-up, and 12-month follow-up. 3. Sample Size: 200 patients with cephalic and carotid atherosclerotic stenosis. 4. Observation Indicators: Distribution patterns of cerebral blood flow and metabolic reduction areas in patients; correlation analysis between blood flow, metabolic values and clinical scores. 5. Statistical Analysis Methods: Statistical analysis will be performed using Statistical Package for the Social Sciences (SPSS) version 21.0. All statistical results will be expressed as mean ± standard deviation. First, the Shapiro-Wilk test will be used to assess normal distribution. Under the premise of normal distribution, paired t-test will be used to compare blood flow and metabolic values between the lesion area and the contralateral area of patients, as well as to compare changes in blood flow and metabolism of patients at different time points. Pearson correlation analysis will be used to analyze the relationship between blood flow/metabolic parameters and clinical neurological function scores. The Mann-Whitney U test will be used to analyze variables with non-normal distribution. A P value \< 0.05 will be considered statistically significant.