Objective: This study aims to investigate the application of multimodal Magnetic Resonance Imaging (MRI) in the brain diagnosis of symptomatic Intracranial Atherosclerotic Stenosis (sICAS) patients with depression; results of which may be an evidence for formulating the early treatment and therapeutic schedule of these patients.
Methods: A total of 120 patients who have severe sICAS with depression admitted in our hospital were randomly divided into the control and observation groups, with 60 cases in each group. All patients were subjected to MRI examinations, including routine MRI+MRA, perfusion-weighted imaging, and highresolution MRI, before the treatment. The patients in the control group underwent the routine internal medical treatment. The patients in the observation group were treated with intravascular stent-assisted angioplasty based on routine internal medical treatment. All patients were followed up for 1 year. The incidence of complications, recurrence rate of sICAS with depression, Mean Transit Time (MTT), Cerebral Blood Volume (CBV), time to peak, and Cerebral Blood Flow (CBF) of patients in two groups were compared before the treatment and after the 12 month follow-up. Meanwhile, the correlation between various imaging indexes and therapeutic schedule was analysed.
Results: Compared with the test results before treatment, MTT was decreased significantly in two groups, and CBV and CBF were increased significantly, and the differences were statistically significant (P<0.05). Compared with that in the control group, MTT was decreased significantly in the observation group, CBV and CBF were increased significantly, the recurrence rate of sICAS with depression was decreased significantly, and the differences were statistically significant (P<0.05).
Conclusion: Endovascular stent implantation reduced the recurrence rate of sICAS with depression. The multimodal magnetic resonance perfusion imaging-associated indexes could be used to evaluate the efficacy of stent implantation in sICAS patients with depression.
Author(s): Dongxue Qin, Lin Sha, Xiang Li, Mei Yi
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