Hemodynamics Segregation Using Expectation-Maximization Algorithm Initialized by Hierarchical Clustering on MR Dynamic Images from Patients with Unilateral Internal Carotid Artery Stenosis

摘要

Expectation-maximization (EM) algorithm initialized by hierarchical clustering (HC) was applied on dynamic susceptibility contrast (DSC) MR images from the patients with unilateral internal carotid artery stenosis to segment out different brain tissue clusters depending on their own specific blood supply patterns. In comparison with the segmented normal and abnormal gray matter components demonstrated that difference in mean transit time (dMTT) and difference in time to peak (dTTP) can robustly reveal the hemodynamic change from pre-stenting to post-stenting state (p-values are 0.027 and 0.004, respectively). Additionally, change of local deficit before and after the placement of stent can be further investigated by the ratio of numbers of normal to abnormal gray-matter pixels within the territories of anterior cerebral artery (ACA), middle cerebral artery (MCA) and posterior cerebral artery (PCA) (p-values are 0.375, 0.037 and 0.020, respectively) in assistance to diagnosis and therapeutic assessment.