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首页> 外文会议>World congress on medical physics and biomedical engineering;International congress of the IUPESM >Hemodynamics Segregation Using Expectation-Maximization Algorithm Initialized by Hierarchical Clustering on MR Dynamic Images from Patients with Unilateral Internal Carotid Artery Stenosis
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Hemodynamics Segregation Using Expectation-Maximization Algorithm Initialized by Hierarchical Clustering on MR Dynamic Images from Patients with Unilateral Internal Carotid Artery Stenosis

机译：使用分层聚类初始化单侧颈内动脉狭窄患者的MR动态图像初始化期望最大化算法的血流动力学分离

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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.
机译：通过分层聚类（HC）初始化的期望最大化（EM）算法应用于来自单侧颈内动脉狭窄患者的动态磁化率对比（DSC）MR图像，以根据他们自己的特定血液供应模式分割出不同的脑组织簇。与分段的正常和异常灰质成分相比，表明平均转运时间（dMTT）和到达峰值时间（dTTP）的差异可以强有力地揭示从支架植入前到支架植入后状态的血液动力学变化（p值为分别为0.027和0.004）。此外，可以通过前脑动脉（ACA），中脑动脉（MCA）和后脑区域内正常与异常灰质像素的数量之比，进一步研究放置支架前后的局部缺陷变化。动脉（PCA）（p值分别为0.375、0.037和0.020）以辅助诊断和治疗评估。

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