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Model-based analysis of cerebrovascular diseases combining 3D and 4D MRA datasets

机译:结合3D和4D MRA数据集的基于模型的脑血管疾病分析

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摘要

The cerebral stroke is a major cause for death and disability. Clinical diagnosis, therapy, and research of stroke can considerably benefit from modern image acquisition methods, which enable a detailed analysis of cerebral blood vessel anatomy as well as an examination of macrovascular and tissue blood flow dynamics. However, visual screening of these datasets can be complex and time-consuming due to the vast amount of data. This article provides an overview of a dissertation, which addresses the problem of an automatic combined analysis and visualization of high-resolution 3D and spatiotemporal (4D) image sequences from the same patient to support diagnosis, treatment decision, and research of cerebrovascular diseases. Therefore, automatic methods for the cerebrovascular segmentation, analysis of the cerebral blood flow and tissue perfusion, as well as the combined quantitative analysis and visualization of the vessel morphology and blood flow dynamics were developed. Apart from a potential clinical application, the developed methods have already proven useful in multiple clinical research studies.
机译:脑卒中是死亡和残疾的主要原因。中风的临床诊断,治疗和研究可从现代图像获取方法中受益匪浅,该方法可对脑血管解剖结构进行详细分析,并检查大血管和组织血流动力学。但是,由于数据量巨大,对这些数据集进行可视筛选可能非常复杂且耗时。本文概述了论文,该论文解决了来自同一患者的高分辨率3D和时空(4D)图像序列的自动组合分析和可视化问题,以支持诊断,治疗决策和脑血管疾病的研究。因此,开发了用于脑血管分割,脑血流和组织灌注分析的自动方法,以及对血管形态和血流动力学进行定量分析和可视化的组合方法。除了潜在的临床应用,已开发的方法已被证明可用于多种临床研究。

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