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Using Digital Reconstructions, Morphometry, And Computational Models To Generate Novel Maps Of Human Brain Vascular Architecture.

机译:使用数字重建,形态计量学和计算模型生成人脑血管体系结构的新颖图。

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摘要

Many non-invasive imaging techniques based upon hemodynamic responses of blood vessels have provided data for analysis of brain vasculature. However, these data lack the detail that could be ascertained by exploiting state-of-the-art neuroinformatics tools. A more complete and sufficiently detailed analysis of brain vascular architecture is critical for a wide variety of applications, including the development of models which may help predict or treat cerebrovascular disease.;Taking advantage of image stacks acquired from 3T time-of-flight magnetic resonance angiography and techniques previously used to create 3D neuronal reconstructions, the circle of Willis and the six major arteries that stem from it were reconstructed for sixty-one healthy subjects. The basis of this dissertation research is that recently available neuroinformatics tools can be exploited to create new models of vascular reconstructions that are representative of the general population of healthy subjects. These models can be used to generate more detailed novel comparative assessments between normal and diseased vasculature found in forms of cerebrovascular disease.;Sixty-one digital reconstructions of healthy human brain vasculature were created and extensive quantitative morphometrical analysis was conducted in order to characterize the anatomy of the human brain vessels, on both global and local levels of size, distances, angles and topology, concentrating in particular on vessel bifurcations. Additional analysis was conducted to provide quantitative description of the arterial branches, bifurcation patterns, shape and geographical distribution of the main cerebral arterial arborizations, as well as estimations of the corresponding vascular territories. Also as part of this analysis, computational models were created to examine fluid dynamics. In order to create these models, the brain was segmented and normal blood flow and wall shear stress values were measured, with the intent to provide a set of baseline values that could then be compared to patient data values. Lastly, the digital reconstructions and their extracted morphological measurements were archived in a database that will be made publicly available.;The information that can be extracted from these detailed reconstructions can be used to examine questions such as how the brain vasculature differs across various populations, such as males and females, different age groups, and healthy individuals versus individuals with cerebrovascular disease. Also, the information that can be extracted from the reconstructions can be used to implement complex computational models of fluid dynamics that may aid in the development of new treatments for individuals afflicted with cerebrovascular disease. Finally, the new information generated here may play a fundamental role in our ability to recognize vascular defects that could help treat, or even predict, cerebrovascular diseases.
机译:许多基于血管的血流动力学反应的非侵入性成像技术为分析脑血管提供了数据。但是,这些数据缺乏可以通过利用最新的神经信息学工具确定的细节。对脑血管结构进行更完整,足够详细的分析对于各种应用至关重要,包括开发可能有助于预测或治疗脑血管疾病的模型。;利用从3T飞行时间磁共振获得的图像堆栈先前用于创建3D神经元重建的血管造影术和技术,Willis环和源自其的6条主要动脉均针对61位健康受试者进行了重建。本论文研究的基础是,可以利用最近可用的神经信息学工具创建代表健康受试者总人数的新的血管重建模型。这些模型可用于在以脑血管疾病形式发现的正常和患病脉管之间进行更详细的新颖比较评估。;创建了健康人脑脉管的六十一种数字重建,并进行了广泛的定量形态计量分析以表征解剖结构人脑血管的大小,距离,角度和拓扑结构的全局和局部水平,尤其集中在血管分支上。进行了额外的分析,以定量描述主要脑动脉分支的动脉分支,分支模式,形状和地理分布,以及对相应血管区域的估计。同样作为此分析的一部分,创建了计算模型来检查流体动力学。为了创建这些模型,对大脑进行了分割,并测量了正常的血流和壁切应力值,目的是提供一组基线值,然后可以将这些基线值与患者数据值进行比较。最后,将数字重建及其提取的形态学测量结果存储在一个数据库中,该数据库将公开提供;从这些详细重建中可以提取的信息可用于检查问题,例如不同人群的脑血管系统如何不同,例如男性和女性,不同年龄段以及健康个体与患有脑血管疾病的个体。同样,可以从重建中提取的信息可用于实施复杂的流体动力学计算模型,这可能有助于开发针对脑血管疾病患者的新疗法。最后,此处生成的新信息可能在我们识别有助于治疗甚至预测脑血管疾病的血管缺陷的能力中起着根本作用。

著录项

  • 作者

    Wright, Susan.;

  • 作者单位

    George Mason University.;

  • 授予单位 George Mason University.;
  • 学科 Biology Neuroscience.;Biology Bioinformatics.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 137 p.
  • 总页数 137
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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