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Analyzing attributes of vessel populations.

机译：分析船只种群的属性。

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Almost all diseases affect blood vessel attributes (vessel number, radius, tortuosity, and branching pattern). Quantitative measurement of vessel attributes over relevant vessel populations could thus provide an important means of diagnosing and staging disease. Unfortunately, little is known about the statistical properties of vessel attributes. In particular, it is unclear whether vessel attributes fit a Gaussian distribution, how dependent these values are upon anatomical location, and how best to represent the attribute values of the multiple vessels comprising a population of interest in a single patient. The purpose of this report is to explore the distributions of several vessel attributes over vessel populations located in different parts of the head. In 13 healthy subjects, we extract vessels from MRA data, define vessel trees comprising the anterior cerebral, right and left middle cerebral, and posterior cerebral circulations, and, for each of these four populations, analyze the vessel number, average radius, branching frequency, and tortuosity. For the parameters analyzed, we conclude that statistical methods employing summary measures for each attribute within each region of interest for each patient are preferable to methods that deal with individual vessels, that the distributions of the summary measures are indeed Gaussian, and that attribute values may differ by anatomical location. These results should be useful in designing studies that compare patients with suspected disease to a database of healthy subjects and are relevant to groups interested in atlas formation and in the statistics of tubular objects.
机译：几乎所有疾病都会影响血管属性（血管数量，半径，曲折度和分支模式）。因此，对相关船只种群上船只属性的定量测量可以提供诊断和分期疾病的重要手段。不幸的是，关于血管属性的统计属性知之甚少。具体而言，尚不清楚血管属性是否适合高斯分布，这些值如何依赖于解剖位置以及如何最好地表示单个患者中包括感兴趣人群的多个血管的属性值。本报告的目的是探讨位于头部不同部位的多种血管属性在整个种群中的分布。在13名健康受试者中，我们从MRA数据中提取血管，定义包括前脑，右，左中脑和后脑循环的血管树，并针对这四个人群中的每一个分析血管数量，平均半径，分支频率和曲折。对于所分析的参数，我们得出结论，对于每个患者，在每个关注区域内针对每个属性的每个属性采用汇总度量的统计方法优于处理单个血管的方法，汇总度量的分布确实是高斯分布，并且属性值可能因解剖位置而异。这些结果对于设计将可疑疾病患者与健康受试者数据库进行比较的研究有用，并且与对图集形成和管状物体统计感兴趣的人群相关。

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