Computer analysis of brain vascular lesions in MRI:evaluating longitudinal change

描述（由申请人提供）：临床和亚临床脑血管疾病（CVD）是一个非常重要的健康问题，特别是在人口老龄化日益严重的情况下。 它是痴呆症的主要原因，单独或作为其他病理学的附加因素，如阿尔茨海默氏症。 CVD在糖尿病人群中也非常普遍，因此测量疾病负担、进展和对治疗的反应对于患者管理非常重要。 MRI是目前使用成像来表征CVD中脑病变的类型和程度的最佳方法。 然而，CVD的表征在很大程度上依赖于定性的、主观的、并且不容易再现的基于人类专家的解释方法，例如白色病变的心血管健康研究（CHS）分级。 基于计算机的测量CVD的方法为测量CVD提供了巨大的潜力，原因有很多：1）它们是定量的; 2）它们是可重复的，特别适合于监测疾病进展和对候选治疗的反应的纵向研究; 3）它们是高度自动化的，从而能够分析经常在大型神经影像学研究中获得的大量数据。 疾病进展的评估特别具有挑战性，因为通常依赖于对基线和随访扫描的独立评估，然后评估变化，而不是通过联合分析基线和随访扫描来最大限度地提高我们直接评估变化的能力。 该项目旨在开发和验证定量图像分析方法，以实现CVD及其随时间推移的进展的准确和精确量化。 4-将开发三维图像分析方法，旨在提高我们检测CVD细微纵向变化和治疗反应的灵敏度和准确性。 这些方法还将利用正常脑结构的统计图谱，这将进一步提高自动化方法检测脑血管病变偏离正常解剖结构的能力。 将使用尸检数据、计算模型和专家定义进行广泛的验证。 最后，应用到一个最大的，迄今为止，糖尿病及其临床管理的神经影像学研究，以及最全面的纵向成像研究的老化，将比较灵敏度检测CVD进展通过这种方法，与现有的替代图像分析方法。 公共卫生相关性：该项目将开发用于量化脑血管疾病及其随时间推移的进展的图像分析方法。 4-将使用三维图像分割方法来获得脑血管病理学及其随时间的进展的纵向一致测量。 捕捉大脑结构正常变化的统计地图集将进一步增强自动计算机方法检测大脑异常（偏离正常变化范围）的能力。 这些方法将在老年人的MRI扫描以及CVD增加的糖尿病患者的MRI扫描上进行测试。

DESCRIPTION (provided by applicant): Cerebrovascular disease (CVD), both clinical and subclinical, is a very significant health problem, especially in view of the increasing aging population. It is a major cause of dementia, individually or as an additive factor to other pathologies, such as Alzheimer's. CVD is also highly prevalent in diabetic populations, therefore measuring disease burden, progression, and response to treatments is very important for patient management. MRI is currently the best way to characterize the type and extent of brain lesions in CVD using imaging. However, characterization of CVD has largely relied on qualitative, subjective, and not easily reproducible methods of human expert-based interpretation, such as the Cardiovascular Health Study (CHS) grading of white matter lesions. Computer-based methods for measuring CVD offer great potential for measuring CVD, for many reasons: 1) they are quantitative; 2) they are reproducible, and particularly suitable for longitudinal studies monitoring disease progression and response to candidate treatments; 3) they are highly automated, thereby enabling the analysis of large amounts of data that are often acquired in large neuroimaging studies. The evaluation of disease progression has been particularly challenging, since typically relied on independent evaluations of baseline and follow- up scans then evaluating change, instead of maximizing our ability to evaluate change directly by jointly analyzing baseline and follow-up scans. This project aims to develop and validate quantitative image analysis methods that will enable the accurate and precise quantification of CVD and its progression over time. 4-dimensional image analysis methods will be developed, aiming to increase our sensitivity and accuracy in detecting subtle longitudinal changes of CVD and response to treatment. These methods will also utilize statistical atlases of normal brain structure, which will further improve the ability of automated methods to detect cerebrovascular lesions as being deviations from normal anatomy. Extensive validation will be performed using autopsy data, computational phantoms and expert definitions. Finally, application to one of the largest, to date, neuroimaging studies of diabetes and its clinical management, as well as to one of the most comprehensive longitudinal imaging studies of aging, will compare the sensitivity in detecting progression of CVD via this methodology, versus existing alternative image analysis approaches. PUBLIC HEALTH RELEVANCE: This project will develop image analysis methods for quantification of cerebrovascular disease, as well as its progression over time. 4-dimensional image segmentation methods will be used to obtain longitudinally consistent measurements of cerebrovascular pathology and its progression over time. Statistical atlases that capture normal variation of brain structure will further enhance the ability of automated computer methods to detect brain abnormalities as deviations from the normal range of variation. The methods will be tested on MRI scans from elderly individuals, as well as on MRI scans from diabetic patients with increased CVD.