Robust White Matter Morphometry with Small Databases

具有小型数据库的强大白质形态测量

• 批准号: 9220858
• 负责人: Pew-Thian Yap
• 金额: $ 36.8万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9220858
• 关键词: Age; Alzheimer' s Disease; Anisotropy; Brain; Collection; Complex; Data; Databases; Detection; Diffuse; Diffusion; Diffusion Magnetic Resonance Imaging; Disease; Ensure; Evaluation; Exhibits; Gender; Hour; Image; Imaging Techniques; Individual; Individual Differences; Investigation; Magnetic Resonance Imaging; Medical Imaging; Methodology; Methods; Modeling; Morphologic artifacts; Noise; Outcome; Pathologic; Pathway interactions; Patient Recruitments; Patients; Pattern; Prevalence; Recruitment Activity; Sample Size; Sampling; Scanning; Shapes; Statistical Data Interpretation; Statistical Distributions; Structure; Techniques; Testing; Time; Variant; base; computerized tools; cost; disease diagnosis; imaging study; improved; indexing; interest; morphometry; neuroimaging; neuropsychiatric disorder; novel; public health relevance; spectrograph; statistics; volunteer; water diffusion; white matter

 DESCRIPTION (provided by applicant): Statistical comparison of neuroimaging data often requires large databases to produce reliable outcomes. However, in medical imaging studies, databases are usually small due to the difficulty in recruiting patients and volunteers. Samples are even more limited when parameters such as age or gender must be matched between healthy controls and patients. In situations as such, conventional statistical tests may become erroneous and generate either false positive or false negative detections. In addition, automatic image comparison approaches typically require a common reference frame that is often constructed from scans of healthy subjects by means of non-linear registration. However, registration methods are not perfect and may be prone to errors due to noise, artifacts, and complex variations in brain topology. Registration errors introduce structural variability that wil decrease the statistical power in detecting real meaningful differences. The objective of this project is to create a set of novel computational tools for robust statistical analysis of diffusio magnetic resonance imaging (MRI) data, particularly in situations where samples are noisy, limited, and exhibit complex shape variations. We propose three aims to achieve this objective. In Aim 1, we will devise a technique that will drastically increase the number of available samples for the estimation of diffusion statistics and their variability. This is achieved by identifying and agglomerating repetitive local information throughout an image to significantly increase sample size for improving estimation. We will further develop statistical techniques that will utilize these `repeated samples' for resampling- based non-parametric estimation of the variability of statistics of interest. In Aim 2, we will develop methods for effective and robust group and individual comparisons of diffusion statistics using a limited number of samples. This is achieved by explicitly correcting for registration errors via a block matching mechanism to ensure that comparisons are performed only between matching structures. Since variability due to registration errors are minimized, our method will significantly increase statistical power in detecting abnormalities. In addition, similar to Aim 1, our method will allow comparisons to be performed without imposing a priori, but often unrealistic, assumption on the distribution of the statistic of interest. In Aim 3, extensive evaluations of the methods developed in Aim 1 and Aim 2 will be carried out using databases associated with neuropsychiatric disorders, such as Alzheimer's disease. If successful, the statistical computational tools developed in this project will increase the statistical power of studies involving smaller databases and will allow detection of smaller effect sizes in studies with moderately-sized databases.

 描述（由申请人提供）：神经影像学数据的统计比较通常需要大型数据库才能产生可靠的结果。然而，在医学影像学研究中，由于招募患者和志愿者的困难，数据库通常较小。当健康对照和患者之间必须匹配年龄或性别等参数时，样本甚至更加有限。在这样的情况下，常规统计测试可能变得错误并且生成假阳性或假阴性检测。此外，自动图像比较方法通常需要通常借助于非线性配准从健康受试者的扫描构造的公共参考系。然而，配准方法并不完美，并且可能由于噪声、伪影和大脑拓扑结构的复杂变化而容易出错。配准误差引入结构可变性，这将降低检测真实的有意义差异的统计功效。该项目的目标是创建一组新的计算工具，用于对扩散磁共振成像（MRI）数据进行稳健的统计分析，特别是在样本有噪声、有限且具有复杂形状变化的情况下。我们提出三个目标来实现这一目标。在目标1中，我们将设计一种技术，该技术将大大增加用于估计扩散统计及其变异性的可用样本数量。这是通过识别和聚集整个图像中的重复局部信息来实现的，以显著增加样本大小，从而改善估计。我们将进一步开发统计技术，利用这些“重复样本”对感兴趣的统计数据的变异性进行基于回归的非参数估计。在目标2中，我们将使用有限数量的样本开发有效和稳健的扩散统计组和个体比较方法。这是通过经由块匹配机制明确地校正配准误差来实现的，以确保仅在匹配结构之间执行比较。由于配准误差引起的变异性被最小化，我们的方法将显著增加检测异常的统计能力。此外，类似于目标1，我们的方法将允许进行比较，而不强加先验的，但往往是不切实际的，对感兴趣的统计量的分布假设。在目标3中，将使用与阿尔茨海默病等神经精神疾病相关的数据库，对目标1和目标2中开发的方法进行广泛评估。如果成功的话，本项目开发的统计计算工具将增加涉及较小数据库的研究的统计能力，并将允许检测 在中等规模数据库的研究中，