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: 9103347
• 负责人: Pew-Thian Yap
• 金额: $ 37.62万
• 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/9103347
• 关键词: Age; Alzheimer' s Disease; Anisotropy; Brain; Collection; Complex; Data; Databases; Detection; 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; Pathway interactions; Patient Recruitments; Patients; Pattern; Prevalence; Recruitment Activity; Sample Size; Sampling; Scanning; Shapes; Statistical Data Interpretation; Structure; Techniques; Testing; Time; Variant; Weight; base; computerized tools; cost; disease diagnosis; improved; indexing; interest; meetings; 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，在医学成像研究中，由于在招募患者和志愿者方面存在差异，数据库通常很小。当年龄或性别等参数必须在健康对照组和患者之间匹配时，样本甚至更有限。在这种情况下，传统的统计测试可能会出错，并产生假阳性或假阴性检测。此外，自动图像比较方法通常需要一个公共参照系，该参照系通常是通过非线性配准从健康受试者的扫描中构建的。然而，配准方法并不完美，可能会由于噪声、伪影和大脑拓扑结构的复杂变化而容易出错。配准误差引入了结构性变异性，这将降低检测真正有意义的差异的统计能力。该项目的目标是创建一套新的计算工具，用于对扩散磁共振成像(MRI)数据进行稳健的统计分析，特别是在样本噪声、有限和表现出复杂形状变化的情况下。为了实现这一目标，我们提出了三个目标。在目标1中，我们将设计一种技术，大大增加可用于估计扩散统计数据及其变异性的样本数量。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以改进估计。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可变性进行基于重新抽样的非参数估计。在目标2中，我们将开发使用有限数量的样本进行扩散统计的有效和稳健的群体和个人比较的方法。这是通过经由块匹配机制显式地校正配准错误来实现的，以确保仅在匹配结构之间执行比较。由于配准误差引起的可变性被最小化，因此我们的方法将显著提高检测异常的统计能力。此外，与目标1类似，我们的方法将允许执行比较，而不会对感兴趣的统计数据的分布施加先验的、但往往不现实的假设。在目标3中，将利用与阿尔茨海默病等神经精神障碍有关的数据库，对目标1和目标2中开发的方法进行广泛评价。如果成功，该项目中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 在具有中等规模数据库的研究中，效果规模较小。