Fast, Robust Analysis of Large Population Data

对大量人口数据进行快速、稳健的分析

• 批准号: 7780861
• 负责人: Dinggang Shen
• 金额: $ 33.3万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7780861
• 关键词: Aging; Algorithms; Alzheimer' s Disease; Atlases; Brain; Brain Diseases; Brain imaging; Clinical Research; Communities; Complex; Computers; Data; Data Set; Detection; Development; Disease; Drug Formulations; Early Diagnosis; Goals; Growth; Image; Individual; Informatics; Intervention; Learning; Location; Longitudinal Studies; Magnetic Resonance Imaging; Matched Group; Measurement; Memory; Methods; Monitor; Monozygotic twins; Neonatal; Nerve Degeneration; Noise; Patients; Performance; Population; Process; Research; Resources; Series; Speed; Staging; Statistical Distributions; Techniques; Testing; Time; Twin Multiple Birth; Uncertainty; base; cerebral atrophy; comparison group; evaluation/testing; image registration; interest; neuroimaging; neuropsychiatry; novel; relating to nervous system; research study; tool

DESCRIPTION (provided by applicant): Modern imaging, such as MRI, can provide a safe, non-invasive measurement of the whole brain, and has been increasingly employed for large clinical and research studies of brain development, maturation, and aging, as well as for monitoring the effects of pharmacological interventions over time. This has created a great need for the development of highly automated, accurate, and robust measurement tools for analysis of large neuroimage dataset. Image registration as an important image measurement tool has attracted enormous scientific interest, since it is the key step for integration and comparison of data from different individuals or groups, as well as for the development of statistical atlases that reflect structural and functional variability within a group of individuals. However, most of the current registration algorithms are based on pair-wise registration of an individual brain with a selected template. This independent pair-wise registration and the subjective selection of template can introduce systematic registration error and bias to the aligned images, thus reducing the statistical power in detecting subtle brain changes, e.g., tiny longitudinal structural and functional changes which are important for early detection of Alzheimer's Disease (AD). To resolve these limitations, group-wise registration and inter-group comparison methods have been recently proposed to achieve consistent registration across all subjects by simultaneous registration of all individual subjects to their group mean directly. However, the accuracy and robustness of these group-wise registration methods are limited in identifying tiny brain differences, since the independent estimation of potentially large complex deformations from each subject to the group mean directly can make the initially very similar images (with tiny difference) become very different after registration, due to noise and uncertainty in the registration. Moreover, because of the required simultaneous registration of a large set of images and the limitation of computer memory capability, current group-wise registration methods can handle only a small number of images, e.g., several to dozens. The first aim of this project is to develop a fast, robust, and accurate group-wise registration algorithm which is able to handle simultaneously a large set of images, e.g., hundreds or thousands of images, by a general computer. Our key idea is to partition a large-scale group-wise registration problem into a series of hierarchical small-scale registration problems, each of which can be handled efficiently by a general computer and can be solved robustly and accurately by simplification of the registration problem. Moreover, for effective comparison of two (or more) groups, i.e., obtained respectively from early-stage diseased patients and normal controls, or from genetically identical twins, we further propose a novel inter- group registration method to effectively align two groups by matching not only their means but also their statistical distributions at all corresponding locations. Thus, the statistical difference between the two groups can be greatly identified, which enables the detection of tiny brain atrophies due to diseases such as those found during the early stage of AD or tiny brain growth differences within twins. This inter-group registration and comparison method can also be extended for the registration of multiple groups, with application in longitudinal study of twins at early neonatal stage. The study of all these novel inter-group registration and comparison methods is the topic of the second aim. Finally, we will apply our developed group-wise registration method, as well as the inter-group registration and comparison method, to the ADNI (Alzheimer's Disease Neuroimaging Initiative) dataset for early detection of AD, and to the neonatal dataset for study of tiny brain growth differences within twins. The performance of the proposed method will be extensively validated and also compared with those obtained by pair-wise registration methods as well as by other group-wise registration methods. These studies are the topic of the third aim. The final developed algorithms will be made freely available to the whole research community through NITRC (Neuroimaging Informatics Tools and Resources Clearinghouse), as we did with our HAMMER registration algorithm (http://www.nitrc.org/projects/hammer/), which is one of the top downloaded tools in NITRC. PUBLIC HEALTH RELEVANCE: This project aims at the development, testing, and evaluation of fast, robust, and accurate group registration and statistical comparison algorithms for effective simultaneous processing of large sets of brain images; to enable the detection of tiny, complex group differences. This is important for early detection of brain diseases (e.g., Alzheimer's Disease) and for identification of tiny brain growth differences within genetically identical twins. The final developed algorithms will be made freely available to the whole research community through NITRC (Neuroimaging Informatics Tools and Resources Clearinghouse), as we did with our HAMMER registration algorithm (http://www.nitrc.org/projects/hammer/), which is currently one of the top download tools in NITRC.

描述（由申请人提供）：现代成像，如MRI，可以提供全脑的安全，非侵入性测量，并且越来越多地用于大脑发育，成熟和衰老的大型临床和研究研究，以及监测药物干预的影响。这就产生了对高度自动化、准确和强大的测量工具的开发的巨大需求，用于分析大型神经图像数据集。图像配准作为一种重要的图像测量工具已经引起了巨大的科学兴趣，因为它是整合和比较来自不同个体或群体的数据的关键步骤，也是开发反映个体群体内结构和功能变化的统计地图集的关键步骤。然而，目前大多数的配准算法都是基于个体大脑与选定模板的成对配准。这种独立的成对配准和模板的主观选择会给对齐后的图像带来系统的配准误差和偏倚，从而降低了检测大脑细微变化的统计能力，例如微小的纵向结构和功能变化，而这些变化对阿尔茨海默病（AD）的早期检测很重要。为了解决这些局限性，最近提出了分组注册和组间比较方法，通过将所有个体受试者直接同时注册到其组均值来实现所有受试者的一致注册。然而，这些分组配准方法的准确性和鲁棒性在识别微小的大脑差异方面受到限制，因为从每个受试者到组均值的潜在大复杂变形的独立估计直接可以使最初非常相似的图像（具有微小差异）在配准后变得非常不同，这是由于配准中的噪声和不确定性。此外，由于需要同时配准大量图像和计算机存储能力的限制，目前的组智能配准方法只能处理少量图像，例如几到几十张。该项目的第一个目标是开发一种快速，稳健，准确的组智能配准算法，该算法能够同时处理大型图像集，例如数百或数千张图像，由普通计算机处理。我们的核心思想是将一个大规模的群体智能配准问题划分为一系列分层的小规模配准问题，每个配准问题都可以由普通计算机有效地处理，并且可以通过简化配准问题来鲁棒性和准确性地求解。此外，为了有效地比较两个（或更多）组，即分别从早期患病患者和正常对照中获得，或从基因相同的双胞胎中获得，我们进一步提出了一种新的组间注册方法，通过在所有相应位置匹配两组的均值和统计分布来有效地对齐两组。因此，两组之间的统计差异可以被极大地识别出来，从而可以检测到由于疾病引起的微小脑萎缩，例如在阿尔茨海默病早期发现的疾病，或者双胞胎中大脑生长的微小差异。这种组间注册和比较方法也可以扩展到多组注册，应用于新生儿早期双胞胎的纵向研究。研究所有这些新的组间配准和比较方法是第二个目标的主题。最后，我们将把我们开发的分组注册方法，以及组间注册和比较方法，应用于ADNI（阿尔茨海默病神经成像倡议）数据集，用于早期发现AD，以及新生儿数据集，用于研究双胞胎大脑生长的微小差异。本文将对该方法的性能进行广泛验证，并将其与配对配准方法以及其他组智能配准方法的结果进行比较。这些研究的主题是第三个目的。最终开发的算法将通过NITRC（神经成像信息学工具和资源交换中心）免费提供给整个研究社区，就像我们对HAMMER注册算法所做的那样（http://www.nitrc.org/projects/hammer/），这是NITRC下载最多的工具之一。