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: 8532675
• 负责人: Dinggang Shen
• 金额: $ 31.4万
• 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/8532675
• 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; public health relevance; 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(申请人提供)：现代成像，如MRI，可以提供安全、非侵入性的全脑测量，并已越来越多地被用于大脑发育、成熟和衰老的大型临床和研究研究，以及随着时间的推移监测药物干预的效果。这就产生了对用于分析大型神经图像数据集的高度自动化、准确和健壮的测量工具的巨大需求。图像配准作为一种重要的图像测量工具引起了巨大的科学兴趣，因为它是综合和比较来自不同个人或群体的数据以及编制反映一组个人内部结构和功能差异的统计地图集的关键步骤。然而，目前的大多数配准算法都是基于单个大脑与选定模板的配对。这种独立的配对配准和模板的主观选择会给对齐的图像引入系统的配准误差和偏差，从而降低了检测微小的大脑变化的统计能力，例如对于阿尔茨海默病(AD)早期检测非常重要的微小的纵向结构和功能变化。为了解决这些限制，最近提出了分组配准和组间比较的方法，通过直接将所有个体受试者同时配准到他们的组均值来实现跨所有受试者的一致配准。然而，这些分组配准方法在识别微小的大脑差异方面的准确性和稳健性是有限的，因为由于配准中的噪声和不确定性，直接独立估计从每个受试者到组平均值的潜在的大的复杂变形可以使最初非常相似的图像(具有微小的差异)在配准后变得非常不同。此外，由于需要大量图像的同时配准以及计算机存储能力的限制，当前的分组配准方法只能处理少量的图像，例如几到几十个。这个项目的第一个目标是开发一种快速、稳健和准确的分组配准算法，该算法能够通过通用计算机同时处理大量图像集，例如数百或数千个图像。我们的核心思想是将大规模的分组注册问题分解为一系列分层的小规模注册问题，每个问题都可以用通用计算机高效地处理，并通过对注册问题的简化来稳健而准确地求解。此外，为了有效地比较两组(或更多组)，即分别从早期疾病患者和正常对照获得的组内配准，或者从基因同卵双胞胎获得的组间配准，我们进一步提出了一种新的组间配准方法，通过匹配两组的均值和它们在所有相应位置的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很大程度上识别出来，这使得能够检测到由于疾病造成的微小大脑萎缩，例如在AD早期阶段发现的疾病或双胞胎内部微小的大脑发育差异。这种组间配准和比对方法还可以推广到多组配准，并应用于新生儿早期双生子的纵向研究。研究所有这些新颖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配准方法以及组间配准和比较方法应用于ADNI(阿尔茨海默病神经成像计划)数据集，以早期检测AD，并应用于新生儿数据集，以研究双胞胎之间的微小大脑发育差异。该方法的性能将得到广泛的验证，并与成对配准方法和其他分组配准方法得到的结果进行比较。这些研究是第三个目标的主题。最终开发的算法将通过NITRC(神经成像信息学工具和资源信息交换所)免费提供给整个研究社区，就像我们对锤子注册算法(http://www.nitrc.org/projects/hammer/)，)所做的那样，这是NITRC中下载最多的工具之一。 公共卫生相关性：该项目旨在开发、测试和评估快速、可靠和准确的组注册和统计比较算法，以便有效地同时处理大量脑图像；能够检测微小的、复杂的组差异。这对于早期发现大脑疾病(例如阿尔茨海默病)和识别基因相同的双胞胎中微小的大脑发育差异非常重要。最终开发的算法将通过NITRC(神经成像信息学工具和资源信息交换所)免费提供给整个研究社区，就像我们对锤子注册算法(http://www.nitrc.org/projects/hammer/)，)所做的那样，这是目前NITRC最热门的下载工具之一。