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（由申请人提供）：MRI等现代成像可以提供对整个大脑的安全，无创的测量，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，以及监测药理干预措施的影响。这非常需要开发高度自动化，准确且可靠的测量工具，以分析大型神经图像数据集。图像注册作为重要的图像测量工具吸引了巨大的科学兴趣，因为它是整合和比较来自不同个人或组的数据的关键步骤，以及反映了一组个人内结构和功能变化的统计图谱的发展。但是，大多数当前的注册算法都是基于与选定模板对单个大脑的成对注册。这种独立的成对登记和主观选择模板可以引入系统的注册误差和对对齐的图像的偏见，从而降低了检测细微的大脑变化的统计能力，例如，小纵向结构和功能变化，对于早期检测阿尔茨海默氏病（AD）很重要。为了解决这些局限性，最近提出了通过同时注册所有各个受试者的组平均均值来实现所有受试者的一致注册，以实现所有受试者的一致注册。但是，由于对识别微小的大脑差异，这些群体登记方法的准确性和鲁棒性受到限制，因为由于注册和注册中的噪声和不确定性，对每个受试者对群体平均可能直接对群体均值的潜在较大复杂变形的独立估计可以使最初非常相似的图像（具有微小的差异）变得非常不同。此外，由于大量图像所需的同时注册以及计算机内存能力的限制，因此当前的组件注册方法只能处理少数图像，例如，几十张图像。该项目的第一个目的是开发一种快速，健壮和准确的群体登记算法，该算法能够通过一般计算机同时处理大量图像，例如数百或数千个图像。我们的关键思想是将大规模的小组注册问题分配为一系列层次的小规模注册问题，每个型号可以通过一般计算机有效地处理，并可以通过简化注册问题来妥善而准确地解决。 此外，为了进行有效比较两个（或更多）组，即从早期患者和正常对照组或基因相同的双胞胎中分别获得，我们进一步提出了一种新型的相互作用方法来有效地对齐两个组，这不仅是匹配他们的平均值，而且在所有相应位置的统计分布。因此，可以大量识别两组之间的统计差异，从而使由于疾病（例如在AD的早期阶段或双胞胎内部的微小脑生长差异）引起的微小脑萎缩的检测。该组间的注册和比较方法也可以扩展到多组的注册，并在新生儿早期对双胞胎的纵向研究中应用。对所有这些新颖的小组间登记和比较方法的研究是第二个目标的主题。 最后，我们将将开发的小组注册方法以及组间注册和比较方法应用于ADNI（阿尔茨海默氏病神经影像学计划）数据集，以早日检测到AD，并将其用于新生儿数据集，以研究三胞胎内部小脑生长差异。该方法的性能将得到广泛的验证，并将其与通过配对登记方法以及其他小组注册方法获得的方法进行比较。这些研究是第三个目标的主题。 最终开发的算法将通过NITRC（神经影像学工具和资源交换所）免费提供给整个研究社区，就像我们使用Hammer Incodigentation Algorithm（http://wwwww.nitrc.org/projects/hammer/）一样，这是最高下载工具的工具。 公共卫生相关性：该项目旨在开发，测试和评估快速，健壮和准确的小组注册和统计比较算法，以有效地同时处理大量脑图像；能够检测微小的，复杂的群体差异。这对于早期检测脑疾病（例如阿尔茨海默氏病）和鉴定遗传相同双胞胎中微小的脑生长差异很重要。最终开发的算法将通过NITRC（神经影像学工具和资源交换所）免费提供给整个研究社区，就像我们使用Hammer Incodigentation Algorithm（http://wwwww.nitrc.org/projects/hammer/）一样，目前是最高下载工具。