ANATOMIC MORPHOLOGIC ANALYSIS OF MR BRAIN IMAGES

MR 脑图像的解剖形态分析

• 批准号: 7369390
• 负责人: DAVID N KENNED
• 金额: $ 0.51万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7369390
• 关键词: ANATOMIC; MORPHOLOGIC; ANALYSIS; MR; BRAIN

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The morphology of the human brain is exceptionally complex; reflecting a myriad of inextricably intertwined systems of neuronal cell bodies, axons, and other components. Groupings of neural components that share common structural or functional properties comprise the structural and functional neuroanatomic framework of the brain. Characterization of the morphologic properties of the brain and its component parts, as enabled by state-of-the-art magnetic resonance imaging (MRI) is exceptionally well suited to permit a quantitative study of the parameters relevant to the structural and functional makeup of the human brain in vivo. The goal of this grant is to continue to develop tools and methods for the precise quantitative analysis of brain morphology in health and disease, and to disseminate the tools and results of the application of these tools to the neuroscience community as a whole. Specifically, we will 1) extend our previously developed pixel segmentation and morphological quantification methods, continuing our efforts to develop a unified neuroanatomic segmentation framework and transition these tools to clinical applications on a routinely available software platform; 2) continue our previously developed methods to characterize shape and shape change metrics in normal subjects and pathological patient populations; and 3) dissemination of segmentation tools and comparison methods, as well as the results of image segmentation and volumetric analysis to the community as a whole using the World Wide Web.This application continues to take advantage of several unique aspects that distinguishes it from other related work. First, a unified framework for segmentation and classification in support of a neurologically-based anatomic morphology has emerged. Second, this unified framework incorporates the multispectral nature of MRI data. Third, this framework intrinsically includes estimates of the underlying uncertainty associated with the segmentation and classification process, which supports a rational assessment of sensitivity of a given method. Fourth, this approach expands upon traditional "static" image analysis by incorporation of shape-based analysis for anomaly detection. In addition, we have identified a number of clinical application areas which, in addition to fostering enhanced analytic capabilities to studies in these areas, permits us to optimize the operational efficiency of the resulting analysis. Specifically, the segmentation, classification and shape analysis of MRI data in patients with stroke and Huntington's disease, as welt as the appropriate normative populations, provide a vital testbed for the evaluation of the clinical utility of these morphological analysis techniques.

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。人脑的形态异常复杂；反映了由神经元细胞体、轴突和其他组成部分组成的无数错综复杂的系统。具有共同结构或功能特性的神经组件的分组构成了大脑的结构和功能神经解剖框架。在最先进的磁共振成像(MRI)的支持下，对大脑及其组成部分的形态特性进行表征非常适合于对与活体中人脑的结构和功能组成相关的参数进行定量研究。这笔赠款的目标是继续开发工具和方法，对健康和疾病中的大脑形态进行精确的定量分析，并向整个神经科学界传播应用这些工具的工具和成果。具体地说，我们将1)扩展我们以前开发的像素分割和形态量化方法，继续努力开发一个统一的神经解剖分割框架，并在常规可用的软件平台上将这些工具过渡到临床应用；2)继续我们以前开发的方法，以表征正常受试者和病理患者群体中的形状和形状变化度量；以及3)使用万维网向整个社区传播分割工具和比较方法，以及图像分割和体积分析的结果。这个应用程序继续利用几个独特的方面，使其有别于其他相关工作。首先，一个统一的分割和分类框架已经出现，以支持基于神经学的解剖形态。其次，这个统一的框架结合了磁共振数据的多光谱特性。第三，这一框架本质上包括对与分割和分类过程相关的潜在不确定性的估计，这支持了对给定方法的敏感性的合理评估。第四，该方法将基于形状的分析引入到异常检测中，扩展了传统的静态图像分析。此外，我们已经确定了一些临床应用领域，除了培养这些领域研究的增强分析能力外，还允许我们优化结果分析的操作效率。具体地说，对中风和亨廷顿病患者的MRI数据进行分割、分类和形状分析，以及适当的正常人群，为评估这些形态分析技术的临床应用提供了重要的试验台。