Anatomic Morphologic Analysis of MR Brain Images

MR 脑图像的解剖形态分析

• 批准号: 6801826
• 负责人: David Nelson Kennedy
• 金额: $ 55.86万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-09-30 至 2006-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6801826
• 关键词: bioimaging /biomedical imaging; brain disorder diagnosis; brain metabolism; brain morphology; brain neoplasms; cerebrovascular disorder diagnosis; clinical research; computer assisted diagnosis; computer program /software; computer simulation; computer system design /evaluation; diagnosis design /evaluation; human data; image processing; magnetic resonance imaging; neoplasm /cancer diagnosis; neuroanatomy; noninvasive diagnosis; stroke

DESCRIPTION (provided by applicant): 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.

描述（由申请人提供）：人脑的形态异常复杂;反映了神经元细胞体、轴突和其他组分的无数不可分割地交织在一起的系统。具有共同结构或功能特性的神经成分的分组构成了大脑的结构和功能神经解剖框架。表征大脑及其组成部分的形态学特性，使国家的最先进的磁共振成像（MRI）是非常适合于允许定量研究的参数相关的结构和功能的组成的人脑在体内。这笔赠款的目标是继续开发用于健康和疾病中大脑形态精确定量分析的工具和方法，并将这些工具的应用工具和结果传播给整个神经科学界。具体而言，我们将1）扩展我们先前开发的像素分割和形态量化方法，继续努力开发统一的神经解剖分割框架，并将这些工具过渡到常规可用软件平台上的临床应用; 2）继续我们先前开发的方法来表征正常受试者和病理患者人群中的形状和形状变化指标;和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。首先，一个统一的框架分割和分类，以支持神经学为基础的解剖形态已经出现。其次，这个统一的框架纳入了MRI数据的多光谱性质。第三，这一框架本质上包括与分割和分类过程相关的潜在不确定性的估计，这支持对给定方法的敏感性进行合理评估。第四，这种方法扩展了传统的“静态”图像分析，结合基于形状的异常检测分析。此外，我们还确定了许多临床应用领域，除了增强这些领域研究的分析能力外，还使我们能够优化结果分析的操作效率。具体而言，脑卒中和亨廷顿病患者的MRI数据的分割、分类和形状分析，以及适当的规范人群，为评价这些形态学分析技术的临床效用提供了重要的测试平台。