ANATOMIC MORPHOLOGIC ANALYSIS OF MR BRAIN IMAGES

MR 脑图像的解剖形态分析

• 批准号: 6393721
• 负责人: David Nelson Kennedy
• 金额: $ 39.68万
• 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 2002-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6393721
• 关键词: bioimaging /biomedical imaging; brain disorder diagnosis; brain metabolism; brain neoplasms; cerebrovascular disorder diagnosis; classification; computer assisted diagnosis; computer program /software; computer simulation; computer system design /evaluation; diagnosis design /evaluation; functional magnetic resonance imaging; human data; image processing; neoplasm /cancer diagnosis; neuroanatomy; stroke

DESCRIPTION (Adapted from applicant's abstract): 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 which 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 by state-of-the-art MRI is 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 project is to continue development of 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, this project will: (1) extend previously developed pixel segmentation and morphological quantification methods to time-domain (functional MRI) and tensor-valued (diffusion-weighted MRI), continuing efforts to develop a unified neuroanatomic segmentation framework, and transition these tools to clinical applications on a routinely available software platform; (2) 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 proposal continues to take advantage of several unique aspects which distinguishes it from other related work. First, a unified framework for segmentation and classification in support of a neurologically-based anatomic morphology has begun to emerge. Second, this unified framework incorporates the multispectral nature of MRI data. Third, this framework intrinsically includes estimates of the resulting 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, several clinical application areas were identified which, in addition to fostering enhanced analytic capabilities to studies in these areas, permits optimization of operational efficiency in the resulting analysis. Specifically, the segmentation, classification and shape analysis of MRI data in patients with Huntington's Disease, stroke, and central nervous system neoplasms, as well as the appropriate normative subjects, provide a vital testbed for the evaluation of the clinical utility of these morphological analysis techniques.

描述(改编自申请人的摘要)： 人脑是异常复杂的；反映了无数千丝万缕的 神经元胞体、轴突和其他成分相互交织的系统。 共享共同结构或功能的神经组件的分组 属性包括结构和功能神经解剖学框架 大脑。脑和脑的形态特征的表征 其组件由最先进的核磁共振技术制成，非常适合于 与结构和性能相关参数的定量研究 活体中人脑的功能组成。这个项目的目标是 继续开发工具和方法，以实现精确的定量 分析健康和疾病中的脑形态，并传播 这些工具在神经科学中的应用工具和结果 社区作为一个整体。具体地说，本项目将：(1)延长 先前开发的像素分割和形态量化 时间域方法(功能磁共振)和张量值方法 (扩散加权磁共振)，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临床 在常规可用的软件平台上的应用程序；(2)特征 正常人和病变者的形态和形态变化指标 (3)分类工具的传播和比较 方法，以及图像分割和体积测量的结果 使用万维网对整个社区进行分析。 这项提议继续利用几个独特的方面， 区别于其他相关工作。第一，统一的框架， 分割和分类支持基于神经学的 解剖学已经开始出现。第二，这个统一的框架 融合了核磁共振数据的多光谱特性。第三，这个框架 本质上包括对由此产生的潜在不确定性的估计 与分割和分类过程相关联，其支持 对给定方法的灵敏度进行合理的评估。第四，这种做法 对传统的“静态”图像分析进行了扩展 基于形状分析的异常检测。此外，还有几个临床 确定了应用领域，除了促进增强 对这些领域的研究进行分析的能力，允许优化 结果分析中的运营效率。具体地说， 脑出血患者MRI数据的分割、分类和形状分析 亨廷顿病、中风和中枢神经系统肿瘤 作为适当的规范性主体，为 这些形态分析的临床应用价值评估 技巧。