Spatially Oriented Database for Digital Brain Images

数字脑图像空间定向数据库

• 批准号: 6791451
• 负责人: Edward H Herskovits
• 金额: $ 41.73万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-30 至 2008-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6791451
• 关键词: aging; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain injury; brain morphology; computational neuroscience; computer data analysis; computer program /software; computer system design /evaluation; digital imaging; human data; image processing; information system analysis; information systems; longitudinal human study; magnetic resonance imaging; neuroanatomy; positron emission tomography; statistics /biometry; stroke

DESCRIPTION (provided by applicant): The overall goal of this project is the integration of advanced image-processing, data-analysis, and data management techniques into a brain-image database (BRAID), for the support of image-based clinical trials. The integration of these components has greatly aided our collaborators' management and analysis of image-based clinical trials (IBCTs) for the elucidation of structure-function associations for the human brain. In the previous cycle, we added segmentation capabilities, implemented Bayesian methods for lesion-deficit analysis, and began construction of a functional white-matter atlas based on lesion-deficit data. These methods, in conjunction with BRAID's visualization and other statistical tools, have resulted in contributions to the peer-reviewed clinical and engineering literature. However, our experience also demonstrates the need for extensions to BRAID. First, our segmentation algorithm is limited by its foundation on a statistical signal-intensity model of T 1-weighted spoiled gradient-recalled echo images. Second, given improvements in our registration techniques, we now have high-quality morphological data from our collaborators' IBCTs, but lack sophisticated methods for morphology-function analysis. Third, given the brain's plasticity, construction of an advanced functional atlas requires access to acute lesion-deficit data; our current collaborators are collecting subacute or chronic lesion-deficit data. Finally, we could facilitate the interchange of data, software, and analytic results with our collaborators and other colleagues by implementing BRAID based on open-source software. Towards these ends, we propose four specific aims to further extend BRAID's functionality: extension of our segmentation algorithm to incorporate more complex spatial and signal-intensity information; development of Bayesian methods for morphological analysis, to complement our Bayesian methods for lesion-deficit analysis; extension of BRAID to accommodate acute-stroke data, including magnetic-resonance (MR) perfusion and diffusion sequences; and reimplementation of BRAID using open-source components. We will test these image analysis, segmentation, and acutestroke extensions to BRAID using data from three IBCTs.

描述（由申请人提供）：本项目的总体目标是将先进的图像处理、数据分析和数据管理技术集成到脑图像数据库（BRAID）中，以支持基于图像的临床试验。这些组件的整合极大地帮助了我们的合作者对基于图像的临床试验（IBCT）的管理和分析，以阐明人脑的结构-功能关联。 在上一个周期中，我们增加了分割功能，实施了用于病变缺陷分析的贝叶斯方法，并开始构建基于病变缺陷数据的功能性白质图谱。这些方法与BRAID的可视化和其他统计工具相结合，为同行评审的临床和工程文献做出了贡献。然而，我们的经验也表明需要扩展BRAID。首先，我们的分割算法是有限的，其基础上的统计信号强度模型的T1加权破坏梯度回波图像。其次，由于我们的配准技术的改进，我们现在从我们的合作者的IBCT获得了高质量的形态学数据，但缺乏复杂的形态学功能分析方法。第三，考虑到大脑的可塑性，构建高级功能图谱需要获得急性病变缺陷数据;我们目前的合作者正在收集亚急性或慢性病变缺陷数据。最后，我们可以通过实现基于开源软件的BRAID来促进与我们的合作者和其他同事之间的数据、软件和分析结果的交换。 为此，我们提出了四个具体的目标，以进一步扩展BRAID的功能：扩展我们的分割算法，以纳入更复杂的空间和信号强度信息;贝叶斯方法的形态分析的发展，以补充我们的贝叶斯方法的病变缺陷分析;扩展BRAID，以适应急性中风的数据，包括磁共振（MR）灌注和扩散序列;并使用开源组件重新实现BRAID。我们将使用三个IBCT的数据测试这些图像分析、分割和对BRAID的急性中风扩展。