Spatially Oriented Database for Digital Brain Images

描述(由申请人提供)：该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。这些组件的集成极大地帮助了我们的合作者管理和分析基于图像的临床试验(IBCT)，以阐明人脑结构与功能的关联。 在前一个周期中，我们添加了分割功能，实施了用于病变缺失分析的贝叶斯方法，并开始基于病变缺失数据构建功能性白质图谱。这些方法与Braid的可视化和其他统计工具一起，为同行评议的临床和工程学文献做出了贡献。然而，我们的经验也证明了编织的延伸的必要性。首先，我们的分割算法受到基于T1加权受损梯度回波图像的统计信号强度模型的限制。其次，由于注册技术的改进，我们现在拥有来自合作者的IBCT的高质量形态数据，但缺乏用于形态函数分析的复杂方法。第三，考虑到大脑的可塑性，构建高级功能图谱需要访问急性病变缺失数据；我们目前的合作者正在收集亚急性或慢性病变缺失数据。最后，我们可以通过实现基于开源软件的编织来促进与我们的合作者和其他同事的数据、软件和分析结果的交换。 为此，我们提出了四个具体目标来进一步扩展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；开发用于形态分析的贝叶斯方法，以补充我们用于病变缺失分析的贝叶斯方法；扩展Braid以适应急性中风数据，包括磁共振(MR)灌注和扩散序列；以及使用开源组件重新实现Braid。我们将使用来自三个IBCT的数据来测试这些图像分析、分割和急性笔画扩展以编织。

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.