BRAINS Morphology and Image Analysis

描述(由申请者提供)：Brains计划是在过去15年中开发的，用于研究精神和神经疾病，已被全球350多个网站下载。它为脑形态分析提供了广泛的工具，包括空间归一化、图像配准、组织分类、自动结构识别、表面生成、扩散张量分析和化学位移成像部分体积校正。目前，Brains软件是爱荷华大学37项有效拨款和待决拨款以及46项外部资助的主要图像处理程序包。该软件为脑形态分析提供了可靠有效的图像处理流水线。在这项提议中，我们将使这条管道完全自动化，以便快速分析大规模的成像研究。这项工作将建立在将ITK工具包整合到Brains软件的基础上。我们建议简化维护，增强开发，并让更多的用户可以使用大脑。维护和代码强化任务将利用作为ITK项目一部分开发的几个开源工具(CMake和DART)。这将允许代码审查过程向程序的用户开放。目前的Brains用户界面将被Fltk取代，提供跨Windows、Macintosh和Unix/Linux平台的本地用户界面。为了评估大脑和其他几个神经成像软件应用程序，将使用思维研究所收集的独特的多中心数据集来衡量这些工具的站点内和站点间可靠性以及有效性。

DESCRIPTION (provided by applicant): The BRAINS program has been developed over the past fifteen years to study psychiatric and neurological disorders and has been downloaded by over 350 sites worldwide. It provides extensive tools for the analysis of brain morphology including spatial normalization, image co-registration, tissue classification, automated structure identification, surface generation, diffusion tensor analysis, and chemical shift imaging partial volume correction. Currently, the BRAINS software is the primary image processing package for 37 active grants and pending grants at The University of Iowa and 46 at outside sites. The software contains a reliable and valid image processing pipeline for analyzing brain morphology. In this proposal we will fully automate this pipeline allowing for large scale imaging studies to be rapidly analyzed. This work will build upon the integration of the ITK toolkit into the BRAINS software. We are proposing to streamline maintenance, enhance development, and make BRAINS available to a wider audience of users. The maintenance and code hardening tasks will leverage several open source tools (CMake and DART) that have been developed as part of the ITK project. This will allow the code review process to be open to users of the program. The current BRAINS user interface will be replaced with FLTK providing a native user interface across Windows, Macintosh and Unix/Linux based platforms. To evaluate BRAINS and several other neuroimaging software applications a unique multi-center dataset that has been collected by the MIND Institute will be used to measure intra- and inter-site reliability as well as validity of these tools.