BrainSuite: Software for Analysis and Visualization of Multimodal Brain Imaging Data

BrainSuite：多模态脑成像数据分析和可视化软件

• 批准号: 9451345
• 负责人: Richard M Leahy
• 金额: $ 57.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9451345
• 关键词: Address; Algorithmic Software; Anatomy; Area; Brain; Brain imaging; Collection; Computer software; Data; Data Analyses; Data Set; Detection; Development; Diffusion; Diffusion Magnetic Resonance Imaging; Documentation; Ensure; Evaluation; Excision; Feedback; Functional Magnetic Resonance Imaging; Goals; Health; Human; Image; Image Analysis; Imagery; Imaging Device; Individual; Joints; Label; Letters; Linux; Magnetic Resonance Imaging; Measures; Methods; Modality; Modeling; Modernization; Morphologic artifacts; Multimodal Imaging; Population; Positron-Emission Tomography; Process; Property; Public Health; Publications; Pythons; Research Personnel; Resolution; Rest; Running; Sampling; Scheme; Software Tools; Statistical Data Interpretation; Surveys; Testing; Training; Uncertainty; United States National Institutes of Health; X-Ray Computed Tomography; base; connectome; data modeling; file format; flexibility; graphical user interface; image registration; image visualization; imaging software; imaging study; improved; in vivo imaging; innovation; interoperability; multimodality; nervous system disorder; open source; programs; reconstruction; simulation; single photon emission computed tomography; software development; statistics; tool; treatment optimization; web based interface; white matter

Project Summary Over the past 16 years, we have developed a collection of algorithms and software for the segmentation, registration, labeling, and analysis of structural and diffusion MRI, integrated into the open source package BrainSuite (http://brainsuite.org). Our approach emphasizes the development of separate, validated modules addressing each aspect of the image analysis problem, which are then integrated through an interactive interface, to provide fast automated or semi-automated processing and image visualization. Command line tools using the same functions are also provided for large scale processing. The software runs on and is consistent across Mac, Windows, and Linux platforms. This renewal application builds on these tools with a continued emphasis on the ability to process large (now multimodal) data sets while simultaneously retaining the ability to rapidly visualize, review, and where necessary modify intermediate results to optimize the fidelity of each stage of processing. The renewal emphasizes development of new tools for coregistration of multimodal data, modeling and analysis of diffusion data, and quantitative analysis of functional and structural connectivity. The project has five specific aims. Aim 1 will develop advanced methods for intersubject anatomical, diffusion, and functional MRI analysis that account for individual structural and functional differences. This will improve upon existing methods that rely solely on structural (T1-weighed) images to define homologies between subjects. Aim 2 will develop tools for intrasubject coregistration of multimodal imaging data that explicitly account for and estimate resolution differences between modalities. In combination with the intersubject methods in Aim 1, this will facilitate group pointwise and regional statistical multimodal analysis. Aim 3 will develop tools to analyze diffusion data characterized by flexible sampling schemes and multiple b-values, addressing the limited ability of current tools to model data produced by increasingly widely used modern acquisition schemes such as those required by the Human Connectome Project and related NIH projects. Aim 4 will expand the BrainSuite Statistics toolbox, which uses Python and R to provide an extensible statistical framework for analyzing data; this aim will also facilitate the use of BrainSuite as part of larger image analysis pipelines by continuing to support standard formats and developing our new tools as modular command line programs. Distributions will be compatible with Nipype and NITRC-CE. Under Aim 5, we will continue software development employing standard best practices. We will develop web-based interfaces for rapidly visualizing and evaluating results from large, multisubject studies. User support will be provided through online forums, tutorials, videos, documentation, and hands-on training. New analysis methods developed in the above aims will be validated through simulation and evaluation on existing in vivo imaging data.

项目摘要 在过去的16年里，我们开发了一系列分割算法和软件， 结构和扩散磁共振成像的注册、标记和分析，集成到开放源码包中 智能套件(http://brainsuite.org).我们的方法强调开发单独的、经过验证的模块 解决图像分析问题的各个方面，然后通过交互 界面，以提供快速自动或半自动处理和图像可视化。命令行 还提供了使用相同功能的工具来进行大规模处理。该软件运行在并正在运行 在Mac、Windows和Linux平台上保持一致。此续订应用程序在这些工具的基础上构建 继续强调处理大型(现在是多模式)数据集的能力，同时保留 能够快速可视化、查看并在必要时修改中间结果以优化保真度 处理的每个阶段。更新的重点是开发新的工具，用于联合注册 多模式数据、扩散数据的建模和分析以及功能和结构的量化分析 连通性。该项目有五个具体目标。目标1将为学科间开发先进的方法 说明个体结构和功能的解剖、弥散和功能MRI分析 不同之处。这将改进仅依赖结构(T1加权)图像的现有方法 定义主题之间的同源性。AIM 2将开发用于多模式对象内共同配准的工具 显式解释和估计不同医疗设备分辨率差异的成像数据。结合在一起 通过目标1中的受试者间方法，这将促进按点分组和区域统计多式联运 分析。AIM 3将开发工具来分析扩散数据，其特点是灵活的抽样方案和 多个b值，解决了当前工具对日益广泛的 使用现代采购方案，如人类连接组项目和相关NIH所需的方案 项目。AIM 4将扩展BrainSuite统计工具箱，该工具箱使用Python和R提供可扩展的 用于分析数据的统计框架；这一目标还将促进将BrainSuite用作更大图像的一部分 通过继续支持标准格式并以模块化形式开发我们的新工具来分析管道 命令行程序。发行版将与Nipype和NITRC-CE兼容。在目标5下，我们将 继续使用标准最佳实践进行软件开发。我们将为以下项目开发基于Web的界面 快速可视化和评估来自大型、多主题研究的结果。用户支持将通过以下方式提供 在线论坛、教程、视频、文档和实践培训。开发了新的分析方法 上述目标将通过对现有活体成像数据的模拟和评估来验证。