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: 9331363
• 负责人: Richard M Leahy
• 金额: $ 59.72万
• 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/9331363
• 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; 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; 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，我们开发了一系列用于分割的算法和软件， 结构和弥散MRI的配准、标记和分析，集成到开源软件包中 BrainSuite（http：//www.example.com）。我们的方法强调开发独立的、经过验证的模块 解决图像分析问题的各个方面，然后通过交互式 接口，以提供快速自动化或半自动化处理和图像可视化。命令行 还提供了用于大规模处理的使用相同功能的工具。该软件运行在 在Mac、Windows和Linux平台上保持一致。此续订应用程序以这些工具为基础， 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 每一个加工阶段。更新的重点是开发新的工具， 多模态数据，扩散数据的建模和分析，以及功能和结构的定量分析。 连通性。该项目有五个具体目标。目标1将发展先进的方法， 解剖、弥散和功能MRI分析，解释个体的结构和功能 差异这将改进现有的方法，这些方法仅依赖于结构（T1加权）图像， 定义主题之间的同源性。Aim 2将开发多模态的受试者内配准工具 明确说明和估计模态之间的分辨率差异的成像数据。组合 利用目标1中的学科间方法，这将促进组逐点和区域统计多模态 分析.目标3将开发工具，以分析具有灵活采样方案特点的扩散数据， 多个b值，解决了当前工具对日益广泛的 使用现代采集方案，如人类连接组项目和相关NIH所要求的方案 项目Aim 4将扩展BrainSuite Statistics工具箱，该工具箱使用Python和R来提供可扩展的 分析数据的统计框架;这一目标也将促进BrainSuite作为更大图像的一部分的使用。 通过继续支持标准格式并将我们的新工具开发为模块化， 命令行程序。发行版将与Nipype和NITRC-CE兼容。根据目标5，我们将 继续使用标准最佳实践进行软件开发。我们将开发基于Web的界面， 快速可视化和评估大型多学科研究的结果。用户支持将通过 在线论坛、教程、视频、文档和实践培训。新开发的分析方法 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。