DISSEMINATION OF CROSS-PLATFORM SOFTWARE FOR ARTIFACT DETECTION AND REGION OF INT

伪影检测和INT区域跨平台软件的传播

• 批准号: 7501200
• 负责人: Satrajit Sujit Ghosh
• 金额: $ 16.22万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7501200
• 关键词: Adopted; Anatomy; Brain; Code; Commit; Communities; Computer software; Custom; Data; Data Analyses; Dependence; Dependency; Detection; Diagnosis; Documentation; Ensure; Environment; Feedback; Functional Imaging; Functional Magnetic Resonance Imaging; Goals; Image; Image Analysis; Imagery; Imaging technology; Individual; Informatics; Java; Laboratories; Language; Lead; Libraries; Magnetic Resonance Imaging; Maps; Methods; Morphologic artifacts; Neurosciences; Numbers; Operating System; Phase; Public Health; Pythons; Quality Control; Research; Resources; Running; Site; Software Engineering; Software Tools; Specificity; Staging; Statistical Methods; Stream; Testing; United States National Institutes of Health; Work; base; cost; design; improved; interest; interoperability; nervous system disorder; neuroimaging; software development; statistics; tool; usability

DESCRIPTION (provided by applicant): The general aim of this project is to disseminate software that will enhance the quality and consistency of analysis of functional magnetic resonance imaging (fMRI) data. The goal is to enhance, document and make publicly available software for artifact detection, statistical region-of-interest analysis and visualization of fMRI data. Better quality control methods and statistical methods will generate more credible and repeatable results, which should therefore lead to faster biomedical discoveries and to potential reduction in the cost of running fMRI studies. From a software engineering standpoint, the goal is to offer a well-designed, cross- platform, extensible software that is intuitive and easy to use. Two existing MATLAB-based software packages will be enhanced, integrated and distributed: the ARtifact detection Tools (ART) and the Region of Interest Analysis of Parcellated Imaging Data (RAPID) software (Nieto-Castanon et al., 2003). To achieve interoperability, the integrated software will be converted from MATLAB to C/C++ and wrappers will be provided for use of this software from other languages such as Python, Java, Tcl/Tk and MATLAB. Custom modules will be created for use of this software within some functional analysis streams (FMRIB Software Library, FSL, Smith et al., 2004, Statistical Parametric Mapping, SPM, Friston 2003, FreeSurfer Functional Analysis STream, FSFAST, Tsao et al., 2003 and Neuroimaging in Python, NiPy), and support will be provided to embed the software in other analysis streams. To achieve dissemination, the software will be beta-tested at several laboratories doing fMRI research and will be maintained and supported through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website. The project will be carried out in three phases: (1) Integration and release of MATLAB-based software; (2) Conversion of the software to a C/C++ framework; and (3) Dissemination and support to ensure widespread use by the neuroimaging community. Throughout the project we will engage with the neuroimaging community through the NITRC website and, in particular, interact with the several laboratories that have committed to beta-testing the software. We will rely on community feedback to improve usability of the software. PUBLIC HEALTH RELEVANCE: The proposed project aims to disseminate software for sophisticated statistical analyses (Nieto-Castanon et al., 2003) and quality control of functional magnetic resonance imaging (fMRI) data. Providing these tools should enable users of fMRI technology to produce more detailed, consistent and reliable results. This will lead to better understanding of how the brain works and thereby directly impact approaches to diagnosing and treating neurological disorders.

描述（由申请人提供）：该项目的总体目标是传播能够提高功能磁共振成像（fMRI）数据分析质量和一致性的软件。目标是增强、记录和公开可用的伪影检测、统计感兴趣区域分析和功能磁共振成像数据可视化软件。更好的质量控制方法和统计方法将产生更可信和可重复的结果，因此，这将导致更快的生物医学发现，并有可能降低运行功能磁共振成像研究的成本。从软件工程的角度来看，目标是提供一个设计良好的、跨平台的、可扩展的、直观的、易于使用的软件。现有的两个基于matlab的软件包将被增强、集成和分发：伪像检测工具（ART）和感兴趣区域分割成像数据分析（RAPID）软件（Nieto-Castanon等人，2003）。为了实现互操作性，集成软件将从MATLAB转换为C/ c++，并提供包装器，以便从Python、Java、Tcl/Tk和MATLAB等其他语言使用该软件。定制模块将在一些功能分析流（FMRIB软件库，FSL， Smith等人，2004年，统计参数映射，SPM， Friston 2003年，FreeSurfer功能分析流，FSFAST， Tsao等人，2003年和Neuroimaging in Python， NiPy）中创建使用该软件，并将提供支持将软件嵌入到其他分析流中。为了实现传播，该软件将在几个从事功能磁共振成像研究的实验室进行beta测试，并将通过神经成像信息学工具和资源交换中心（NITRC）网站进行维护和支持。项目将分三个阶段进行：(1)基于matlab软件的集成和发布；(2)将软件转换为C/ c++框架；(3)传播和支持，以确保神经影像学社区的广泛使用。在整个项目中，我们将通过NITRC网站与神经成像社区进行接触，特别是与承诺对软件进行beta测试的几个实验室进行互动。我们将依靠社区反馈来提高软件的可用性。公共卫生相关性：拟议的项目旨在传播用于复杂统计分析（Nieto-Castanon等人，2003年）和功能磁共振成像（fMRI）数据质量控制的软件。提供这些工具将使fMRI技术的用户能够产生更详细、一致和可靠的结果。这将有助于更好地理解大脑的工作原理，从而直接影响神经系统疾病的诊断和治疗方法。

项目成果

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python.

• DOI: 10.3389/fninf.2011.00013
• 发表时间: 2011
• 期刊: Frontiers in neuroinformatics
• 影响因子: 3.5
• 作者: Gorgolewski K;Burns CD;Madison C;Clark D;Halchenko YO;Waskom ML;Ghosh SS
• 通讯作者: Ghosh SS

Optimized design and analysis of sparse-sampling FMRI experiments.

• DOI: 10.3389/fnins.2013.00055
• 发表时间: 2013
• 期刊: Frontiers in neuroscience
• 影响因子: 4.3
• 作者: Perrachione TK;Ghosh SS
• 通讯作者: Ghosh SS