Continued Development and Maintenance of MriStudio

MriStudio的持续开发和维护

• 批准号: 8610697
• 负责人: MICHAEL I MILLER
• 金额: $ 35.44万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8610697
• 关键词: Adopted; Algorithms; Anatomy; Architecture; Archives; Atlases; Base of the Brain; Brain Mapping; Collaborations; Communities; Computer software; Computers; Data; Databases; Dependence; Development; Diffusion Magnetic Resonance Imaging; Environment; Grant; Hand; Image; Image Analysis; Link; Magnetic Resonance Imaging; Mails; Maintenance; Manuals; Memory; Methods; Online Systems; Process; Quality Control; Reporting; Research; Resolution; Resources; Services; Solutions; Supercomputing; System; Technology; Testing; Training; Universities; Update; Visit; base; image processing; imaging Segmentation; improved; neuroimaging; platform-independent; programs; public health relevance; research study; software development; symposium; tool; web based interface; web interface; white matter

The purpose of this grant is to support continued development and maintenance of MriStudio software developed in Johns Hopkins University. MriStudio is comprehensive software for MR image processing and analysis with emphasis on white matter anatomy. MriStudio consists of three modules, DtiStudio, DiffeoMap, and RoiEditor. DtiStudio was introduced in 2001 and remains one of the most widely used programs to process diffusion tensor imaging (DTI) data. DiffeoMap and RoiEditor were introduced in 2007, which provides a very unique environment to perform a cutting-edge image transformation and atlas-based automated image segmentation. What is especially unique about DiffeoMap is, because our advanced brain mapping algorithms are highly CPU and memory intensive, it adopts Cloud-type architecture, through which users can have access to our supercomputation resource. Currently, there are more than 6,500 registered uses. In this application, we propose to extend this service to the community through the following aims; Aim 1: Continued user support through training and dissemination Currently, two major channels of training and dissemination are through web-based resources (manuals and videos) and hand-on monthly 2-day tutorials. As the functionalities of MriStudio expand, we will continuously update the online materials and tutorials. Aim 2: Extension of the functionality Aim 2-1: Advanced diffusion MRI analysis package: Through the collaboration with Dr. Tournier, spherical harmonic decomvolution algorithm will be implemented. Aim 2-2: Automated and probabilistic tractography: We will incorporate a probabilistic tractography method based on dynamic programing and automate the ROI definition process. Aim 2-3: Quality control reporting: We will deploy a comprehensive and quantitative quality control reporting system, which is extremely important for automated analysis of large-scale studies. Aim 3: Cross-platform extension by adopting web-based interface We will develop web-based Cloud computation service, which will eliminate the platform-dependence. Aim 4: Completion of XNAT-based solution We will develop a server-based automated analysis pipeline that is linked to a research image database system, called XNAT. Aim 5: Deployment of multi-atlas-based brain segmentation algorithm We will deploy our multi-atlas technology in our server and make them available for testing to users through the Cloud computation system.

该补助金的目的是支持MriStudio软件的持续开发和维护 是在约翰霍普金斯大学发展起来的。MriStudio是用于MR图像处理的综合软件， 重点分析白色物质解剖。MriStudio由三个模块组成，DtiStudio，MapMap， RoiEditor的DtiStudio于2001年推出，至今仍是使用最广泛的程序之一 扩散张量成像（DTI）数据。2007年推出了RoiMap和RoiEditor， 独特的环境来执行先进的图像转换和基于地图集的自动化图像 细分AntoMap的独特之处在于，我们先进的大脑映射算法 高度CPU和内存密集型，它采用云式架构，用户可以通过它访问 我们的超级计算资源目前，已有超过6，500种注册用途。在本申请中，我们 建议透过以下目标，把这项服务扩展至社区; 目标1：通过培训和传播继续支持用户 目前，培训和传播的两个主要渠道是网上资源（手册 和视频）和每月2天的实践教程。随着MriStudio功能的扩展，我们将 不断更新在线材料和教程。 目标2：扩展功能 目标2-1：高级弥散MRI分析包：通过与Tournier博士的合作， 将实施谐波反卷积算法。 目标2-2：自动化和概率纤维束成像：我们将结合概率纤维束成像方法 基于动态编程和自动化的ROI定义过程。 目标2-3：质量控制报告：我们将部署全面和量化的质量控制报告 系统，这是非常重要的自动化分析的大规模研究。 目标3：通过采用基于Web的界面进行跨平台扩展 我们将开发基于Web的云计算服务，这将消除平台依赖性。 目标4：完成基于XNAT的解决方案 我们将开发一个基于服务器的自动化分析管道，该管道连接到研究图像数据库 系统，称为XNAT。 目标5：部署基于多图谱的脑分割算法 我们将在我们的服务器中部署我们的多地图集技术，并通过 云计算系统。