Continued Development and Maintenance of MriStudio

MriStudio的持续开发和维护

• 批准号: 9118340
• 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/9118340
• 关键词: Adopted; Algorithms; Anatomy; Architecture; Archives; Atlases; Base of the Brain; Brain Mapping; Collaborations; Communities; Computer Analysis; Computer software; Data; Databases; Dependence; Development; Diffusion Magnetic Resonance Imaging; Environment; Grant; Hand; Health; Image; Image Analysis; Link; Magnetic Resonance Imaging; Maintenance; Manuals; Memory; Methods; Multicenter Studies; Online Systems; Process; Quality Control; Reporting; Research; Resolution; Resources; Services; Supercomputing; System; Technology; Testing; Training; Universities; Update; Visit; base; image processing; imaging Segmentation; improved; neuroimaging; platform-independent; programs; research study; software development; symposium; technology diffusion; tool; web based interface; web interface; white matter

DESCRIPTION (provided by applicant): 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.

描述(申请人提供)：这笔资金的目的是支持约翰霍普金斯大学开发的MriStudio软件的继续开发和维护。MriStudio是一款全面的磁共振图像处理和分析软件，侧重于脑白质解剖。MriStudio由三个模块组成：DtiStudio、DiffeoMap和RoiEditor。DtiStudio于2001年推出，至今仍是处理扩散张量成像(DTI)数据最广泛使用的程序之一。DiffeoMap和RoiEditor于2007年推出，它提供了一个非常独特的环境来执行尖端图像转换和基于atlas的自动图像分割。DiffeoMap特别独特的是，由于我们先进的脑图算法对CPU和内存的密集度很高，它采用了云式架构，用户可以通过云架构访问我们的超算资源。目前，已登记的用途超过6500项。在这项申请中，我们建议通过以下目标将这项服务扩展到社区：目标1：通过培训和传播继续为用户提供支持目前，培训和传播的两个主要渠道是通过基于网络的资源(手册和视频)和每月两天的实践教程。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：功能扩展目标2-1：高级扩散磁共振分析程序包：通过与Tournier博士的合作，将实现球谐去卷积算法。目标2-2：自动化和概率跟踪图：我们将结合基于动态规划的概率跟踪图方法，并自动化ROI定义过程。目标2-3：质量控制报告：我们将部署一个全面和定量的质量控制报告系统，这对大规模研究的自动化分析极其重要。目标3：采用基于Web的界面进行跨平台扩展。我们将发展基于网络的云计算服务，消除平台依赖。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分析管道，该管道连接到一个名为XNAT的研究图像数据库系统。目标5：部署基于多图谱的脑分割算法，将我们的多图谱技术部署在我们的服务器上，并通过云计算系统向用户提供测试。