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（由申请人提供）：这笔赠款的目的是支持约翰·霍普金斯大学开发的 MriStudio 软件的持续开发和维护。 MriStudio 是一款用于 MR 图像处理和分析的综合软件，重点关注白质解剖学。 MriStudio 由三个模块组成：DtiStudio、DiffeoMap 和 RoiEditor。 DtiStudio 于 2001 年推出，至今仍然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图集的自动图像分割。 DiffeoMap 的独特之处在于，由于我们先进的大脑映射算法是高度 CPU 和内存密集型的，因此它采用云式架构，用户可以通过该架构访问我们的超级计算资源。目前，已有超过 6,500 个注册用途。在此应用程序中，我们建议通过以下目标将该服务扩展到社区；目标 1：通过培训和传播持续提供用户支持 目前，培训和传播的两个主要渠道是通过网络资源（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资料和教程。目标 2：功能扩展 目标 2-1：高级扩散 MRI 分析包：通过与 Tournier 博士的合作，将实现球谐解卷积算法。目标 2-2：自动化概率纤维束成像：我们将采用基于动态编程的概率纤维束成像方法，并自动化 ROI 定义过程。目标2-3：质量控制报告：我们将部署全面、定量的质量控制报告系统，这对于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。 我们将开发基于网络的云计算服务，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分析管道，该管道连接到称为 XNAT 的研究图像数据库系统。目标5：部署基于多图谱的大脑分割算法，我们将在我们的服务器中部署我们的多图谱技术，并通过云计算系统供用户测试。