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.

这笔赠款的目的是支持 MriStudio 软件的持续开发和维护 约翰·霍普金斯大学开发。 MriStudio 是一款用于 MR 图像处理和 分析重点是白质解剖学。 MriStudio由三个模块组成，DtiStudio、DiffeoMap、 和 RoiEditor。 DtiStudio 于 2001 年推出，至今仍然是使用最广泛的处理程序之一 扩散张量成像（DTI）数据。 DiffeoMap 和 RoiEditor 于 2007 年推出，提供了非常好的 执行尖端图像转换和基于图集的自动化图像的独特环境 分割。 DiffeoMap 的特别独特之处在于，我们先进的大脑映射算法 CPU和内存高度密集，采用云式架构，用户可以通过该架构进行访问 到我们的超级计算资源。目前，已有超过 6,500 个注册用途。在这个应用程序中，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基于网络的资源（手册 和视频）以及每月为期 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：先进的扩散 MRI 分析包：通过与 Tournier 博士的合作，球形 将实施谐波解卷积算法。 目标 2-2：自动化概率纤维束成像：我们将采用概率纤维束成像方法 基于动态规划并自动化 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，该管道链接到研究图像数据库 系统，称为 XNAT。 目标 5：部署基于多图谱的大脑分割算法 我们将在我们的服务器中部署我们的多图集技术，并通过以下方式向用户提供测试： 云计算系统。