Advanced neuroimaging visualization for cloud computing ecosystems

项目总结/摘要 失语症康复研究中心（C-STAR，P50-DC 014664）探索康复 从中风后的语言障碍，汇集了不同的专家团队， 传播科学、神经病学、心理学、统计学和神经成像。这个项目 获得广泛的磁共振成像（MRI）模态（结构，扩散， 动脉自旋标记，功能，静息状态），以了解脑 对语言至关重要的区域，改善预后，并确定最佳治疗或 每个人的薪酬策略。C-STAR的神经成像核心已经开发出 独特的方法来可视化这些不同的模式，以支持研究项目。 这些方法极大地扩展了我们流行的MRIcroGL和Survival工具， 利用现代硬件并提供独特的可视化效果。然而，这些工具 目前仅作为Windows、Linux和 MacOS。 本补充的主要目的是将我们的本地应用程序转换为基于云的应用程序 解决方案具体来说，我们将使用模块化的JavaScript和Vue.js组件， 嵌入到网页中。这将使用户能够直观地检查和传播结果 无论操作系统或设备如何。我们的新技术与任何网络兼容 支持WebGL 2标准的浏览器，从而支持计算机、平板电脑和 手机.流行的FSL管道的开发人员已经计划使用我们的模块来创建 交互式图像，以取代他们目前的FEAT工具生成的静态网页。而 对于这个利基市场，还有其他解决方案，但没有一个能提供我们所拥有的独特的可视化特性 在我们目前的应用中。事实上，我们将开发的基于网络的开源工具 我将展示这些功能，并允许其他开发人员使用我们的代码来改进他们自己的代码。 工具.因为我们使用GitHub和神经成像工具和资源合作实验室（NITRC， www.nitrc.org），用户将能够发布问题，请求新功能，并提交 改进.我们的自动化测试允许持续集成（CI）和验证 性能将我们当前的可视化工具套件转换为基于云的程序， 使数据更易于查找、访问、互操作和重用（FAIR）。

Project Summary/Abstract The Center for the Study of Aphasia Recovery (C-STAR, P50-DC014664) explores recovery from language impairments following stroke, bringing together a diverse team of specialists from communication sciences, neurology, psychology, statistics and neuroimaging. This project acquires a broad range of magnetic resonance imaging (MRI) modalities (structural, diffusion, arterial spin labelling, functional, resting state) from stroke survivors to understand the brain areas critical for language, improve prognosis, and identify the optimal treatment or compensation strategy for each individual. The neuroimaging core for C-STAR has developed unique methods to visualize these different modalities, in support of the research projects. These methods have dramatically extended our popular MRIcroGL and Surfice tools to better leverage modern hardware and provide unique visualizations. However, these tools are currently available only as native standalone desktop applications for Windows, Linux and MacOS. The primary aim of this supplement will be to translate our native applications to cloud-based solutions. Specifically, we will use modular JavaScript and Vue.js components that can be embedded into web pages. This will allow users to visually inspect and disseminate results regardless of operating system or device. Our novel techniques are compatible with any web browser that supports the WebGL 2 standard, thereby supporting computers, tablets and phones. Already, the developers of the popular FSL pipeline plan to use our modules to create interactive images to replace the static web pages generated by their current FEAT tool. While other solutions exist for this niche, none provide the unique visualization properties we have developed in our current applications. Indeed, the open source web-based tool we will develop will showcase these features and allow other developers to use our code to improve their own tools. Because we use GitHub and the NeuroImaging Tools & Resources Collaboratory (NITRC, www.nitrc.org), users will be able to post issues, request new features, and submit improvements. Our automated tests allow Continuous Integration (CI) and validated performance. Translation of our current suite of visualization tools to cloud-based programs will make data more findable, accessible, interoperable, and reusable (FAIR).