Scaling Volumetric Imaging, Analysis and Science Communication Using Immersive Virtual Reality: Administrative Supplement

使用沉浸式虚拟现实扩展体积成像、分析和科学传播：行政补充

• 批准号: 10887718
• 负责人: Gianfranco Doretto
• 金额: $ 39.29万
• 依托单位: ISTOVISR
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2026-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10887718
• 关键词: 4D Imaging; Active Learning; Address; Administrative Supplement; COVID-19 pandemic; Communication; Computer software; Data; Development; Education; Goals; Image; Learning; Learning Disabilities; Mission; Modeling; Monitor; Narration; Neurosciences; Performance; Phase; Science; Students; Technology; Testing; Three-Dimensional Image; Time; Work; experience; improved; instructor; interest; peer; response; tool; virtual reality environment

The original Phase IIb proposal addressed problems in analyzing, accessing and communicating increasingly large 3D and 4D image sets in neuroscience. This supplement request is motivated by two unforeseen developments since submission and review of the original application. The requested activities are necessary to maintain the high overall impact of the project. First, due to larger than expected positive response from educators who are interested in testing the technology, we need to refine the active learning features for instructors and student testers and advance performance monitoring to capture data rigorously. Second, there has been a very rapid improvement in Foundational AI Models (FM) but which were not at the appropriate stage of development to incorporate into our original application. We propose to address these developments by seamless incorporation of performance monitoring into the software, and by researching and integrating appropriate FMs into the user experience.

最初的IIb期提案解决了分析、访问和 在神经科学中，越来越多的3D和4D图像集被用于通信。这种补充剂 提出这一请求的原因是，自提交和审查《公约》以来， 原始应用程序。所要求的活动对于维持《公约》的高度总体影响是必要的。 该项目首先，由于教育工作者的积极反应比预期的要大， 为了测试这项技术，我们需要改进教师的主动学习功能 和学生测试员，并推进性能监控，以严格捕获数据。第二、 基础人工智能模型（FM）有了非常迅速的改进，但 适当的发展阶段，以纳入我们的原始应用程序。我们建议 通过将业绩监测无缝地纳入 软件，并通过研究和整合适当的FM到用户体验。