III: Small:Collaborative Research: Coordinated Visualization for Comparative Analysis of Cross-Subject, Multi-Measure, Multi-Dimensional Brain Imaging Data

III：小：协作研究：跨受试者、多测量、多维度脑成像数据比较分析的协调可视化

• 批准号: 0914631
• 负责人: Yizhou Yu
• 金额: $ 25万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0914631&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Coordinated

The amount of medical imaging data has been growing at an unprecedented rate in recent years due to the rapid advancement in medical imaging devices and technologies. In many medical application areas, assessment of similarity and disparity from multimodality, multi-dimensional data across subjects plays a central role. Current software in exploration and visualization of a collection of multimodality, multidimensional cross-subject data impedes the effective utilization and better understanding of acquired, large-scale data. The overall objective of this research is to design and develop a unique coordinated visualization framework, based on advanced geometric computing as well as data and visual abstraction, for integrating, interpreting and comparative analysis of cross-subject, multidimensional, multi-measure brain imaging data. This developed visualization framework extends the state-of-the-art in both information visualization and medical visualization. It employs novel geometric feature analysis for better supporting surface matching and shape comparison, and generalizes data warehousing technology to spatially varying information deep inside multi-dimensional medical images. The research outcomes are disseminated through traditional publications as well as the Internet.This research project provides a useful multimodality imaging analytics framework which contributes to diverse application domains, such as clinical diagnosis of neurological disorders, drug efficacy analysis through quantitative image analysis, and basic neuroscience. In addition, the sharing of data and software tools has both clinical and educational values for students, physicians, researchers, and the general public. The integration of the research and education components promotes further interactions between computer science and neuroscience.

近年来，由于医学成像设备和技术的快速发展，医学成像数据的数量以前所未有的速度增长。在许多医学应用领域，评估多模态、多维数据跨学科的相似性和差异性起着核心作用。目前的软件在探索和可视化多模态，多维交叉学科数据的集合阻碍了有效利用和更好地理解获得的大规模数据。本研究的总体目标是设计和开发一个独特的协调可视化框架，基于先进的几何计算以及数据和视觉抽象，用于整合、解释和比较分析跨学科、多维、多测量的脑成像数据。这个开发的可视化框架扩展了信息可视化和医学可视化的最新技术。它采用新颖的几何特征分析，更好地支持表面匹配和形状比较，并将数据仓库技术推广到多维医学图像深处的空间变化信息。研究成果既通过传统出版物传播，也通过互联网传播。本研究提供了一个有用的多模态影像分析框架，有助于不同的应用领域，如神经系统疾病的临床诊断，通过定量影像分析药物疗效分析，以及基础神经科学。此外，数据和软件工具的共享对学生、医生、研究人员和公众具有临床和教育价值。研究和教育组件的整合促进了计算机科学和神经科学之间的进一步互动。