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

近年来，由于医学成像设备和技术的快速发展，医学成像数据量以前所未有的速度增长。在许多医学应用领域，从多模态、多维度的数据中评估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的多模态，多维的跨学科数据的集合阻碍了有效利用和更好地理解所获得的，大规模的数据。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，用于跨学科，多维，多测量脑成像数据的集成，解释和比较分析。这个可视化框架扩展了信息可视化和医学可视化的最新技术。它采用了新的几何特征分析，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内部的空间变化信息。该研究项目提供了一个有用的多模态成像分析框架，有助于不同的应用领域，如神经系统疾病的临床诊断，通过定量图像分析进行药物疗效分析，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、研究人员和公众都具有临床和教育价值。研究和教育部分的整合促进了计算机科学和神经科学之间的进一步互动。

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.