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.
