Issue No. 06 - November/December (2007 vol. 13)
Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimension reduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.
correlation methods, medical image processing, principal component analysis,interactive visual analysis, perfusion data evaluation, dynamic medical image data, regional blood flow, human tissue, medical diagnosis, evaluation methods, contrast agent enhancement, diagnostic evaluation, feature-based approaches, correlation analysis, principal component analysis, PCA, statistical methods, coronary heart disease,Data analysis, Principal component analysis, Biomedical imaging, Medical diagnostic imaging, Blood flow, Humans, Medical diagnosis, Diseases, Statistical analysis, Focusing,Multi-field Visualization, Visual Data Mining, Time-varying Volume Data, Integrating InfoVis/SciVis
"Interactive Visual Analysis of Perfusion Data", IEEE Transactions on Visualization & Computer Graphics, vol. 13, no. , pp. 1392-1399, November/December 2007, doi:10.1109/TVCG.2007.70569