Interactive Visual Analysis of Perfusion Data

Abstract

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 dimensionreduction 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.

Representative Factor Generation for the Interactive Visual Analysis of High-Dimensional Data

“…Fuchs et al [13] integrate methods from machine learning with interactive visual analysis to assist the user in knowledge discovery. Oeltze et al [28] demonstrate how statistical methods, such as correlation analysis and principal component analysis, are used interactively to assist the derivation of new features in the analysis of multivariate data. With our work, we contribute to this part of the literature by having the computational tools as inherent parts and integrating their results seamlessly to the interactive visual analysis cycle.…”

A Survey On Interactivity in Topic Models

“…It is particularly useful for hypotheses generation and validation, since it equips the user with tools enabling them to look at data sets in a variety of different ways and perspectives [8].…”

Survey of the Visual Exploration and Analysis of Perfusion Data

“…Recently, data analysis techniques and advanced information visualization techniques have been combined in order to efficiently explore the space of perfusion parameters [27]. In particular, a correlation analysis is carried out so as to investigate which perfusion parameters strongly correlate.…”

“…The original BEP fits into C2a, whereas the refined version belongs to C2b. The MAMMOEXPLORER (recall Section 7) and the exemplary implementation of the analysis pipeline presented by Oeltze et al [27] (recall Section 5.5) do not fit in either of these classes. Both rather represent a complex system comprising several techniques and integrate a sophisticated solution to link them.…”