Interactive Visual Analysis of Perfusion Data
Steffen Oeltze, Helmut Doleisch, Helwig Hauser, Philipp Muigg, Bernhard Preim
ARTICLE,
IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG),
nov, 2007
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.
Published
IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)
- Volume: 13
- Number: 6
- Pages: 1392-1399
- Publisher: IEEE Computer Society
- ISSN: 1077-2626
- Event: IEEE Visualization 2007
- Location: Sacramento, California, USA
- Date: nov 2007
- URL: http://dx.doi.org/10.1109/TVCG.2007.70569
Media
BibTeX
@article{oeltze07perfusion,
author = {Steffen Oeltze and Helmut Doleisch and Helwig Hauser and Philipp Muigg and Bernhard Preim},
title = {Interactive Visual Analysis of Perfusion Data},
journal = {IEEE Transactions on Visualization and Computer Graphics (IEEE TVCG)},
volume = {13},
number = {6},
year = {2007},
month = {nov},
issn = {1077-2626},
pages = {1392-1399},
publisher = {IEEE Computer Society},
location = "Sacramento, California, USA",
event = {IEEE Visualization 2007},
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.},
URL = {http://dx.doi.org/10.1109/TVCG.2007.70569},
}
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