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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

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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},

}






 Last Modified: Jean-Paul Balabanian, 2013-05-29