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Interactive Visual Analysis of Perfusion Data
November/December 2007 (vol. 13 no. 6)
pp. 1392-1399
ASCII Text
x 
 
Steffen Oeltze, Helmut Doleisch, Helwig Hauser, Philipp Muigg, Bernhard Preim, "Interactive Visual Analysis of Perfusion Data," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1392-1399, November/December, 2007.
 
 
BibTex
x
 
@article{ 10.1109/TVCG.2007.70569,
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},
volume = {13},
number = {6},
issn = {1077-2626},
year = {2007},
pages = {1392-1399},
doi = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2007.70569},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Visualization and Computer Graphics
TI - Interactive Visual Analysis of Perfusion Data
IS - 6
SN - 1077-2626
SP1392
EP1399
EPD - 1392-1399
A1 - Steffen Oeltze,
A1 - Helmut Doleisch,
A1 - Helwig Hauser,
A1 - Philipp Muigg,
A1 - Bernhard Preim,
PY - 2007
KW - Multi-field Visualization
KW - Visual Data Mining
KW - Time-varying Volume Data
KW - Integrating InfoVis/SciVis
VL - 13
JA - IEEE Transactions on Visualization and Computer Graphics
ER -
 
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.

[1] N. Al-Saadi, M. Gross, A. Bornstedt, B. Schnackenburg, C. Klein, E. Fleck, and E. Nagel, Comparison of various parameters for determining an index of myocardial perfusion reserve in detecting coronary stenosis with cardiovascular magnetic resonance tomography. Z Kardiol, 90 (11): 824–34, Nov 2001.
[2] U. Behrens, J. Teubner, C. J. Evertsz, M. Walz, H. Jürgens, and H.-O. Peitgen, Computer-Assisted Dynamic Evaluation of Contrast-Enhanced-MRI. In Proc. of Computer Assisted Radiology, pages 362–367, 1996.
[3] J. Blaas, C. Botha, and F. Post, Interactive visualization of multi-field data using dynamically linked physical and feature space views. In Proc. of EuroVis, pages 123–130, 2007.
[4] W. Chen, M. Giger, U. Bick, and G. Newstead, Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Medical Physics, 33 (8): 2878–2887, 2006.
[5] P.L. Choyke, A. J. Dwyer, and M. V. Knopp, Functional tumor imaging with dynamic contrast-enhanced magnetic resonance imaging. J Magn Reson Imaging, 17 (5): 509–20, May 2003.
[6] E. Coto, S. Grimm, S. Bruckner, E. Gröller, A. Kanitsar, and O. Rodriguez, MammoExplorer: An Advanced CAD Application for Breast DCE-MRI. In Proc. of VMV, pages 91–98, 2005.
[7] J. A. den Boer and P. J. M. Folkers, MR perfusion and diffusion imaging in ischaemic brain disease. Medica Mundi, 41 (2): 20–35, 1997.
[8] H. Doleisch, M. Gasser, and H. Hauser, Interactive Feature Specification for Focus+Context Visualization of Complex Simulation Data. In Proc. of IEEE TCVG - EUROGRAPHICS Symp. on Vis., pages 239–248, 2003.
[9] H. Doleisch and H. Hauser, Smooth brushing for focus+context visualization of simulation data in 3D. Journal of WSCG, 10 (1): 147–154, 2002.
[10] G. Furnas, Generalized fisheye views. In Proc. of the ACM CHI '86 Conf. on Human Factors in Computing Systems, pages 16–23, 1986.
[11] D. L. Gresh, B. E. Rogowitz, R. L. Winslow, D. F. Scollan, and C. K. Yung, WEAVE: A System for Visually Linking 3-D and Statistical Visualizations, Applied to Cardiac Simulation and Measurement Data. In Proc. of IEEE Visualization, pages 489–492, 2000.
[12] H. K. Hahn and H.-O. Peitgen, The skull stripping problem in MRI solved by a single 3d watershed transform. In Proc. of MICCAI, volume 1935 of LNCS, pages 134–143. Springer, 2000.
[13] H. Hauser, Generalizing focus+context visualization. In Scientific Visualization: The Visual Extraction of Knowledge from Data, pages 305–327, 2005.
[14] G. Hellwig, G. Brix, J. Griebel, R. Lucht, S. Delorme, M. Siebert, and K.-H. Englmeier, Dynamic MR mammography: three-dimensional realtime visualization of contrast enhancement in virtual reality. Academic Radiology, 9: 1255–1263, 2002.
[15] I. T. Jolliffe, Principal Component Analysis. Series in Statistics. Springer, 2nd edition, 2002.
[16] S. Kohle, B. Preim, J. Wiener, and H.-O. Peitgen, Exploration of Time-varying Data for Medical Diagnosis. In Proc. of VMV, pages 31–38, 2002.
[17] M. König, E. Klotz, and L. Heuser, Perfusion CT in Acute Stroke: Characterization of Cerebral Ischemia using Parameter Images of Cerebral Blood Flow and their Therapeutic Relevance. Electromedica, 66 (2): 61–67, 1998.
[18] C. K. Kuhl, P. Mielcareck, S. Klaschik, C. Leutner, E. Wardelmann, J. Gieseke, and H. H. Schild, Dynamic Breast MR Imaging: Are Signal Intensity Time Course Data Useful for Differential Diagnosis of Enhancing Lesions? Radiology, 211 (1): 101–110, 1999.
[19] M. Lysaker, A. Lundervold, and X. Tai, Noise Removal Using Fourth-Order Partial Differential Equation with Applications to Medical Magnetic Resonance Images in Space and Time. IEEE Trans. Image Processing, 12 (12): 1579–1590, 2003.
[20] M. Mlejnek, P. Ermes, A. Vilanova, R. van der Rijt, H. van den Bosch, E. Gröller, and F. Gerritsen, Application-oriented extensions of profile flags. In Proc. of EuroVis, pages 339–346, 2006.
[21] W. Müller, T. Nocke, and H. Schumann, Enhancing the visualization process with principal component analysis to support the exploration of trends. In Proc. of Asia-Pacific Symposium on Information Visualisation, pages 121–130, 2006.
[22] T. Nattkemper and A. Wismuller, Tumor feature visualization with unsupervised learning. Medical Image Analysis, 9 (4): 344–351, 2005.
[23] S. Oeltze, F. Grothues, A. Hennemuth, A. Kuß, and B. Preim, Integrated Visualization of Morphologic and Perfusion Data for the Analysis of Coronary Artery Disease. In Proc. of EuroVis, pages 131–138, 2006.
[24] I. Paetsch, C. Jahnke, A. Wahl, R. Gebker, M. Neuss, E. Fleck, and E. Nagel, Comparison of Dobutamine Stress Magnetic Resonance, Adenosine Stress Magnetic Resonance, and Adenosine Stress Magnetic Resonance Perfusion. Circulation, 110 (7): 835–842, 2004.
[25] J. R. Panting, P. D. Gatehouse, G. Z. Yang, M. Jerosch-Herold, N. Wilke, D. N. Firmin, and D. J. Pennell, Echo-planar magnetic resonance myocardial perfusion imaging: parametric map analysis and comparison with thallium SPECT. J Magn Reson Imaging, 13 (2): 192–200, Feb 2001.
[26] D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. J. Hawkes, Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag., 18 (8): 712–721, 1999.
[27] A. Schenk, G. Prause, and H.-O. Peitgen, Efficient semiautomatic segmentation of 3d objects in medical images. In Proc. of MICCAI, pages 186–195. Springer, 2000.
[28] T. Twellmann, O. Lichte, and T. Nattkemper, An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data. IEEE Trans. Med. Imag., 24 (10): 1256–1266, 2005.

Index Terms:
Multi-field Visualization, Visual Data Mining, Time-varying Volume Data, Integrating InfoVis/SciVis
Citation:
Steffen Oeltze, Helmut Doleisch, Helwig Hauser, Philipp Muigg, Bernhard Preim, "Interactive Visual Analysis of Perfusion Data," IEEE Transactions on Visualization and Computer Graphics, vol. 13, no. 6, pp. 1392-1399, Nov.-Dec. 2007, doi:10.1109/TVCG.2007.70569
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