This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Survey of the Visual Exploration and Analysis of Perfusion Data
March/April 2009 (vol. 15 no. 2)
pp. 205-220
Bernhard Preim, University of Magdeburg, Magdeburg
Steffen Oeltze, University of Magdeburg, Magdeburg
Matej Mlejnek, AGFA Healthcare, Vienna
Eduard Gröeller, Vienna University of Technology, Vienna
Anja Hennemuth, MeVis Research, Bremen
Sarah Behrens, MeVis Medical Solutions, Bremen
Dynamic contrast-enhanced image data (perfusion data) are used to characterize regional tissue perfusion. Perfusion data consist of a sequence of images, acquired after a contrast agent bolus is applied. Perfusion data are used for diagnostic purposes in oncology, ischemic stroke assessment or myocardial ischemia. The diagnostic evaluation of perfusion data is challenging, since the data is complex and exhibits various artifacts, e.g., motion artifacts. We provide an overview on existing methods to analyze, and visualize CT and MR perfusion data. The integrated visualization of several 2D parameter maps, the 3D visualization of parameter volumes and exploration techniques are discussed. An essential aspect in the diagnosis of perfusion data is the correlation between perfusion data and derived time-intensity curves as well as with other image data, in particular with high resolution morphologic image data. We discuss visualization support with respect to the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis and the diagnosis of coronary heart disease.

[1] P. Choyke, A. Dwyer, and M. Knopp, “Functional Tumor Imaging with Dynamic Contrast-Enhanced Magnetic Resonance Imaging,” Magnetic Resonance in Medicine, vol. 17, pp. 509-520, 2003.
[2] L. Axel, “Cerebral Blood Flow Determination by Rapid-Sequence Computed Tomography: Theoretical Analysis,” Radiology, vol. 137, pp. 679-686, 1980.
[3] J. Detre, J. Leigh, D. Williams, and A. Koretsky, “Perfusion Imaging,” Magnetic Resonance in Medicine, vol. 23, no. 1, pp. 37-45, 1992.
[4] H. Michaely et al., “Renal Perfusion: Comparison of Saturation-Recovery TurboFLASH Measurements at 1.5T with Saturation-Recovery TurboFLASH and Time-Resolved Echo-Shared Angiographic Technique (TREAT) at 3.0T,” Magnetic Resonance in Medicine, vol. 24, no. 6, pp.1413-1419, 2006.
[5] K. Nikolaou et al., “Quantification of Pulmonary Blood Flow and Volume in Healthy Volunteers by Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using a Parallel Imaging Technique,” Investigative Radiology, vol. 39, no. 9, pp. 537-545, 2004.
[6] D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes, “Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images,” IEEE Trans. Medical Imaging, vol. 18, no. 8, pp. 712-721, 1999.
[7] W.M. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, “Multi-Modal Volume Registration by Maximization of Mutual Information,” Medical Image Analysis, vol. 1, no. 1, pp. 35-51, 1996.
[8] S. Walker-Samuel, M. Leach, and D. Collins, “Reference Tissue Quantification of DCE-MRI Data without a Contrast Agent Calibration,” Physics in Medicine and Biology, vol. 52, no. 3, pp.589-601, 2007.
[9] M. Lysaker, A. Lundervold, and X. Tai, “Noise Removal Using Fourth-Order Partial Differential Equation with Applications toMedical Magnetic Resonance Images in Space and Time,” IEEE Trans. Image Processing, vol. 12, no. 12, pp. 1579-1590, 2003.
[10] U. Behrens, J. Teubner, C. Evertsz, M. Walz, H. Jürgens, and H.-O. Peitgen, “Computer-Assisted Dynamic Evaluation of Contrast-Enhanced-MRI,” Proc. Computer Assisted Radiology (CAR '96), pp.362-367, 1996.
[11] 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, vol. 66, no. 2, pp. 61-67, 1998.
[12] A. Sorensen et al., “Hyperacute Stroke: Simultaneous Measurement of Relative Cerebral Blood Volume, Relative Cerebral Blood Flow, and Mean Tissue Transit Time,” Radiology, vol. 210, no. 2, pp. 519-527, 1999.
[13] C. Ware, Information Visualization. Morgan Kaufmann, 2000.
[14] 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,” Proc. Eurographics/IEEE VGTC Symp. Visualization '06, pp. 131-138, 2006.
[15] R. Tyler, “Visualization of Multiple Fields on the Same Surface,” IEEE Computer Graphics and Applications, vol. 22, no. 3, pp. 6-10, 2002.
[16] H. Levkowitz, “Color Icons: Merging Color and Texture Perception for Integrated Visualization of Multiple Parameters,” Proc. IEEE Visualization (VIS '91), pp. 164-170, 1991.
[17] W. Schroeder, K. Martin, and B. Lorensen, The Visualisation Toolkit, third ed. Kitware, 2001.
[18] E. Bier, M. Stone, K. Pier, W. Buxton, and T. DeRose, “Toolglass and Magic Lenses: The See-Through Interface,” Proc. ACM SIGGRAPH '93, pp. 73-80, 1993.
[19] S. Oeltze, A. Malyszczyk, and B. Preim, “Intuitive Mapping of Perfusion Parameters to Glyph Shape,” Proc. Bildverarbeitung für die Medizin, pp. 262-266, 2008.
[20] H. Doleisch, M. Gasser, and H. Hauser, “Interactive Feature Specification for ${\rm Focus}+{\rm Context}$ Visualization of Complex Simulation Data,” Proc. IEEE TCVG/Eurographics Symp. Visualization (EUROVIS '03), pp. 239-248, 2003.
[21] M. Mlejnek, P. Ermes, A. Vilanova, R. van der Rijt, H. van den Bosch, E. Gröller, and F. Gerritsen, “Profile Flags: A Novel Metaphor for Probing of T2 Maps,” Proc. IEEE Visualization (VIS'05), pp. 599-606, 2005.
[22] 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,” Proc. Eurographics/IEEE VGTC Symp. Visualization '06, pp. 339-346, 2006.
[23] W. Chen, M.L. Giger, U. Bick, and G.M. Newstead, “Automatic Identification and Classification of Characteristic Kinetic Curves of Breast Lesions on DCE-MRI,” Medical Physics, vol. 33, no. 8, pp.2878-2887, 2006.
[24] R.E.A. Lucht, M.V. Knopp, and G. Brix, “Classification of Signal-Time Curves from Dynamic MR Mammography by Neural Networks,” Magnetic Resonance Imaging, vol. 19, no. 1, pp. 51-57, 2001.
[25] T. Twellmann, O. Lichte, and T.W. Nattkemper, “An AdaptiveTissue Characterization Network for Model-Free Visualization of Dynamic Contrast-Enhanced Magnetic Resonance Image Data,” IEEE Trans. Medical Imaging, vol. 24, no. 10, pp.1256-1266, 2005.
[26] T. Nattkemper and A. Wismüller, “Tumor Feature Visualization with Unsupervised Learning,” Medical Image Analysis, vol. 9, no. 4, pp. 344-351, 2005.
[27] S. Oeltze, H. Doleisch, H. Hauser, P. Muigg, and B. Preim, “Interactive Visual Analysis of Perfusion Data,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1392-1399, Nov./Dec. 2007.
[28] P. Muigg, J. Kehrer, S. Oeltze, H. Piringer, H. Doleisch, B. Preim, and H. Hauser, “A Four-Level ${\rm Focus}+{\rm Context}$ Approach to Interactive Visual Analysis of Temporal Features in Large Scientific Data,” Computer Graphics Forum, vol. 27, no. 3, pp. 775-782, 2008.
[29] A. Grzesik, J. Bernarding, J. Braun, H.-C. Koennecke, K.J. Wolf, and T. Tolxdorff, “Characterization of Stroke Lesions Using aHistogram-Based Data Analysis Including Diffusion- and Perfusion-Weighted Imaging,” Proc. SPIE Medical Imaging: Physiologyand Function from Multidimensional Images, vol. 3978, pp. 23-31, 2000.
[30] M. Wintermark et al., “Comparative Overview of Brain Perfusion Imaging Techniques,” Stroke, vol. 36, no. 9, pp. 83-99, 2005.
[31] M. Wintermark, N. Fischbein, W. Smith, N. Ko, M. Quist, and W. Dillon, “Accuracy of Dynamic Perfusion CT with Deconvolution in Detecting Acute Hemispheric Stroke,” Am. J. Neuroradiology, vol. 26, no. 1, pp. 104-112, 2005.
[32] J. Eastwood, M. Lev, and J. Provenzale, “Perfusion CT with Iodinated Contrast Material,” Am. J. Roentgenology, vol. 180, no. 1, pp. 3-12, 2003.
[33] J. den Boer and P. Folkers, “MR Perfusion and Diffusion Imaging in Ischemic Brain Disease,” Medica Mundi, vol. 41, no. 2, pp. 20-35, 1997.
[34] S. Rose et al., “MRI Based Diffusion and Perfusion Predictive Model to Estimate Stroke Evolution,” Magnetic Resonance Imaging, vol. 19, no. 8, pp. 1043-1053, 2001.
[35] S. Warach, J. Gaa, B. Siewert, P. Wielopolski, and R. Edelman, “Acute Human Stroke Studied by Whole Brain Echo Planar Diffusion-Weighted Magnetic Resonance Imaging,” Ann. Neurology, vol. 37, no. 2, pp. 231-241, 1995.
[36] S. Oeltze, C. Bendicks, S. Behrens, and B. Preim, “Multiparametervisualisierung zur Exploration Dynamischer Bilddaten,” Proc. Bildverarbeitung für die Medizin, pp. 317-321, 2005.
[37] E. Furman-Haran, D. Grobgeld, and H. Degani, “Dynamic Contrast-Enhanced Imaging and Analysis at High Spatial Resolution of MCF7 Human Breast Tumors,” J. Magnetic Resonance Imaging, vol. 128, pp. 161-171, 1997.
[38] M. Knopp et al., “Pathophysiologic Basis of Contrast Enhancement in Breast Tumors,” Magnetic Resonance in Medicine, vol. 10, pp. 260-266, 1999.
[39] W.A. Kaiser and E. Zeitler, “MR Imaging of the Breast: Fast Imaging Sequences with and without Gd-DTPA-Preliminary Observations,” Radiology, vol. 170, pp. 681-686, 1989.
[40] C. Wood, “Computer Aided Detection (CAD) for Breast MRI,” Technology in Cancer Research and Treatment, vol. 4, no. 1, pp. 49-53, 2005.
[41] S. Heywang-Köbrunner, P. Viehweg, A. Heinig, and C. Kuchler, “Contrast-Enhanced MRI of the Breast: Accuracy, Value, Controversies, Solutions,” European J. Radiology, vol. 24, pp. 94-108, 1997.
[42] H. Degani, V. Gusis, D. Weinstein, S. Fields, and S. Strano, “Mapping Pathophysiological Features of Breast Tumors by MRI at High Spatial Resolution,” Nature in Medicine, vol. 2, pp. 780-782, 1997.
[43] M. Schnall et al., “Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study,” Radiology, vol. 238, no. 1, pp. 42-53, 2006.
[44] S. Meyer, M. Müller-Schimpfle, H. Jürgens, and H. Peitgen, “MT-DYNA:Computer Assistance for the Evaluation of Dynamic MR and CT Data in a Clinical Environment,” Proc. Computer Assisted Radiology and Surgery (CARS '99), pp. 331-334, 1999.
[45] J. Wiener, K. Schilling, C. Adami, and N. Obuchowski, “Assessment of Suspected Breast Cancer by MRI: A Prospective Clinical Trial Using a Combined Kinetic and Morphologic Analysis,” Am. J.Roentgenology, vol. 184, no. 3, pp. 878-886, 2005.
[46] H. Alfke et al., “Analysis of Mice Tumor Models Using Dynamic MRI Data and a Dedicated Software Platform,” Fortschritte auf dem Gebiete der RoÉntgenstrahlen, vol. 176, no. 9, pp. 1226-1231, 2004.
[47] G. Hellwig, K.-H. Englmeier, J. Griebel, R. Lucht, S. Delorme, M. Siebert, and G. Brix, “Dynamic MR Mammography: Multidimensional Visualization of Contrast Enhancement in Virtual Reality,” Proc. SPIE Medical Imaging: Physiology and Function from Multidimensional Images, vol. 4683, pp. 54-81, 2002.
[48] E. Coto, S. Grimm, S. Bruckner, E. Gröller, A. Kanitsar, and O. Rodriguez, “MammoExplorer: An Advanced CAD Application for Breast DCE-MRI,” Proc. Vision, Modeling, and Visualization (VMV '05), pp. 91-98, 2005.
[49] S. Kohle, B. Preim, J. Wiener, and H.-O. Peitgen, “Exploration of Time-Varying Data for Medical Diagnosis,” Proc. Vision, Modeling, and Visualization (VMV '02), pp. 31-38, 2002.
[50] S. Napel, G. Rubin, and R. Jeffrey, “STS-MIP: A New Reconstruction Technique for CT of the Chest,” J. Computer Assisted Tomography, vol. 17, no. 5, pp. 832-838, 1993.
[51] P. Hunold, T. Schlosser, and J. Barkhausen, “Magnetic Resonance Cardiac Perfusion Imaging—A Clinical Perspective,” European Radiology, vol. 16, no. 8, pp. 1779-1788, 2006.
[52] H. Iida, I. Kanno, and A. Takahashi, “Measurement of Absolute Myocardial Blood Flow with H215O and Dynamic Positron Emission Tomography: Strategy for Quantification in Relation to the Partial Volume Effect,” Circulation, vol. 78, pp. 104-115, 1988.
[53] A. Kitsiou, S. Bacharach, M. Bartlett, G. Srinivasan, R.M. Summers, A.A. Quyyumi, and V. Dilsizian, “13N-Ammonia Myocardial Blood Flow and Uptake: Relation to Functional Outcome of Asynergic Regions after Revascularization,” J. Am. College of Cardiology, vol. 33, no. 3, pp. 678-686, 1999.
[54] S.C. Chua, R.H. Ganatra, D.J. Green, and A.M. Groves, “Nuclear Cardiology: Myocardial Perfusion Imaging with SPECT and PET,” Imaging, vol. 18, pp. 166-177, 2006.
[55] M. Merhige, W. Breen, V. Shelton, T. Houston, B. D'Arcy, and A. Perna, “Impact of Myocardial Perfusion Imaging with PET and (82)Rb on Downstream Invasive Procedure Utilization, Costs, and Outcomes in Coronary Disease Management,” European Radiology, vol. 48, no. 7, pp. 1069-1076, 2007.
[56] R. Go, T. Marwick, W. MacIntyre, G.B. Saha, D.R. Neumann, D.A. Underwood, and C.C. Simpfendorfer, “A Prospective Comparison of Rubidium-82 PET and Thallium-201 SPECT in Myocardial Perfusion Imaging Utilizing a Single Dipyridamole Stress in the Diagnosis of Coronary Artery Disease,” J. Nuclear Medicine, vol. 31, no. 12, pp. 1899-1905, 1990.
[57] J. Schwitter et al., “Assessment of Myocardial Perfusion in Coronary Artery Disease by Magnetic Resonance: A Comparison with Positron Emission Tomography and Coronary Angiography,” Circulation, vol. 103, no. 18, pp. 2230-2235, 2001.
[58] J. Panting, P. Gatehouse, G. Yang, M. Jerosch-Herold, N. Wilke, D. Firmin, and D. Pennell, “Echo-Planar Magnetic Resonance Myocardial Perfusion Imaging: Parametric Map Analysis and Comparison with Thallium SPECT,” J. Magnetic Resonance Imaging, vol. 13, no. 2, pp. 192-200, 2001.
[59] M.D. Cerqueira et al., “Standardized Myocardial Segmentation and Nomenclature for Tomographic Imaging of the Heart. A Statement for Healthcare Professionals from the Cardiac Imaging Committee of the Council on Clinical Cardiology of the American Heart Association,” Circulation, vol. 105, no. 4, pp. 539-542, 2002.
[60] N. Al-Saadi et al., “Noninvasive Detection of Myocardial Ischemia from Perfusion Reserve Based on Cardiovascular Magnetic Resonance,” Circulation, vol. 101, no. 12, pp. 1379-1383, 2000.
[61] R. Edelman, “Contrast-Enhanced MR Imaging of the Heart: Overview of the Literature,” Radiology, vol. 232, no. 3, pp.653-668, 2004.
[62] A. Hennemuth, S. Behrens, C. Kuehnel, S. Oeltze, O. Konrad, and H.-O. Peitgen, “Novel Methods for Parameter Based Analysis of Myocardial Tissue in MR-Images,” Proc. SPIE Medical Imaging: Physiology, Function, and Structure from Medical Images, vol. 6511, no. 1N, pp. 1-9, 2007.
[63] R. Pohle, M. Wegner, K. Rink, K. Tönnies, A. Celler, and S. Blinder, “Segmentation of the Left Ventricle in 4D-dSPECT Data Using Free Form Deformation of Super Quadrics,” Proc. SPIE Medical Imaging: Image Processing, vol. 5370, pp. 1388-1394, 2004.
[64] K. Friston, A. Holmes, J.-B. Poline, P. Grasby, S. Williams, R. Frackowiak, and R. Turner, “Analysis of fMRI Time Series Revisited,” NeuroImage, vol. 2, pp. 45-53, 1995.
[65] K. Hoehne, U. Obermoeller, and M. Boehm, “X-Ray Functional Imaging—Evaluation of the Properties of Different Parameters,” Proc. Conf. Digital Radiography, pp. 224-228, 1981.

Index Terms:
Life and Medical Sciences, Visualization, Applications, Multivariate visualization, Visualization techniques and methodologies, Volume visualization
Citation:
Bernhard Preim, Steffen Oeltze, Matej Mlejnek, Eduard Gröeller, Anja Hennemuth, Sarah Behrens, "Survey of the Visual Exploration and Analysis of Perfusion Data," IEEE Transactions on Visualization and Computer Graphics, vol. 15, no. 2, pp. 205-220, March-April 2009, doi:10.1109/TVCG.2008.95
Usage of this product signifies your acceptance of the Terms of Use.