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Vessel Visualization with Volume Rendering

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Visualization in Medicine and Life Sciences II

Part of the book series: Mathematics and Visualization ((MATHVISUAL))

Abstract

Volume rendering allows the direct visualization of scanned volume data, and can reveal vessel abnormalitiesmore faithfully. In this overview, we will present a pipeline model for direct volume rendering systems, which focus on vascular structures. We will cover the fields of data pre-processing, classification of the volume via transfer functions, and finally rendering the volume in 2D and 3D. For each stage in the pipeline, different techniques are discussed to support the diagnosis of vascular diseases. Next to various general methods we will present two case studies, in which the systems are optimized for two different medical issues. At the end, we discuss current trends in volume rendering and their implications for vessel visualization.

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References

  1. Preim B, Oeltze S. Visualization in Medicine and Life Sciences. In: Linsen L, Hagen H, editors. 3D Visualization of Vasculature: An Overview. Springer Verlag; 2007. 19-39.

    Google Scholar 

  2. Selle D, Preim B, Schenk A, Heinz-Otto-Peitgen. Analysis of Vasculature for Liver Surgery Planning. IEEE Transactions on Medical Imaging. 2002 November;21(11):1344-1357.

    Google Scholar 

  3. Zerfowski D. Motion artifact compensation in CT. In: SPIE Medical Imaging 1998: Image Processing; 1998. p. 416-424.

    Google Scholar 

  4. Zheng L, Maksimov D, Stutzmann T. Bone removal in dual energy CT. In: CVII 2008: Computer Vision for Intravascular and Intracardiac Imaging; 2008. 120-127.

    Google Scholar 

  5. Blum H. Biological shape and visual science: Part I. J Theor Biology. 1973;38:205-283.

    Article  Google Scholar 

  6. Sethian JA. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. 2nd ed. Cambridge University Press; 1999.

    Google Scholar 

  7. Schaap M, Metz CT, van Walsum T, van der Giessen AG, Weustink AC, Mollet NRA, et al. Standardized Evaluation Methodology and Reference Database for Evaluating Coronary Artery Centerline Extraction Algorithms. Medical Image Analysis. 2009;13/5:701-714.

    Google Scholar 

  8. Boskamp T, Hahn H, Hindennach M, Zidowitz S, Oeltze S, Preim B, et al. Geometrical and Structural Analysis of Vessel Systems in 3D Medical Image Datasets. In: Leondes CT, editor. Medical Imaging Systems: Technology und Applications. vol. V. World Scientific Press; 2005. 1-60.

    Google Scholar 

  9. Kanitsar A, Wegenkittl R, Felkel P, Fleischmann D, Sandner D, Gröler E. Computed Tomography Angiography:A Case Study of Peripheral Vessel Investigation. In: IEEE Visualization; 2001.477-480.

    Google Scholar 

  10. Frangi AF, Frangi RF, Niessen WJ, Vincken KL, Viergever MA. Multiscale Vessel Enhancement Filtering. Springer-Verlag; 1998. 130-137.

    Google Scholar 

  11. Joshi A, Qian X, Dione D, Bulsara K, Breuer C, Sinusas A, et al. Effective visualization of complex vascular structures using a non-parametric vessel detection method. IEEE Transactions on Visualization and Computer Graphics. 2008;14(6):1603-1610.

    Article  Google Scholar 

  12. Kindlmann G, Durkin JW. Semi-automatic generation of transfer functions for direct volume rendering. In: IEEE Symposium on Volume Visualization; 1998. 79-86.

    Google Scholar 

  13. Kniss J, Kindlmann G, Hansen C. Multidimensional Transfer Functions for Interactive Volume Rendering. IEEE Transactions on Visualization and Computer Graphics. 2002;8(3):270-285.

    Article  Google Scholar 

  14. Vega Higuera F, Sauber N, Tomandl B, Nimsky C, Greiner G, Hastreiter P. Enhanced 3D-Visualization of Intracranial Aneurysms Involving the Skull Base. In: MICCAI (2); 2003. 256-263.

    Google Scholar 

  15. Rezk-Salama C, Hastreiter P, Scherer J, Greiner G. Automatic Adjustment of Transfer Functions for 3D Volume Visualization. In: VMV; 2000. 357-364.

    Google Scholar 

  16. Vega Higuera F, Sauber N, Tomandl B, Nimsky C, Greiner G, Hastreiter P. Automatic adjustment of bidimensional transfer functions for direct volume visualization of intracranial aneurysms. In: Proc. of Society of Photo-Optical Instrumentation Engineers (SPIE). vol. 5367; 2004.275-284.

    Google Scholar 

  17. Lundström C, Ljung P, Ynnerman A. Local Histograms for Design of Transfer Functions in Direct Volume Rendering. IEEE Transactions on Visualization and Computer Graphics. 2006;12(6):1570-1579.

    Google Scholar 

  18. Lundström C, Ljung P, Persson A, Ynnerman A. Uncertainty Visualization in Medical Volume Rendering Using Probabilistic Animation. IEEE Transactions on Visualization and Computer Graphics. 2007;13(6):1648-1655.

    Google Scholar 

  19. Tappenbeck A, Preim B, Dicken V. Distance-Based Transfer Function Design: Specification Methods and Applications. In: Simulation und Visualisierung; 2006. 259-274.

    Google Scholar 

  20. Correa CD, Ma KL. Size-based Transfer Functions: A New Volume Exploration Technique. IEEE Transactions on Visualization and Computer Graphics. 2008;14(6):1380-1387.

    Google Scholar 

  21. Kanitsar A, Fleischmann D, Wegenkittl R, Gröller ME. Diagnostic Relevant Visualization of Vascular Structures. Favoritenstrasse 9-11/186, A-1040 Vienna, Austria; 2004. Human contact: technical-report@cg.tuwien.ac.at.

    Google Scholar 

  22. Zuiderveld KJ, Koning AHJ, Viergever MA. Techniques for speeding up high-quality perspective maximum intensity projection. Pattern Recogn Lett. 1994;15(5):507-517.

    Article  MATH  Google Scholar 

  23. Sato Y NSTS Shiraga N, R K. LMIP: Local Maximum Intensity Projection: Comparison of Visualization Methods Using Abdominal CT Angiograpy. Journal of Computer Assisted Tomography. 1998;22(6):912-917.

    Google Scholar 

  24. Scharsach H, Hadwiger M, Neubauer A, Wolfsberger S, Bühler K. Perspective Isosurface and Direct Volume Rendering for Virtual Endoscopy Applications. In: EG/IEEE Eurovis; 2006. 315-322.

    Google Scholar 

  25. Vega Higuera F, Hastreiter P, Fahlbusch R, Greiner G. High Performance Volume Splatting for Visualization of Neurovascular Data. In: IEEE Visualization; 2005. 35-42.

    Google Scholar 

  26. Rezk-Salama C, Kolb A. Opacity Peeling for Direct Volume Rendering. Computer Graphics Forum (Proc Eurographics). 2006;25(3):597-606.

    Article  Google Scholar 

  27. Bruckner S, Gröller ME. Instant Volume Visualization using Maximum Intensity Difference Accumulation. Comput Graph Forum. 2009;28(3):775-782.

    Google Scholar 

  28. Wesarg S, Khan MF, Firle E. Localizing Calcifications in Cardiac CT Data Sets Using a New Vessel Segmentation Approach. J Digital Imaging. 2006;19(3):249-257.

    Article  Google Scholar 

  29. Kanitsar A, Fleischmann D, Wegenkittl R, Felkel P, Gröller ME. CPR: curved planar reformation. In: IEEE Visualization; 2002. 37-44.

    Google Scholar 

  30. Kanitsar A, Wegenkittl R, Fleischmann D, Groller ME. Advanced Curved Planar Reformation: Flattening of Vascular Structures. In: IEEE Visualization; 2003. 43-50.

    Google Scholar 

  31. Straka M, Cervenansky M, La Cruz A, Kochl A, Sramek M, Groller E, et al. The VesselGlyph: Focus & Context Visualization in CT-Angiography. In: IEEE Visualization; 2004. 385-392.

    Google Scholar 

  32. Bartrolí AV, Wegenkittl R, König A, Gröller E. Nonlinear virtual colon unfolding. In: IEEE Visualization; 2001. 411-420.

    Google Scholar 

  33. Ropinski T, Hermann S, Reich R, Schäfers M, Hinrichs KH. Multimodal Vessel Visualization of Mouse Aorta PET/CT Scans. IEEE Transactions on Visualization and Computer Graphics (TVCG) (Vis Conference Issue). 2009; 1515-1522.

    Google Scholar 

  34. Anxionnat R, Bracard S, Ducrocq X, Trousset Y, Launay L, Kerrien E, et al. Intracranial Aneurysms: Clinical Value of 3D Digital Subtraction Angiography in the Therapeutic Decision and Endovascular Treatment. Radiology. 2001; 799-808. Article dans revue scientifique avec comité de lecture.

    Google Scholar 

  35. Zhan C, Villa-Uriol MC, Craene MD, Pozo JM, Frangi AF. Morphodynamic Analysis of Cerebral Aneurysm Pulsation from Time-Resolved Rotational Angiography. MedImg. 2009 July;28(7):1105 - 1116.

    Google Scholar 

  36. Neugebauer M, Gasteiger R, Diehl V, Beuing O, Preim B. Automatic generation of context visualizations for cerebral aneurysms from MRA datasets. International Journal of Computer Assisted Radiology and Surgery (CARS). 2009 Juni;4 (Supplement 1):112-113.

    Google Scholar 

  37. Glaßer S, Oeltze S, Hennemuth A, Kubisch C, Mahnken A, Wilhelmsen S, et al. Automatic Transfer Function Specification for Visual Emphasis of Coronary Artery Plaque. Computer Graphics Forum. 2010;29(1):191-201.

    Article  Google Scholar 

  38. Agatston AS, Janowitz WR, Hildner FJ, et al. Quantification of coronary artery calcium using ultrafast computed tomography. J Am Coll Cardiol. 1990;15(4):827-832.

    Article  Google Scholar 

  39. Pohle K, Achenbach S, MacNeill B, et al. Characterization of non-calcified coronary atherosclerotic plaque by multi-detector row CT: Comparison to IVUS. Atherosclerosis.2007;190:174-180.

    Article  Google Scholar 

  40. Boskamp T, Rinck D, Link F, Kmmerlen B, Stamm G, Mildenberger P. New Vessel Analysis Tool for Morphometric Quantification and Visualization of Vessels in CT and MR Imaging Data Sets. Radiographics. 2004;24(1):287-297.

    Article  Google Scholar 

  41. Ropinski T, Döring C, Rezk-Salama C. Advanced Volume Illumination with Unconstrained Light Source Positioning. IEEE Computer Graphics and Applications. 2010;Accepted for publication.

    Google Scholar 

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Correspondence to Christoph Kubisch .

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Kubisch, C., Glaßer, S., Neugebauer, M., Preim, B. (2012). Vessel Visualization with Volume Rendering. In: Linsen, L., Hagen, H., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences II. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21608-4_7

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