Hybrid Model for Vascular Tree Structures
This paper proposes a new representation scheme of the cerebral blood vessels. This model provides information on the semantics of the vascular structure: the topological relationships between vessels and the labeling of vascular accidents such as aneurysms and stenoses. In addition, the model keeps information of the inner surface geometry as well as of the vascular map volume properties, i.e. the tissue density, the blood flow velocity and the vessel wall elasticity.
The model can be constructed automatically in a pre-process from a set of segmented MRA images. Its memory requirements are optimized on the basis of the sparseness of the vascular structure. It allows fast queries and efficient traversals and navigations. The visualizations of the vessel surface can be performed at different levels of detail; The direct rendering of the volume is fast because the model provides a natural way to skip over empty data. The paper analyzes the memory requirements of the model along with the costs of the most important Operations on it.
KeywordsMemory Requirement Symbolic Model Topological Relationship Vessel Surface Voxel Model
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- A. Savopoulos, G. Sakas, M. Grimm. Optimized maximum intensity projection (mip). 6th Eurographics Workshop on Rendering, pages 81-93, June 1995.Google Scholar
- G. Gerig, R. Kikinis, and F.A. Jolesz. Image processing of routine spin-echo mr images to enhance vascular structures: Comparison with mr angiography. 3D Imaging in Medicine: Algorithms, Systems and Applications, pages 121-132, 1990.Google Scholar
- C. Zahlten, H. Jurgens, and H.O. Peitgen. Reconstruction of branching blood vessels from ct-data. Proceedins of Rostock, Eurographics Workshop on Visualization in Scientific Computing, 1994.Google Scholar
- D. Vandermeulen, R. Verbeeck, L. Berben, D. Delaere, P. Suetens, and G. Marchal. Continuous voxel Classification by stochastic relaxation: Theory and application to mr imaging and mr angiography. Internal Research Report, Medical Imaging Research Lab, ESAT, Belgium, 1994.Google Scholar
- W. Heidrich, M. McCool, and J. Stevens. Interactive maximum projection volume rendering. Proceedings of the IEEE Visualization 95, pages 11–18 October 1995.Google Scholar
- K.J. Zuiderveld. Vr in radiology - first experiences at the university hospital Utrecht. ACM Computer Graphics, pages 47-48, November 1996.Google Scholar
- A.V. Aho, J.E. Hopcroft, and J.D. Ullman.Data Structures and Algorithms. Addison-Wesley, 1983.Google Scholar
- T. Maekawa, N.M. Patrikalakis, T. Sakkalis, and G. Yu. Analysis and applications of pipe surfaces. Computer Aided Geometric Design, 1997.Google Scholar
- J.D. Boissonnat.Surface reconstruction from planar cross section. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 393-397, 1985.Google Scholar
- P. Lacroute. Fast volume rendering using a shear-warp factorization of the viewing transformation. Technical report, Departments of Electrical Engineeering and Computer Science, Stanford University, Setember 1995.Google Scholar
- A. Puig. Contribution to Volume Modeling and to Volume Visualization. PhD thesis, Software Department, Universitat Politecnica de Catalunya, Barcelona, Spain, October 1998.Google Scholar