Abstract
This paper presents on-going work on a method for determining which subvolumes of a patient-specific tissue map, extracted from CT data of the head, are relevant to simulating endoscopic sinus surgery of that individual, and for decomposing these relevant tissues into triangles and tetrahedra whose mesh size is well controlled. The overall goal is to limit the complexity of the real-time biomechanical interaction while ensuring the clinical relevance of the simulation. Relevant tissues are determined as the union of the pathology present in the patient, of critical tissues deemed to be near the intended surgical path or pathology, and of bone and soft tissue near the intended path, pathology or critical tissues. The processing of tissues, prior to meshing, is based on the Fast Marching method applied under various guises, in a conditional manner that is related to tissue classes. The meshing is based on an adaptation of a meshing method of ours, which combines the Marching Tetrahedra method and the discrete Simplex mesh surface model to produce a topologically faithful surface mesh with well controlled edge and face size as a first stage, and Almost-regular Tetrahedralization of the same prescribed mesh size as a last stage.
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References
American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS)http://www. entnet.org, 2008.
M. A. Audette, H. Delingette, A. Fuchs, O. Burgert, and K. Chinzei, A topologically faithful, tissue-guided, spatially varying meshing strategy for computing patient-specific head models for endoscopic pituitary surgery simulationJournal of Computer Aided Surgery, 12:1, 43–52, Jan. 2007.
S.L. Chan and E.O. Purisima, A new tetrahedral tesselation scheme for isosurface generationComputers & Graphics, 22:1, 83–90, Feb. 1998.
P. Cignoni, C. Montani, and R. Scopigno, A comparison of mesh simplification algorithmsComputers and Graphics, 22:1, 37–54, 1998.
H. Delingette, General object reconstruction based on simplex meshesInternal Journal of Computer Vision, 32:2, 111–146, 1999.
F. Li, G. Strauss, C. Trantakis, and M.A. Audette, An iterative classification method of 2D CT head data based on statistical and spatial informationComputer Aided Surgery Around the Head, CAS-H, Feb. 2008.
A. Fuchs, Almost regular triangulations of trimmed NURBS-SolidsEngineering with Computers, 17, 55–65, 2001.
P.J. Frey, H. Borouchaki, and P.-L. George, Delaunay tetrahedralization using an advancing-front approachProc. 5th Int. Meshing Roundtable, Pittsburgh, 1996.
C. Grühser, N. Ritter, G. Strauss, H. Maass, and M. Audette, Development of a tool-centered collision model for volumetric resection in ENT surgery simulationEuroHaptics, 2008.
W. Lorensen and H. Cline, Marching cubes: a high resolution 3D surface construction algorithmComputer Graphics, 21:(4), pp. 163–170, July 1987.
Montreal Neurological Institute, Brain Imaging Software Toolboxhttp://www.bic.mni. mcgill.ca/software/, 2008.
P. Ning and J. Bloomenthal, An evaluation of implicit surface tilersIEEE Computer Graphics and Applications, 13:6, 33–41, 1993.
Elisabeth Rouy and Agnés Tourin, A viscosity solutions approach to shape-from-shadingSIAM Journal on Numerical Analysis, 29:3, 867–884, 1992.
W.J. Schroeder, J.A. Zarge, and W.E. Lorensen, Decimation of triangle meshesComputer Graphics, 26:2, 65–70, 1992.
J.A. SethianLevel Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge University Press, Cambridge, 2nd ed., 1999.
J. Shewchuk, What is a good linear element? Interpolation, conditioning, and quality measuresEleventh International Meshing Roundtable, 115–126, Sept. 2002.
SPRING Surgical Simulation Platform, Stanford Universityhttp://spring.stanford.edu/.
P. Svendsen, L. Quiding, and I. Landahl, Blackout and other artefacts in computed tomography caused by fillings in teethNeuroradiology, 19:5, 229–234, July 1980.
C. Trantakis, J. Meixensberger, G. Strauss, E. Nowatius, D. Lindner, H. K. Çakmak, H. Maass, C. Nagel, and U.G. Kühnapfel, IOMaster 7D - a new device for virtual neuroendoscopyComputer Assisted Radiology and Surgery, pp. 707–712, 2004.
G.M. Treece, R.W. Prager, and A.H. Gee, Regularised marching tetrahedra: improved iso-surface extractionComputers & Graphics, 23:4, 583–598, Aug. 1999.
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Audette, M.A., Hertel, I., Burgert, O., Strauss, G. (2009). A Tissue Relevance and Meshing Method for Computing Patient-Specific Anatomical Models in Endoscopic Sinus Surgery Simulation. In: Tavares, J.M.R.S., Jorge, R.M.N. (eds) Advances in Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9086-8_9
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