Tetrahedral mesh generation for non-rigid registration of brain MRI: Analysis of the requirements and evaluation of solutions

Proceedings of the 17th International Meshing Roundtable, IMR 2008

Volumn , Issue , 2008, Pages 55-72

  Fedorov, Andriy ^a   Chrisochoides, Nikos ^a  

^a William and Mary   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING; NEUROSURGERY; TUMORS;

APPLICATION CONTEXTS; APPLICATION SPECIFIC; APPLICATION SPECIFIC REQUIREMENTS; BIO-MECHANICAL MODELS; IMAGE GUIDED NEURO SURGERIES; NONRIGID REGISTRATION; QUANTITATIVE MEASURES; TETRAHEDRAL MESH GENERATION;

MESH GENERATION;

EID: 84856515838     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-87921-3_4     Document Type: Conference Paper

References (38)

1. Clatz, O., Delingette, H., Talos, I.-F., Golby, A.J., Kikinis, R., Jolesz, F.A., Ayache, N., Warfield, S.K.: Robust non-rigid registration to capture brain shift from intraoperative MRI. IEEE Trans. Med. Imag. 24, 1417-1427 (2005)
2. Archip, N., Clatz, O., Whalen, S., Kacher, D., Fedorov, A., Kot, A., Chrisochoides, N., Jolesz, F., Golby, A., Black, P.M., Warfield, S.K.: Non-rigid alignment of preoperative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. NeuroImage 35, 609-624 (2007)
3. Fedorov, A., Chrisochoides, N., Kikinis, R., Warfield, S.K.: An evaluation of three approaches to tetrahedral mesh generation for deformable registration of MR images. In: Proc. of IEEE ISBI 2006, pp. 658-661 (2006)
4. Hill, D.L.G., Batchelor, P.G., Holden, M., Hawkes, D.J.: Medical image registration. Physics in Medicine and Biology 46, R1-R45 (2001)
5. Delingette, H., Ayache, N.: Soft tissue modeling for surgery simulation. In: Computational models for the human body, 1st edn. Handbook of Numerical Analysis, vol. XII, pp. 453-550. Elsevier, Amsterdam (2004)
6. Shewchuk, J.R.: What is a good linear element? Interpolation, conditioning, and quality measures. In: Proc. of 11th IMR, pp. 115-126 (2002)
7. Owen, S.J.: A survey of unstructured mesh generation technology. In: Proc. of 7th IMR, pp. 239-267 (1998)
8. Yoo, T.S.: Insight into images. A K Peters (2004)
9. Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. In: Proc. of ACM SIGGRAPH, pp. 163-169 (1987)
10. Boyd, S.K., Müller, R.: Smooth surface meshing for automated finite element model generation from 3d image data. J. of Biomechanics 39, 1287-1295 (2006)
11. Dey, T.K., Li, G., Ray, T.: Polygonal surface remeshing with Delaunay refinement. In: Proc. of 14th IMR, pp. 343-361 (2005)
12. Cebral, J., Castro, M.A., Löhner, R., Burgess, J.E., Pergolizzi, R., Putman, C.M.: Recent developments in patient-specific image-based modeling of hemodynamics. Mecanica Computacional XXIII, 1471-1482 (2004)
13. Amenta, N., Bern, M.: Surface reconstruction by Voronoi filtering. In: Proc. of 14th SCG, pp. 39-48. ACM Press, New York (1998)
14. Kolluri, R., Shewchuk, J.R., O'Brien, J.F.: Spectral surface reconstruction from noisy point clouds. In: Proc. of SGP 2004, pp. 11-21 (2004)
15. Dey, T.K., Levine, J.A.: Delaunay meshing of isosurfaces. In: Proc. of IEEE Int. Conf. on Shape Modeling and Applications (SMI 2007), pp. 241-250 (2007)
16. Boissonnat, J.-D., Oudot, S.: Provably good sampling and meshing of surfaces. Graphical Models 67, 405-451 (2005)
17. Frey, P., Sarter, B., Gautherie, M.: Fully automatic mesh generation for 3-d domains based upon voxel sets. Int. J. for Num. Meth. in Engng. 37, 2735-2753 (1994)
18. Zhang, Y., Bajaj, C., Sohn, B.-S.: 3d finite element meshing from imaging data. Comp. Meth. in App. Mech. and Engng. 194, 5083-5106 (2005)
19. Cline, H.E., Lorensen, W.E., Kikinis, R., Jolesz, F.: Three-dimensional segmentation of MR images of the head using probability and connectivity. J. of Comp. Assisted Tomography 14, 1037-1045 (1990)
20. Cebral, J.R., Löhner, R.: From medical images to CFD meshes. In: Proc. of 8th IMR, pp. 321-331 (1999)
21. Hartmann, U., Kruggel, F.: A fast algorithm for generating large tetrahedral 3d finite element meshes from magnetic resonance tomograms. In: IEEE Workshop on Biomedical Image Analysis, p. 184 (1998)
22. Udeshi, T.: Tetrahedral mesh generation from segmented voxel data. In: Proc. of 12th IMR, pp. 425-436 (2003)
23. Yerry, M.A., Shephard, M.S.: Automatic three-dimensional mesh generation by the modified-octree technique. Int. J. Num. Meth. Engng 20, 1965-1990 (1984)
24. Mitchell, S.A., Vavasis, S.A.: Quality mesh generation in higher dimensions. SIAM J. Comp. 29, 1334-1370 (2000)
25. Ferrant, M.: Physics-based Deformable Modeling of Volumes and Surfaces for Medical Image Registration, Segmentation and Visualization. PhD thesis, Universite' Catholique de Louvain (2001)
26. Mohamed, A., Davatzikos, C.: Finite element mesh generation and remeshing from segmented medical images. In: Proc. of IEEE ISBI, pp. 420-423 (2004)
27. Fedorov, A., Chrisochoides, N., Kikinis, R.,Warfield, S.K.: Tetrahedral mesh generation for medical imaging. ISC Insight Journal, 2005 MICCAI Open Source Workshop (2005), http://hdl.handle.net/1926/35
28. Freitag, L.A., Knupp, P.M., Munson, T.S., Shontz, S.M.: A comparison of optimization software for mesh shape-quality improvement problems. In: Proc. of 11th IMR, pp. 29-40 (2002)
29. Castellano-Smith, A.D., Hartkens, T., Schnabel, J., Hose, D.R., Liu, H., Hall, W.A., Truwit, C.L., Hawkes, D.J., Hill, D.L.G.: Constructing patient specific models for correcting intraoperative brain deformation. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 1091-1098. Springer, Heidelberg(2001)
30. Molino, N.P., Bridson, R., Teran, J., Fedkiw, R.: A crystalline, red green strategy for meshing highly deformable objects with tetrahedra. In: Proc. of 12th IMR, pp. 103-114 (2003)
31. Persson, P.O., Strang, G.: A simple mesh generator in MATLAB. SIAM Review 46, 329-345 (2004)
32. Si, H.: On refinement of constrained Delaunay tetrahedralizations. In: Proc. of 15th IMR, pp. 509-528 (2006)
33. Schöberl, J.: NETGEN: An advancing front 2d/3d-mesh generator based on abstract rules. Computing and Visualization in Science 1, 41-52 (1997)
34. Brewer, M., Diachin, L.F., Knupp, P.M., Leurent, T., Melander, D.J.: TheMesquite mesh quality improvement toolkit. In: Proc. of 12th IMR, pp. 239-250 (2003)
35. CGAL: Cgal, Computational Geometry Algorithms Library (2008), http://www.cgal.org
36. Ibanez, L., Schroeder, W.J.: The ITK Software Guide. Kitware Inc. (2003)
37. Billet, E., Fedorov, A., Chrisochoides, N.: The use of robust local Hausdorff distances in accuracy assessment for image alignment of brain MRI. ISC Insight Journal (January-June 2008), http://hdl.handle.net/1926/1354
38. Baker, T.J.: Mesh movement and metamorphosis. Engng with Comp. 18, 188-198 (2002)