Biomedical Engineering Reference
In-Depth Information
the displacement at the corresponding position in the statistical meshless model.
The maximum difference is approximately 0.2 mm. As the accuracy of neurosur-
gery is not better than 1 mm and the voxel size in high-quality pre-operative MR
images is usually of a similar magnitude, we can conclude that the difference is, for
practical purposes, negligible. So the proposed statistical meshless model has
acceptable accuracy as compared to the finite element model.
4 Conclusions and Discussion
We have developed a statistical meshless model based on fuzzy tissue classification
and meshless solution method. Compared to finite element models (which have
been widely used in biomechanical computations) the statistical meshless model is
much easier to generate. The proposed patient-specific model generation pipeline is
vastly different from traditional CAD based finite element modeling and offers a
prospect of the neuroimage being used as a biomechanical model. The verification
shows that acceptable accuracy of the computed deformation field can be obtained,
even if nodes and integration points do not conform to tissue boundaries. Although
the verification was conducted in 2D, the generalization to 3D is straightforward.
As our meshless method is inherently data parallel (all degrees of freedom
are treated in exactly the same way), GPU implementation will allow very high
efficiency and a possibility for near-real time intraoperative computations.
Acknowledgments The first author is an SIRF scholar in the University of Western Australia
during the completion of this research. The financial support of National Health and Medical
Research Council (NHMRC Grant No.1006031) and Australian Research Council (ARC Grant
No.DP1092893) is gratefully acknowledged.
References
1. Ferrant, M., Nabavi, A., Macq, B., Black, P.M., Jolesz, F.A., Kikinis, R., Warfield, S.K.: Serial
registration of interoperative MR images of the brain. Med. Image Anal. 6, 337-359 (2002)
2. Bucholz, R., MacNeil, W., McDurmont, L.: The operating room of the future. Clin. Neurosurg.
51, 228-237 (2004)
3. Warfield, S.K., Haker, S.J., Talos, I.F., Kemper, C.A., Weisenfeld, N., Mewes, A.U.J.,
Goldberg-Zimring, D., Zou, K.H., Westin, C.F., Wells, W.M., Tempany, C.M.C., Golby, A.,
Black, P.M., Jolesz, F.A., Kikinis, R.: Capturing intraoperative deformations: Research expe-
rience at Brigham and Women's hospital. Med. Image Anal. 9, 145-162 (2005)
4. Nakaji, P., Spetzler, R.F.: Innovations in surgical approach: the marriage of technique,
technology, and judgment. Clin. Neurosurg. 51, 177-185 (2004)
5. Grosland, N.M., Shivanna, K.H., Magnotta, V.A., Kallemeyn, N.A., DeVries, N.A., Tadepalli,
S.C., Lisle, C.: IA-FEMesh: an open-source, interactive, multiblock approach to anatomic
finite element model development. Comput. Meth. Programs Biomed. 94, 96-107 (2009)
6. Belytschko, T.: Meshless methods: an overview and recent developments. Comput. Meth.
Appl. Mech. Eng. 139, 3 (1996)
