Clinical applications of biomechanical simulation

• Autorzy: Kruggel F. , Tittgemeyer M. , Wollny G.
• Języki publikacji: EN

Abstrakty

EN

The planning of therapeutical interventions and the understanding of pathological processes may be improved by providing tools for biomechanical simulation. This article focuses on computational services providing numerical simulations for analysis, prediction and virtual prototyping to the medical sector ("bio-numerics"). Two example problems are discussed: (1) The simulation of distraction osteogenesis using Finite Element Models (FEM): Simulations use highly resolved meshes in order to represent the complex midface structures with sufficient detail. Meshes with 1 mm resolutions typically have 1+ million nodes, so forward models must be computed on high performance computing (HPC) platforms. Pre-operative planning involves "playing" with different what-if scenarios so fast response times are highly desirable in order to provide tools that are accepted in a clinical environment. (2) Intra-operative planning in neurosurgery using inverse biomechanical models: Surgically induced deformations invalidate pre-operatively acquired information about functionally relevant areas. This problem is addressed by non-linear registration of pre-operative functional Magnetic Resonance Imaging (MRI) data to intra-operative MRI data.

Słowa kluczowe

EN

biomechanical simulation; finite element models; MRI and CT Data

PL

symulacja biomechaniczna; modele elementów skończonych

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2003
• Tom: Vol. 6
• Strony: IP3--11
• Opis fizyczny: Bibliogr. 8 poz., rys.

Twórcy

autor

Kruggel F.

• Max-Planck-Institute of Cognitive Neuroscience, Stephanstrasse 1, D-04103 Leipzig, Germany

autor

Tittgemeyer M.

• Max-Planck-Institute of Cognitive Neuroscience, Stephanstrasse 1, D-04103 Leipzig, Germany

autor

Wollny G.

• Max-Planck-Institute of Cognitive Neuroscience, Stephanstrasse 1, D-04103 Leipzig, Germany

Bibliografia

• [1] AIMEDIEU P., GREBE R., The VivMat-Site. http://www.u-picardie.fr/labo/UGBM/, 2003.
• [2] BASERMANN A., FINGBERG J., LONSDALE G., MAERTEN B., Dynamic multi-partitioning for parallel finite element applications. Proc. Int. Conf. ParCo'99, pp. 259-266, Imperial College Press, London, 2000.
• [3] CLARKSON K.L., MEHLHORN K., SEIDEL R., Four results on randomized incremental constructions. Computational Geometry: Theory and Applications 3, pp. 185-212, 1993.
• [4] CLAY R.L., MISH K.D., OTERO I.J., TAYLOR L.M., WILLIAMS A.B., An annotated reference guide to the finite element interface specification. Sandia Report SAND99-8229, http://z.ca.sandia.gov/fei/, 1999.
• [5] POPE A.R., LOWE D.G., Vista: A software environment for computer vision research. Computer Vision and Pattern Recognition (CVPR'94), pp. 768-772. IEEE Press, Los Alamitos, 1994.
• [6] The SimBio Project, http://www.simbio.de, 2003.
• [7] TITTGEMEYER M., WOLLNY G., KRUGGEL F., Visualising deformation fields computed by non-linear image registration. Computing and Visualization in Science, Vol. 5, pp. 45-51, 2002.
• [8] WOLLNY G., KRUGGEL F.; Computational cost of non-rigid registration algorithms based on fluid dynamics. IEEE Transactions on Medical Imaging, Vol. 21, pp. 946-952, 2002.