Interfacing quantitative computed tomography with finite element modeling

R. Müller

doi:10.1117/12.2294382

1 August 1990 Interfacing quantitative computed tomography with finite element modeling

R. Müller

Proceedings Volume 1395, Close-Range Photogrammetry Meets Machine Vision; 13953V (1990) https://doi.org/10.1117/12.2294382
Event: Close-Range Photogrammetry Meets Machine Vision, 1990, Zurich, Switzerland

Abstract

The use of finite element modeling for the study of implant-bone systems is a valuable method for improving the design of new implants. However the cumberson modeling of the anatomic geometries has been limiting the accuracy of the models, regarding the geometry as well as the material properties. It was the purpose of the present project to generate almost automatically a FEM model from stacked CT-scans of long bones and to assign to every element the material properties according to the local density. An interface program was written which detects the inner and outer contours of the cortical bone on every scan, joins them together to build up subvolumes and transfers them in vector format into the FEM package I-DEAS. The sub- volumes are smaller units of a model, which allow to control the meshing. After the automatic mesh generation within I- DEAS, the elements are read back to the interface program, where each one is assigned the material properties corresponding to the local density of the element location in the cortical or spongy bone. Besides of the generation of FE models it is possible to generate solid models of the scanned bones which may be used for display purpose or the simulation of implantations. It is now possible, to create detailed models of long bones within a short time and to use not only standard or average bone properties but individual values that may vary strongly, for example in the case of osteoporosis.

Citation Download Citation

R. Müller "Interfacing quantitative computed tomography with finite element modeling", Proc. SPIE 1395, Close-Range Photogrammetry Meets Machine Vision, 13953V (1 August 1990); https://doi.org/10.1117/12.2294382

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