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A quantitative assessment of approaches to mesh generation for surgical simulation

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Abstract

In surgical simulation, it is common practice to use tetrahedral meshes as models for anatomy. These meshes are versatile, and can be used with a number of different physically based modelling schemes. A variety of mesh generators are available that can automatically create tetrahedral meshes from segmented anatomical volumes. Each mesh generation scheme offers its own set of unique attributes. However, few are readily available. When choosing a mesh generator for simulation, it is critical for it to output good-quality, patient-specific meshes that provide a good approximation of the shape or volume to be modelled. To keep computation time within the bounds required for real-time interaction, there is also a limit imposed on the number of elements in the mesh generated. To the authors knowledge, there has been little work directly assessing the suitability of mesh generators for surgical simulation. This paper seeks to address this issue by assessing the use of six mesh generators in a surgical simulation scenario, and examining how they affect simulation precision. This paper aims to perform these comparisons against high-resolution reference meshes, where we examine the precision of meshes from the same mesh generator at different levels of complexity.

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Notes

  1. This is usually measured by a geometric criteria, such as the aspect ratio of the elements.

  2. i.e., meshes with good element quality

  3. An FE solver is the specific algorithm in FEA that is used to solve the PDE that the system represents.

  4. In this section, mesh quality refers to aspect ratio of the elements in the mesh.

  5. This is expected behaviour described in the accompanying documentation of Tetgen, as the possibilities to improve mesh by inserting new points are limited.

  6. Given two displacement vectors, ua and ub, the L2 norm in this paper is defined as ||ua − ub||.

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Acknowledgments

We would like to thank Hervé Delingette, Dan Popescu and Zieke Taylor for their help in editing and reviewing this article prior to submission.

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Correspondence to Bhautik Joshi.

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This investigation was supported in part by a research grant from CIMIT, by NSF grant ITR 0426558, by grant RG 347A2/2 from the NMSS, and by NIH grants R01 GM074068, R03 CA126466, R01 EB008015, and R01 RR021885.

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Joshi, B., Fedorov, A., Chrisochoides, N. et al. A quantitative assessment of approaches to mesh generation for surgical simulation. Engineering with Computers 24, 417–430 (2008). https://doi.org/10.1007/s00366-008-0088-z

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