Abstract
This paper systematically describes a real-time dissection approach for digital organs by strong coupling of geometric metaballs and physically correct mesh-free method. For organ geometry, we employ a novel hybrid model comprising both inner metaballs and high-resolution surface mesh with texture information. Through the use of metaballs, the organ interior is geometrically simplified via a set of overlapping spheres with different radii. As for digital organ’s physical representation, we systematically articulate a hybrid framework to interlink metaballs with physics-driven mesh-free method based on moving least squares (MLS) shape functions. MLS approach enables the direct and rapid transition from metaball geometry to local nodal formulations, which afford potential-energy-correct physical modeling and simulation over continuum domain with physical accuracy. For soft tissue dissection, the nature of our MLS-driven mesh-free method also facilitates adaptive topology modification and cutting surface reconstruction. To expedite simulation towards real-time performance, at the numerical level, we resort to position-based dynamics to simplify physical deformation to drive metaballs participating in the mesh-free formulation. Since nodal points participating in the physical process exist temporarily only in localized regions adjacent to the cutting path, our method could warrant accurate cutting surface without sacrificing real-time computational efficiency. This hybrid dissection technique has already been integrated into a VR-based laparoscopic surgery simulator with a haptic interface.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Nos. 61402025, 61532002 and 61672149), the National Science Foundation of USA (Nos. IIS-0949467, 1047715, and 1049448), and the Fundamental Research Funds for the Central Universities.
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Pan, J., Yan, S., Qin, H. et al. Real-time dissection of organs via hybrid coupling of geometric metaballs and physics-centric mesh-free method. Vis Comput 34, 105–116 (2018). https://doi.org/10.1007/s00371-016-1317-x
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DOI: https://doi.org/10.1007/s00371-016-1317-x