Octree cube constraints in PBD method for high resolution surgical simulation

Rintaro Miyazaki; Yuichiro Hayashi; Masahiro Oda; Kensaku Mori

doi:10.1117/12.2654092

3 April 2023 Octree cube constraints in PBD method for high resolution surgical simulation

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, Kensaku Mori

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 124642N (2023) https://doi.org/10.1117/12.2654092
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

This paper proposes a deformable tissue model that introduces octree lattice vertex layout and cubic constraints to the orthodox PBD (Position Based Dynamics) method. Surgical simulation is expected to provide a safe method for training in surgery, which is especially useful for preoperative education of inexperienced surgeons and/or for the case a prior attempt is required. To build a surgical simulator, it is necessary to develop organ models with deformations and interaction algorithms between surgical instruments and organ models, all of which must be performed in real time. Since existing surgical simulators focus on real-time performance, the resolution of organ models is limited. The proposed method restricts the vertex locations of the PBD method to the vertices of the octree lattice to save computation time while maintaining a high deformation resolution. To obtain appropriate results even for large deformations, three-dimensional constraints are applied to each octree cube as the constraints of the PBD method. In the simulations, we tested the overall deformation by dropping a liver model and the local deformation scene by laparoscopic clipping. As a result, we achieved deformation simulations at 26.5 fps for the model with approximately 2,672 cube elements and 20,659 vertices.

Citation Download Citation

Rintaro Miyazaki, Yuichiro Hayashi, Masahiro Oda, and Kensaku Mori "Octree cube constraints in PBD method for high resolution surgical simulation", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124642N (3 April 2023); https://doi.org/10.1117/12.2654092

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