Particle-based simulation technique for medical applications

References

• Baccouch, M. (2021). A brief summary of the finite element method for differential equations. In Finite element methods and their applications. https://doi.org/10.5772/intechopen.83274
   Google Scholar
• Bender, J., Müller, M., Otaduy, M. A., Teschner, M., & Macklin, M. (2014). A survey on position-based simulation methods in computer graphics. Computer Graphics Forum, 33(6), 228–251. https://doi.org/10.1111/cgf.12346
   Web of Science ®Google Scholar
• Bianchi, G., Solenthaler, B., Székely, G., & Harders, M. (2004). Simultaneous topology and stiffness identification for mass-spring models based on FEM reference deformations. In C. Barillot, D. R. Haynor, & P. Hellier (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2004 (pp. 293–301). Springer. https://doi.org/10.1007/978-3-540-30136-3_37
   Google Scholar
• Bouaziz, S., Martin, S., Liu, T., Kavan, L., & Pauly, M. (2014). Projective dynamics. ACM Transactions on Graphics, 33(4), 1–11. https://doi.org/10.1145/2601097.2601116
   Web of Science ®Google Scholar
• Bro-Nielsen, M., & Cotin, S. (1996). Real-time volumetric deformable models for surgery simulation using finite elements and condensation. Computer Graphics Forum, 15(3), 57–66. https://doi.org/10.1111/1467-8659.1530057
   Web of Science ®Google Scholar
• Cazacu, O., Chandola, N., Alves, J. L., & Revil-Baudard, B. (2014). Importance of the consideration of the specificities of local plastic deformation on the response of porous solids with Tresca matrix. European Journal of Mechanics - A/Solids, 47, 194–205. https://doi.org/10.1016/j.euromechsol.2014.04.004
   Web of Science ®Google Scholar
• Choi, K.-J., & Ko, H.-S. (2002). Stable but responsive cloth. ACM Transactions on Graphics, 21(3), 604–611. https://doi.org/10.1145/566654.566624
   Web of Science ®Google Scholar
• Comas, O., Taylor, Z. A., Allard, J., Ourselin, S., Cotin, S., & Passenger, J. (2008). Efficient nonlinear FEM for soft tissue modelling and Its GPU implementation within the open source framework SOFA. In F. Bello, & P. J. E. Edwards (Eds.), Biomedical simulation (pp. 28–39). Springer. https://doi.org/10.1007/978-3-540-70521-5_4
   Google Scholar
• Felippa, C. (2000). A systematic approach to the element-Independent corotational dynamics of finite elements (Report No. CU-CAS-00-03). Center for Aerospace Structures.
   Google Scholar
• Fratarcangeli, M., Tibaldo, V., & Pellacini, F. (2016). Vivace. ACM Transactions on Graphics, 35(6), 214:1–214:1. https://doi.org/10.1145/2980179.2982437
   Google Scholar
• Gibson, S., & Mirtich, B. (1997). A survey of deformable modeling in computer graphics (Report No. TR97-10). MERL — Mitsubishi Electric Research Laboratory.
   Google Scholar
• He, Y., & Tang, Z. (2021). Strategy for task offloading of multi-user and multi-server based on cost optimization in mobile edge computing environment. Journal of Information Processing Systems, 17(3), 615–629. https://doi.org/10.3745/JIPS.01.0078
   Web of Science ®Google Scholar
• Jayasudha, K., & Kabadi, M. G. (2020). Soft tissues deformation and removal simulation modelling for virtual surgery. International Journal of Intelligence and Sustainable Computing, 1(1), 83–100. https://doi.org/10.1504/IJISC.2020.104830
   Google Scholar
• Joldes, G. R., Wittek, A., Couton, M., Warfield, S. K., & Miller, K. (2009). Real-Time prediction of brain shift using nonlinear finite element algorithms. In G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, & C. Taylor (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2009 (pp. 300–307). Springer Berlin Heidelberg.
   Google Scholar
• Kim, J., Kwon, K., & Shin, B.-S. (2021). Adaptive tetrahedral mesh generation for Non-uniform soft tissue simulation. Human-Centric Computing and Information Sciences, 11(29), https://doi.org/10.22967/HCIS.2021.11.029
   Google Scholar
• Kubiak, B., Pietroni, N., Ganovelli, F., & Fratarcangeli, M. (2007). A robust method for real-time thread simulation. Proceedings of the 2007 ACM Symposium on Virtual Reality Software and Technology, 85–88. https://doi.org/10.1145/1315184.1315198
   Google Scholar
• Lampen, N., Kim, D., Fang, X., Xu, X., Kuang, T., Deng, H. H., Barber, J. C., Gateno, J., Xia, J., & Yan, P. (2022). Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning. International Journal of Computer Assisted Radiology and Surgery, 17(5), 945–952. https://doi.org/10.1007/s11548-022-02596-1
   PubMed Web of Science ®Google Scholar
• Lee, B., Popescu, D. C., Joshi, B., & Ourselin, S. (2006). Efficient topology modification and deformation for finite element models using condensation. Studies in Health Technology and Informatics, 119, 299–304. PubMed.
   PubMedGoogle Scholar
• Li, C., Ding, J., Hong, Z., Pan, Y., & Liu, P. X. (2018). A surface mass-spring model With New flexion springs and collision detection algorithms based on volume structure for real-time soft-tissue deformation interaction. IEEE Access, 6, 75572–75597. https://doi.org/10.1109/ACCESS.2018.2883679
   Web of Science ®Google Scholar
• Liu, Y., Guan, C., Li, J., Yu, X., & Zhang, S. (2020). The PBD model based simulation for soft tissue deformation in virtual surgery. Journal of Physics: Conference Series, 1621(1), 012043. https://doi.org/10.1088/1742-6596/1621/1/012043
   Google Scholar
• Macklin, M., & Müller, M. (2013). Position based fluids. ACM Transactions on Graphics, 32(4), 1–12. https://doi.org/10.1145/2461912.2461984
   Web of Science ®Google Scholar
• Macklin, M., Müller, M., & Chentanez, N. (2016). XPBD. Proceedings of the 9th International Conference on Motion in Games, 49–54. https://doi.org/10.1145/2994258.2994272
   Google Scholar
• Mendizabal, A., Márquez-Neila, P., & Cotin, S. (2020). Simulation of hyperelastic materials in real-time using deep learning. Medical Image Analysis, 59, 101569. https://doi.org/10.1016/j.media.2019.101569
   PubMed Web of Science ®Google Scholar
• Mozafary, V., & Payvandy, P. (2018). Introducing and optimizing a novel mesh for simulating knitted fabric. The Journal of The Textile Institute, 109(2), 202–218. https://doi.org/10.1080/00405000.2017.1335446
   Web of Science ®Google Scholar
• Müller, M. (2008). Hierarchical position based dynamics. Proceedings of the Fifth Workshop on Virtual Reality Interactions and Physical Simulations, VRIPHYS 2008, Grenoble, France, 2008, 1–10. https://doi.org/10.2312/PE/vriphys/vriphys08/001-010
   Google Scholar
• Müller, M., Heidelberger, B., Hennix, M., & Ratcliff, J. (2007). Position based dynamics. Journal of Visual Communication and Image Representation, 18(2), 109–118. https://doi.org/10.1016/j.jvcir.2007.01.005
   Web of Science ®Google Scholar
• Nealen, A., Müller, M., Keiser, R., Boxerman, E., & Carlson, M. (2006). Physically based deformable models in computer graphics. Computer Graphics Forum, 25(4), 809–836. https://doi.org/10.1111/j.1467-8659.2006.01000.x
   Web of Science ®Google Scholar
• Nedel, L. P., & Thalmann, D. (1998). Real time muscle deformations using mass-spring systems. Proceedings Computer Graphics International (Cat. No.98EX149), 156–165. https://doi.org/10.1109/CGI.1998.694263
   Google Scholar
• Omar, N., Zhong, Y., Jazar, R. N., Subic, A., Smith, J., & Shirinzadeh, B. (2015). Soft tissue modelling with conical springs. Bio-Medical Materials and Engineering, 26(Suppl 1), S207–S214. PubMed. https://doi.org/10.3233/bme-151307
   PubMedGoogle Scholar
• Pezzementi, Z., Ursu, D., Misra, S., & Okamura, A. M. (2008). Modeling realistic tool-tissue interactions with haptic feedback: A learning-based method. 2008 Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems, 209–215. https://doi.org/10.1109/HAPTICS.2008.4479944
   Google Scholar
• Ren, D., Chen, Y., Lin, B., Zeng, F., Huang, J., & Wang, J. (2017). Modelling and simulation of vessel surgery based on mass-spring. MATEC Web of Conferences, 108, 13004. https://doi.org/10.1051/matecconf/201710813004
   Google Scholar
• Reznick, R., Regehr, G., MacRae, H., Martin, J., & McCulloch, W. (1997). Testing technical skill via an innovative “bench station” examination. The American Journal of Surgery, 173(3), 226–230. https://doi.org/10.1016/S0002-9610(97)89597-9
   PubMed Web of Science ®Google Scholar
• Rizzo, A. S., & Shilling, R. (2017). Clinical Virtual Reality tools to advance the prevention, assessment, and treatment of PTSD. European Journal of Psychotraumatology, 8(sup5), 1414560. https://doi.org/10.1080/20008198.2017.1414560
   PubMed Web of Science ®Google Scholar
• Romeo, M., Monteagudo, C., & Sánchez-Quirós, D. (2018). Muscle simulation with extended position based dynamics. Proceedings of the XXVIII Spanish Computer Graphics Conference, 1–10. https://doi.org/10.2312/ceig.20181146
   Google Scholar
• Rungjiratananon, W., Kanamori, Y., & Nishita, T. (2010). Chain shape matching for simulating complex hairstyles. Computer Graphics Forum, 29(8), 2438–2446. https://doi.org/10.1111/j.1467-8659.2010.01755.x
   Web of Science ®Google Scholar
• San-Vicente, G., Aguinaga, I., & Celigueta, J. T. (2012). Cubical mass-spring model design based on a tensile deformation test and nonlinear material model. IEEE Transactions on Visualization and Computer Graphics, 18(2), 228–241. https://doi.org/10.1109/TVCG.2011.32
   PubMed Web of Science ®Google Scholar
• Schwam, Z. G., Kaul, V. F., Bu, D. D., Iloreta, A.-M. C., Bederson, J. B., Perez, E., Cosetti, M. K., & Wanna, G. B. (2021). The utility of augmented reality in lateral skull base surgery: A preliminary report. American Journal of Otolaryngology, 42(4), 102942. https://doi.org/10.1016/j.amjoto.2021.102942
   PubMed Web of Science ®Google Scholar
• Segato, A., Vece, C. D., Zucchelli, S., Marzo, M. D., Wendler, T., Azampour, M. F., Galvan, S., Secoli, R., & De Momi, E. (2021). Position-Based dynamics simulator of brain deformations for path planning and intra-operative control in keyhole neurosurgery. IEEE Robotics and Automation Letters, 6(3), 6061–6067. https://doi.org/10.1109/LRA.2021.3090016
   Web of Science ®Google Scholar
• Selle, A., Lentine, M., & Fedkiw, R. (2008). A mass spring model for hair simulation. ACM SIGGRAPH 2008 Papers, https://doi.org/10.1145/1399504.1360663
   Google Scholar
• Shan, C., Zhao, J., Zhao, W., Zhang, T., Du, B., & Yuan, Z. (2022). Magnetic-Actuated cyber-physical system for interventional surgery. Human-Centric Computing and Information Sciences, 12(37), https://doi.org/10.22967/HCIS.2022.12.037
   Google Scholar
• Shi, W., Liu, P. X., & Zheng, M. (2019). A new volumetric geometric model for cutting procedures in surgical simulation. Computer Methods and Programs in Biomedicine, 178, 77–84. https://doi.org/10.1016/j.cmpb.2019.06.015
   PubMed Web of Science ®Google Scholar
• Stomakhin, A., Schroeder, C., Chai, L., Teran, J., & Selle, A. (2013). A material point method for snow simulation. ACM Transactions on Graphics, 32(4), 12:1–102:10. https://doi.org/10.1145/2461912.2461948
   Web of Science ®Google Scholar
• Tan, S., Zhou, H., & Zheng, J. (2021). A hybrid model method for accurate surface deformation and incision based on FEM and PBD. Scientific Programming, 2021, e8343312. https://doi.org/10.1155/2021/8343312
   Web of Science ®Google Scholar
• Taylor, Z. A., Comas, O., Cheng, M., Passenger, J., Hawkes, D. J., Atkinson, D., & Ourselin, S. (2008). Modelling anisotropic viscoelasticity for real-time soft tissue simulation. In D. Metaxas, L. Axel, G. Fichtinger, & G. Székely (Eds.), Medical image computing and computer-assisted intervention – MICCAI 2008 (pp. 703–710). Springer. https://doi.org/10.1007/978-3-540-85988-8_84
   Google Scholar
• Umetani, N., Schmidt, R., & Stam, J. (2015). Position-Based elastic rods. Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 21–30. https://dl.acm.org/doi/10.5555/2849517.2849522
   Google Scholar
• Wang, Z., Wu, L., Fratarcangeli, M., Tang, M., & Wang, H. (2018). Parallel multigrid for nonlinear cloth simulation. Computer Graphics Forum, 37(7), 131–141. https://doi.org/10.1111/cgf.13554
   Web of Science ®Google Scholar
• Wolper, J., Fang, Y., Li, M., Lu, J., Gao, M., & Jiang, C. (2019). CD-MPM. ACM Transactions on Graphics, 38(4), 119:1–119:1. https://doi.org/10.1145/3306346.3322949
   Google Scholar
• Xie, H., Song, J., Zhong, Y., & Gu, C. (2020). Kalman filter finite element method for real-time soft tissue modeling. IEEE Access, 8, 53471–53483. https://doi.org/10.1109/ACCESS.2020.2981400
   Google Scholar
• Yoshida, S., Fukuda, S., Moriyama, S., Yokoyama, M., Taniguchi, N., Shinjo, K., Sugimoto, M., Saito, K., & Fujii, Y. (2019). Mp35-09 holographic surgical planning using a wearable mixed reality computer. Journal of Urology, 201(Supplement 4), e508–e509. https://doi.org/10.1097/01.JU.0000555999.71874.8d
   Web of Science ®Google Scholar
• Zhang, X., Zhang, W., Sun, W., & Song, A. (2022). A new soft tissue deformation model based on Runge-Kutta: Application in lung. Computers in Biology and Medicine, 148, 105811. https://doi.org/10.1016/j.compbiomed.2022.105811
   PubMed Web of Science ®Google Scholar
• Zienkiewicz, O. C., Taylor, R. L., & Zhu, J. Z. (2005). The finite element method: Its basis and fundamentals. Elsevier.
   Google Scholar