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Real-time non-uniform surface refinement model for lung adenocarcinoma surgery

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Abstract

Soft tissue models play a crucial role in virtual surgery. However, most existing methods use uniform meshes and overall refinement to construct inhomogeneous soft tissues for virtual lungs. This leads to a complex computation and poor model realism. Therefore, a real-time non-uniform surface refinement model (RNSM) for lung adenocarcinoma surgery is proposed in this paper. First, to better describe the inhomogeneous soft tissues, the tetrahedra are subdivided to different degrees depending on their densities, which reduce the model’s complexity while ensuring accuracy. Second, to improve the model accuracy, the model surface is subdivided using the Loop subdivision method. Finally, an optimal algorithm based on deformation radius is designed to enhance the deformation in real-time, in which a linear attenuation method of physical quantities is used to simulate the deformation of the weak deformation regions directly, and the finite element method (FEM) is used for the strong deformation regions. The experimental results show that the model is more accurate and faster than the existing soft tissue models for lung adenocarcinoma surgery simulation.

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References

  1. Li D, Shi J, Dong X, Liang D, Jin J, He Y (2022) Epidemiological characteristics and risk factors of lung adenocarcinoma: a retrospective observational study from North China. Front Oncol 12:892571. https://doi.org/10.3389/fonc.2022.892571

    Article  PubMed  PubMed Central  Google Scholar 

  2. Zhang Y, Chen Z, Hu H, Chen H (2022) Surgical strategies for pre-and minimally invasive lung adenocarcinoma 3.0: lessons learned from the optimal timing of surgical intervention. Sem Thoracic Cardiovasc Surg 34(1):311–314. https://doi.org/10.1053/j.semtcvs.2020.12.009

    Article  Google Scholar 

  3. Kirana KP (2023) A comparison between the results from linear analysis and nonlinear analysis in the context of simulation of biological materials. J Compos Sci 7(3):109. https://doi.org/10.3390/jcs7030109

    Article  Google Scholar 

  4. Roh TH, Oh JW, Jang CK, Choi S, Kim EH, Hong CK, Kim SH (2021) Virtual dissection of the real brain: integration of photographic 3D models into virtual reality and its effect on neurosurgical resident education. Neurosurgical Focus 51(2):E16. https://doi.org/10.3171/2021.5.FOCUS21193

    Article  PubMed  Google Scholar 

  5. Jin C, Dai L, Wang T (2021) The application of virtual reality in the training of laparoscopic surgery: a systematic review and meta-analysis. Int J Surg 87:105859. https://doi.org/10.1016/j.ijsu.2020.11.022

    Article  PubMed  Google Scholar 

  6. Zhang X, Zhang W, Sun W, Song A (2022) A new soft tissue deformation model based on Runge-Kutta: application in lung. Comput Biol Med 148:105811. https://doi.org/10.1016/j.compbiomed.2022.105811

    Article  PubMed  Google Scholar 

  7. Kou J, Gu X, Kang L (2022) Correlation analysis of computed tomography features and pathological types of multifocal ground-glass godular lung adenocarcinoma. Comput Math Methods Med. https://doi.org/10.1155/2022/7267036

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lv YL, Zhang J, Xu K, Jin XY, Zhang XB, Yang HH, Fan XH, Zhang YJ, Li M, Zheng ZC, Huang J, Ye XD, Tao GY, Han YC, Ye B (2022) Computed tomography and frozen sections: concordance rates for distinguishing lung adenocarcinoma—a cohort study. Asian J Surg 45(15):2172–2178. https://doi.org/10.1016/j.asjsur.2022.03.001

    Article  PubMed  Google Scholar 

  9. Song Y, Chen D, Lian D, Xu S, Xiao H (2022) Study on the correlation between CT features and vascular tumor thrombus together with nerve invasion in surgically resected lung adenocarcinoma. Front Surg 9:931568. https://doi.org/10.3389/fsurg.2022.931568

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hu Y, Schneider T, Wang B, Zorin D, Panozzo D (2020) Fast tetrahedral meshing in the wild. ACM Trans Graphics 39(4):117–121. https://doi.org/10.1145/3386569.3392385

    Article  Google Scholar 

  11. Qi L, Guo-Dong C, Shu-Zhen W (2020) Softness-based adaptive mesh refinement algorithm for soft tissue deformation. Biosystems 191:104103. https://doi.org/10.1016/j.biosystems.2020.104103

    Article  PubMed  Google Scholar 

  12. Ballit A, Dao TT (2022) HyperMSM: a new MSM variant for efficient simulation of dynamic soft-tissue deformations. Comput Methods Programs Biomed 216:106659. https://doi.org/10.1016/j.cmpb.2022.106659

    Article  PubMed  Google Scholar 

  13. Berndt I, Torchelsen R, Maciel A (2017) Efficient surgical cutting with position-based dynamics. IEEE Comput Graphics Appl 37(3):24–31. https://doi.org/10.1109/MCG.2017.45

    Article  Google Scholar 

  14. Kumara KP (2014) A study of speed of the boundary element method as applied to the realtime computational simulation of biological organs. Electron J Bound Elem 12(2):1–25. https://doi.org/10.48550/arXiv.1311.4533

    Article  Google Scholar 

  15. Shi W, Gao X, Lv L, Pan Z, Shao J (2021) A new geometric combination of cutting and bleeding modules for surgical simulation systems. Comput Methods Prog Biomed 206:106109. https://doi.org/10.1016/j.cmpb.2021.106109

    Article  Google Scholar 

  16. Zhang X, Sun X, Sun W, Xu T, Wang P, Jha SK (2022) Deformation expression of soft tissue based on BP neural network. Intell Autom Soft Comput 32(2):1041–1053. https://doi.org/10.32604/iasc.2022.016543

    Article  Google Scholar 

  17. Xu W, Wang Y, Huang W, Duan Y (2022) An efficient nonlinear mass-spring model for anatomical virtual reality. IEEE Trans Instrum Meas 71:1–10. https://doi.org/10.1109/TIM.2022.3164132

    Article  Google Scholar 

  18. Zhang X, Yu X, Sun W, Song A (2020) An optimized oodel for the local compression deformation of soft tissue. KSII Trans Internet Inform Syst 14(2):671–686. https://doi.org/10.3837/tiis.2020.02.011

    Article  Google Scholar 

  19. Kirana KP, Ghosal A (2012) Real-time computer simulation of three dimensional elastostatics using the finite point method. Appl Mech Mater 110:2740–2745. https://doi.org/10.4028/www.scientific.net/AMM.110-116.2740

    Article  Google Scholar 

  20. Xie H, Song J, Zhong Y, Li J, Gu C, Choi KS (2021) Extended kalman filter nonlinear finite element method for nonlinear soft tissue deformation. Comput Methods Prog Biomed 200:105828. https://doi.org/10.1016/j.cmpb.2020.105828

    Article  Google Scholar 

  21. Zhang X, Zhang W, Sun W, Wu H, Song A, Jha SK (2022) A real-time cutting model based on finite element and order reduction. Comput Syst Sci Eng 43(1):1–15. https://doi.org/10.32604/csse.2022.024950

    Article  Google Scholar 

  22. Hou W, Liu PX, Zheng M (2019) A new model of soft tissue with constraints for interactive surgical simulation. Comput Methods Programs Biomed 175:35–43. https://doi.org/10.1016/j.cmpb.2019.03.018

    Article  PubMed  Google Scholar 

  23. Lauzeral N, Borzacchiello D, Kugler M, George D, Rémond Y, Hostettler A, Chinesta F (2019) A model order reduction approach to create patient-specific mechanical models of human liver in computational medicine applications. Comput Methods Programs Biomed 170:95–106. https://doi.org/10.1016/j.cmpb.2019.01.003

    Article  PubMed  Google Scholar 

  24. Chittajallu SNSH, Richhariya A, Tse KM, Chinthapenta V (2022) A review on damage and rupture dodelling for soft tissues. Bioengineering 9(1):26. https://doi.org/10.3390/bioengineering9010026

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Wang K, Kang S, Tian R, Zhang X, Wang Y (2020) Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol 75(5):341–347. https://doi.org/10.1016/j.crad.2020.03.004

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Song J, Xie H, Zhong Y, Li J, Gu C, Choi KS (2021) Reduced-order extended kalman filter for deformable tissue simulation. J Mech Phys Solids 158:104696. https://doi.org/10.1016/j.jmps.2021.104696

    Article  Google Scholar 

  27. Zhang X, Wu H, Sun W, Yuan C (2020) An optimized mass-spring model with shape restoration ability based on volume conservation. KSII Trans Internet Inform Syst 14(4):1738–1756. https://doi.org/10.3837/tiis.2020.04.018

    Article  Google Scholar 

  28. Tang Y, Liu S, Deng Y, Zhang Y, Yin L, Zheng W (2020) An improved method for soft tissue modeling. Biomed Signal Process Control 65:102367. https://doi.org/10.1016/j.bspc.2020.102367

    Article  Google Scholar 

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Acknowledgements

We are grateful to Nanjing University of Information Science and Technology for providing a research environment and computing equipment.

Funding

This study was supported, in part, by the National Nature Science Foundation of China under Grant 62272236, 62376128; in part, by the Natural Science Foundation of Jiangsu Province under Grant BK20201136, BK20191401.

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Correspondence to Xiaorui Zhang.

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Zhang, X., Wang, Z., Sun, W. et al. Real-time non-uniform surface refinement model for lung adenocarcinoma surgery. Med Biol Eng Comput 62, 183–193 (2024). https://doi.org/10.1007/s11517-023-02924-w

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