Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 3: 112 - 112

DOI: https://doi.org/10.1186/s10033-022-00796-z

Special Issue on Processing of Biological Tissue

Soft Tissue Deformation Modeling in the Procedure of Needle Insertion: A Kriging-Based Method

  • Yong Lei ,
  • Murong Li ,
  • Dedong Gao
Expand
  • 1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China;
    2. School of Mechanical Engineering, Qinghai University, Xining, 810016, China

Received date: 2021-03-17

  Revised date: 2022-06-27

  Online published: 2022-10-24

Supported by

Supported by National Major Scientific Research Instrument Development Project of China (Grant No. 81827804), Zhejiang Provincial Natural Science Foundation of China (Grant No. LSD19H180004), Science Fund for Creative Group of NSFC (Grant No. 51821903), and National Natural Science Foundation of China (Grant No. 51665049)

Fold

Abstract

The simulation and planning system (SPS) requires accurate and real-time feedback regarding the deformation of soft tissues during the needle insertion procedure. Traditional mechanical-based models such as the finite element method (FEM) are widely used to compute the deformations of soft tissue. However, it is difficult for the FEM or other methods to find a balance between an acceptable image fidelity and real-time deformation feedback due to their complex material properties, geometries and interaction mechanisms. In this paper, a Kriging-based method is applied to model the soft tissue deformation to strike a balance between the accuracy and efficiency of deformation feedback. Four combinations of regression and correlation functions are compared regarding their ability to predict the maximum deformations of ten characteristic markers at a fixed insertion depth. The results suggest that a first order regression function with Gaussian correlation functions can best fit the results of the ground truth. The functional response of the Kriging-based method is utilized to model the dynamic deformations of markers at a series of needle insertion depths. The feasibility of the method is verified by investigating the adaptation to step variations. Compared with the ground truth of the finite element (FE) results, the maximum residual is less than 0.92 mm in the Y direction and 0.31 mm in the X direction. The results suggest that the Kriging metamodel provides real-time deformation feedback for a target and an obstacle to a SPS.

Key words： Kriging metamodel; Needle insertion; Real-time simulator; Soft tissue deformation

Cite this article

Yong Lei , Murong Li , Dedong Gao . Soft Tissue Deformation Modeling in the Procedure of Needle Insertion: A Kriging-Based Method[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(3) : 112 -112 . DOI: 10.1186/s10033-022-00796-z

References

[1] N Abolhassani, R Patel, M Moallem. Needle insertion into soft tissue: A survey. Medical Engineering and Physics, 2007, 29(4): 413-431.
[2] D Gao, Y Lei, H Zheng. Needle steering for robot-assisted insertion into soft tissue: A survey. Chinese Journal of Mechanical Engineering, 2012, 25(4): 629-638.
[3] O'Flynn EAM, Wilson ARM, MJ Michell. Image-guided breast biopsy: State-of-the-art. Clinical Radiology, 2010, 65(4): 259-270.
[4] O Goksel, K Sapchuk, S E Salcudean. Haptic simulator for prostate brachytherapy with simulated needle and probe interaction. IEEE Transactions on Haptics, 2011, 4(3): 188-198.
[5] N J Cowan, K Goldberg, G S Chirikjian, et al. Robotic needle steering: Design, modeling, planning, and image guidance. Surgical Robotics: Systems Applications and Visions, 2011: 557-582.
[6] W Sun, Sr H Gao, O Kaynak. Finite frequency control for vehicle active suspension systems. IEEE Transactions on Control Systems Technology, 2011, 19(2): 416-422.
[7] A M Okamura, C Simone, M D O'Leary. Force modeling for needle insertion into soft tissue. IEEE Transactions on Biomedical Engineering, 2004, 51(10): 1707-1716.
[8] G Wan, Z Wei, L Gardi, et al. Brachytherapy needle deflection evaluation and correction. Medical Physics, 2005, 32(4): 902-909.
[9] N Abolhassani, R V Patel, F Ayazi. Minimization of needle deflection in robot-assisted percutaneous therapy. International Journal of Medical Robotics and Computer Assisted Surgery, 2007, 3(2): 140-148.
[10] O Goksel, E Dehghan, S E Salcudean. Modeling and simulation of flexible needles. Medical Engineering and Physics, 2009, 31(9): 1069-1078.
[11] S Misra, K B Reed, B W Schafer, et al. Observations and models for needle-tissue interactions. Proceedings-IEEE International Conference on Robotics and Automation, 12-17 May, 2009, Kobe, Japan.
[12] J T Hing, A D Brooks, J P Desai. Reality-based needle insertion simulation for haptic feedback in prostate brachytherapy. Proceedings - IEEE International Conference on Robotics and Automation, 15-19 May, 2006, Orlando, FL, USA.
[13] S P DiMaio, S E Salcudean. Needle insertion modeling and simulation. IEEE Transactions on Robotics and Automation, 2003, 19(5): 864-875.
[14] S Misra, K T Ramesh, A M Okamura. Modeling of tool-tissue interactions for computer-based surgical simulation: A literature review. Presence: Teleoperators and Virtual Environments, 2008, 17(5): 463-491.
[15] Y Kobayashi, A Onishi, T Hoshi, et al. Development and validation of a viscoelastic and nonlinear liver model for needle insertion. International Journal of Computer Assisted Radiology and Surgery, 2009, 4(1): 53-63.
[16] S Misra, KJ Macura, KT Ramesh, et al. The importance of organ geometry and boundary constraints for planning of medical interventions. Medical Engineering Physics, 2009, 31(2): 195-206.
[17] JO Den Buijs, HHG Hansen, RGP Lopata, et al. Predicting target displacements using ultrasound elastography and finite element modeling. IEEE Transactions on Biomedical Engineering, 2011, 58(11): 3143-3155.
[18] B Takabi, BL Tai. A review of cutting mechanics and modeling techniques for biological materials. Medical Engineering and Physics, 2017, 45: 1-14.
[19] C Gokgol, C Basdogan, D Canadinc. Estimation of fracture toughness of liver tissue: Experiments and validation. Medical Engineering and Physics, 2012, 34(7): 882-891.
[20] S Singh, MC Lo, VB Damodaran, et al. Modeling the insertion mechanics of flexible neural probes coated with sacrificial polymers for optimizing probe design. Sensors, 2016, 16(3).
[21] M Oldfield, D Dini, G Giordano, et al. Detailed finite element modelling of deep needle insertions into a soft tissue phantom using a cohesive approach. Computer Methods in Biomechanics and Biomedical Engineering, 2013, 16(5): 530-543.
[22] C J Paulus, S Suwelack, N Schoch, et al. Simulation of complex cuts in soft tissue with the extended finite element method (X-FEM). Preprint, 2014, doi: https://doi.org/10.11588/emclpp.2014.02.17635.
[23] Q Q Cheng, P X Liu, P H Lai, et al. An interactive meshless cutting model for nonlinear viscoelastic soft tissue in surgical simulators. IEEE Access, 2017, 5: 16359-71.
[24] Q Q Cheng, P X Liu, P H Lai, et al. A novel haptic interactive approach to simulation of surgery cutting based on mesh and meshless models. Journal of Healthcare Engineering, 2018, 2018(5): 1-16.
[25] M Yuan, Z Chen, B Yao, et al. Time optimal contouring control of industrial biaxial gantry: A highly efficient analytical solution of trajectory planning. IEEE/ASME Transactions on Mechatronics, 2017, 22(1): 247-257.
[26] Z Chen, Y J Pan, J Gu. Integrated adaptive robust control for multilateral teleoperation systems under arbitrary time delays. International Journal of Robust and Nonlinear Control, 2016, 26(12): 2708-2728.
[27] JPC Kleijnen. Kriging metamodeling in simulation: A review. European Journal of Operational Research, 2009, 192(3): 707-716.
[28] M Meckesheimer, AJ Booker, RR Barton, et al. Computationally inexpensive metamodel assessment strategies. AIAA Journal, 2002, 40(10): 2053-2060.
[29] Y Hung, VR Joseph, S N Melkote. Analysis of computer experiments with functional response. Technometrics, 2015, 57(1): 35-44.
[30] J Yao, Z Jiao, D Ma, et al. High-accuracy tracking control of hydraulic rotary actuators with modeling uncertainties. IEEE/ASME Transactions on Mechatronics, 2014, 19(2): 633-641.
[31] J Yao, Z Jiao, D Ma. Extended-state-observer-based output feedback nonlinear robust control of hydraulic systems with backstepping. IEEE Transactions on Industrial Electronics, 2014, 61(11): 6285-6293.
[32] D Gao, Y Lei, B Lian, et al. Modeling and simulation of flexible needle insertion into soft tissue using modified local constraints. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 2016, 138(12).
[33] SN Lophaven, HB Nielsen, J Søndergaard. DACE -- A MATLAB Kriging Toolbox -- Version 2.0. 2002.
[34] AJ Booker. Design and analysis of computer experiments. 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1998.
[35] VR Joseph, L Kang. Regression-based inverse distance weighting with applications to computer experiments. Technometrics, 2011, 53(3): 254-265.
[36] B Williams, D Higdon, J Gattiker, et al. Combining experimental data and computer simulations, with an application to flyer plate experiments. Bayesian Analysis, 2006, 1(4): 765-792.
Options
Outlines

/

〈 〉

　 Copyright © Chinese Journal of Mechanical Engineering, All Rights Reserved.
Powered by Beijing Magtech Co. Ltd,.