A Computational study on the effect of different design parameters on the accuracy of biopsy procedure

Document Type: Research Paper


1 Department of Mechanical Engineering, Isfahan University of Technology, Isfahan, Iran

2 School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran

3 Department of Mechanical Engineering, University of Shiraz, Shiraz, Iran


Needle insertion is a minimally invasive technique in diagnosing and treating tumors. However, to perform a surgery accurately, the tissue should have minimum amount of displacement during needle insertion so that it reaches the target tissue. Therefore, the tissue membrane has to move less to decrease rupturing under the membrane. In this study, the effect of different design parameters on displacement of the point where a puncture occurs during needle insertion is investigated. Finite element simulation is used to study the effect of mechanical properties of the tissues (hyper-viscoelastic coefficients) and geometric parameters of the needle (fillet radius, needle tip angle and needle diameter) and friction coefficient. To validate the simulations, the results were compared with previously published results in the literature, i.e. the hyper-viscoelastic properties of brain tissue in neurosurgical procedure and the hyper-viscoelastic properties of liver tissue. The results show that the hyper-viscoelastic constitutive is a suitable model to describe soft tissue behavior. Also, the mechanical properties of the tissue and needle velocity are effective on the displacement of the tissue's membrane and therefore in surgery accuracy.


Main Subjects

1. Goksel O., Salcudean S.E., P.Dimaio S., Rohling R., Morris J., 2005, 3D needle-tissue interaction simulation for prostate brachytherapy, Medical Image Computing and Computer-Assisted Intervention, MICCAI.
2. Abolhassani N., Patel R., Moallem M., 2007, Needle insertion into soft tissue: A survey, Med Eng Phys 29: 413-431.
3. Simone C., Okamura A.M., 2002, Modeling of needle insertion forces for robot-assisted percutaneous therapy, in: Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC, 2085-2091.
4. Kobayashi Y., Watanabe H., Hossi T., Kawamura K., G.Fujie M., 2012, viscoelastic and nonlinear liver modeling for needle insertion simulation, Stus mechanobiol tissue eng biomater 11:41-67.
5. Heverly M., Dupont P., Triedman J., 2005, Trajectory optimization for dynamic needle insertion, in: Proceedings of the IEEE International Conference on Robotics and Automation, 1646-1651.
6. Duriez C., Guebrt C., Marchal M., Cotin S., Grisoni L., 2009, Interactive simulation of flexible needle insertion based on constraint models, Medical Image Computing and Computer-Assisted Intervention, MICCAI.
7. Samur E., Sedef M., Basdogan C., Avtan L., Duzgun O., 2007, A robotic indenter for minimally invasive measurement and characterization of soft tissue response, Medical Image Analysis 11:361-373.
8. Miller K., 1999, Constitutive model of brain tissue suitable for finite element analysis of surgical procedures, J Biomech 32: 531-537.
9. Sharifi Sedeh R., Ahmadian M.T., Janabi Sharifi F. (2010). Modeling, simulation, and optimal initiation planning for needle insertion into the liver, J Biomech Eng 132: 1-11.
10. Miller K., Chinzei K. (2002). Mechanical properties of brain tissue in tension, J Biomech 35: 483-490.
11. Miller K., Chinzei K., Orssengo G., Bendnarz P. (2000). Mechanical properties of brain tissue in-vivo: experiment and computer simulation, J of Biomechanics 33: 1369-1376.
12. Rashid B., Destrade M., D.Gilchrist M. (2013). Mechanical characterization of brain tissue in simple shear at dynamic strain rates, J mech behav biomed mate 28: 71-85.
13. Miller K., Chinzei K. (1997). Constitutive modeling of brain tissue: experiment and theory, J Biomech 30(11/12): 1115-1121.

14. Han P., Ehmann K. (2013). Study of the effect of cannula rotation on tissue cutting for needle biopsy, Med Eng Phys 35: 1584-1590.
15. M.Okamura A., Simone C., D. O,Leary M. (2004). Force modeling for needle insertion into soft tissue, In: Proceedings of the IEEE transactions biomedical engineering 51(10): 1707-1716.
16. Z.Moore J., Malukhin K., J.Shin A., F.Ehmann K. (2011). Hollow needle tissue insertion force model, CIRP Ann Manuf Technol 60: 157-160.
17. Mahvash M., E.Dupont P. (2009). Fast needle insertionto minimize tissue deformation and damage, in: Proceedings of the IEEE International conference on robotics and automation, Kobe, Japan, 3097-3102.
18. Atkins A.G., Mai Y.W. (1985). Elastic and plastic fracture: metals, polymers, ceramics, composites, biological materials, Chichester: Ellis Halsted Press, 1st ed.
19. Lathrop A., Smith R., Webster R. (2008). Needle-membrane puncture mechanics, in: Proceedings of the International conference medical image computer assisted intervention , MICCAI.

20. Mahvash M., Hayward V. (2001). Haptic rendering of cutting, a fracture mechanics approach. Haptics-e, Electron J Haptics Res 2(3): 1-12.
21. Mahvash M., E.Dupont P. (2010). Mechanics of Dynamic needle insertion into a biological material, IEEE Transactions biomedical engineering 57(4): 934-943.
22. Gokgol C., Basdogan C., Canadinc D. (2012). Estimation of fracture toughness of liver tissue: experiments and validation, Med Eng Phys 34: 882-891.
23. Yarpuzlu B., Ayyildiz M., Enis Tok O., Ranan Gulhan A., Cagatay B. (2014). Correltion between the mechanical and histological properties of liver tissue, J Mech Behav Biomed Mater 29: 403-416.
24. Sharifi Sedeh R. (2005). Online control of needle injection in haptic devices into soft tissue using finite element method, MS thesis, Sharif University of technology, Iran (in Persian).