Skip to main content

Advertisement

SpringerLink
Log in

Estimating needle tip deflection in biological tissue from a single transverse ultrasound image: application to brachytherapy

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

This paper proposes a method to predict the deflection of a flexible needle inserted into soft tissue based on the observation of deflection at a single point along the needle shaft.

Methods

We model the needle-tissue as a discretized structure composed of several virtual, weightless, rigid links connected by virtual helical springs whose stiffness coefficient is found using a pattern search algorithm that only requires the force applied at the needle tip during insertion and the needle deflection measured at an arbitrary insertion depth. Needle tip deflections can then be predicted for different insertion depths.

Results

Verification of the proposed method in synthetic and biological tissue shows a deflection estimation error of \(<\)2 mm for images acquired at 35 % or more of the maximum insertion depth, and decreases to 1 mm for images acquired closer to the final insertion depth. We also demonstrate the utility of the model for prostate brachytherapy, where in vivo needle deflection measurements obtained during early stages of insertion are used to predict the needle deflection further along the insertion process.

Conclusion

The method can predict needle deflection based on the observation of deflection at a single point. The ultrasound probe can be maintained at the same position during insertion of the needle, which avoids complications of tissue deformation caused by the motion of the ultrasound probe.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Abayazid M, Roesthuis R, Reilink R, Misra S (2013) Integrating deflection models and image feedback for real-time flexible needle steering. Robot IEEE Trans 29(2):542–553

    Article  Google Scholar 

  2. Abolhassani N, Patel R, Ayazi F (2007) Needle control along desired tracks in robotic prostate brachytherapy. In: IEEE international conference on systems, man and cybernetics, 2007, ISIC, pp 3361–3366. IEEE

  3. Beer FP, Sanghi S (2012) Mechanics of materials, 6th edn. McGraw-Hill, New York (global ed./adapted by Sanjeev Sanghi edn., includes index)

    Google Scholar 

  4. Bogdanich W (2009) At VA hospital, a rogue cancer unit. The New York Times, New York

    Google Scholar 

  5. Chapman GA, Johnson D, Bodenham AR (2006) Visualisation of needle position using ultrasonography. Anaesthesia 61(2):148–158. doi:10.1111/j.1365-2044.2005.04475.x

    Article  CAS  PubMed  Google Scholar 

  6. Chin KJ, Perlas A, Chan VW, Brull R (2008) Needle visualization in ultrasound-guided regional anesthesia: challenges and solutions. Reg Anesth Pain Med 33(6):532–544

    PubMed  Google Scholar 

  7. Dehghan E, Goksel O, Salcudean SE (2006) A comparison of needle bending models. In: Medical image computing and computer-assisted intervention–MICCAI 2006, pp 305–312. Springer

  8. Fischler MA, Bolles RC (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395

    Article  Google Scholar 

  9. Glozman D, Shoham M (2007) Image-guided robotic flexible needle steering. Robot IEEE Trans 23(3):459–467. doi:10.1109/TRO.2007.898972

    Article  Google Scholar 

  10. Goksel O, Dehghan E, Salcudean SE (2009) Modeling and simulation of flexible needles. Med Eng Phys 31(9):1069–1078

    Article  PubMed  Google Scholar 

  11. Gonzales RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  12. Haddadi A, Hashtrudi-Zaad K (2011) Development of a dynamic model for bevel-tip flexible needle insertion into soft tissues. In: 2011 annual international conference of the IEEE, engineering in medicine and biology society, EMBC, pp 7478–7482. IEEE

  13. Hong J, Dohi T, Hashizume M, Konishi K, Hata N (2004) An ultrasound-driven needle-insertion robot for percutaneous cholecystostomy. Phys Med Biol 49(3):441

    Article  CAS  PubMed  Google Scholar 

  14. Khadem M, Fallahi B, Rossa C, Sloboda R, Usmani N, Tavakoli M (2015) A mechanics-based model for simulation and control of flexible needle insertion in soft tissue. In: 2015 IEEE international conference on, robotics and automation (ICRA), pp 2264–2269

  15. Krouskop TA, Wheeler TM, Kallel F, Garra BS, Hall T (1998) Elastic moduli of breast and prostate tissues under compression. Ultrason Imaging 20(4):260–274

    Article  CAS  PubMed  Google Scholar 

  16. Lachaine M, Falco T (2013) Intrafractional prostate motion management with the clarity autoscan system. Med Phys Int 1(1):72–80

    Google Scholar 

  17. Lehmann T, Rossa C, Usmani N, Sloboda R, Tavakoli M (2015) A virtual sensor for needle deflection estimation during soft-tissue needle insertion. In: 2015 IEEE international conference on, robotics and automation (ICRA), pp 1217–1222

  18. Mathiassen K, Dall’Alba D, Muradore R, Fiorini P, Elle OJ (2013) Real-time biopsy needle tip estimation in 2d ultrasound images. In: 2013 IEEE international conference on, robotics and automation (ICRA), pp 4363–4369. IEEE

  19. Misra S, Reed K, Schafer B, Ramesh K, Okamura A (2010) Mechanics of flexible needles robotically steered through soft tissue. Int J Robot Res 29(13):1640–1660. doi:10.1177/0278364910369714

    Article  Google Scholar 

  20. Moreira P, Misra S (2014) Biomechanics-based curvature estimation for ultrasound-guided flexible needle steering in biological tissues. Ann Biomed Eng. doi:10.1007/s10439-014-1203-5

  21. Nag S, Bice W, DeWyngaert K, Prestidge B, Stock R, Yu Y (2000) The american brachytherapy society recommendations for permanent prostate brachytherapy postimplant dosimetric analysis. Int J Radiat Oncol Biol Phys 46(1):221–230

    Article  CAS  PubMed  Google Scholar 

  22. Neubach Z, Shoham M (2010) Ultrasound-guided robot for flexible needle steering. Biomed Eng IEEE Trans 57(4):799–805

  23. Okamura A, Simone C, O’Leary M (2004) Force modeling for needle insertion into soft tissue. IEEE Trans Biomed Eng 51(10):1707–1716. doi:10.1109/TBME.2004.831542

    Article  PubMed  Google Scholar 

  24. Okazawa SH, Ebrahimi R, Chuang J, Rohling RN, Salcudean SE (2006) Methods for segmenting curved needles in ultrasound images. Med Image Anal 10(3):330–342

    Article  PubMed  Google Scholar 

  25. Schlosser J, Salisbury K, Hristov D (2011) We-d-220-01: tissue displacement monitoring for prostate and liver igrt using a robotically-controlled ultrasound system. Med Phys 38(6):3812–3812

    Article  Google Scholar 

  26. Spong MW, Vidyasagar M (2008) Robot dynamics and control. Wiley, Hoboken

    Google Scholar 

  27. Vrooijink GJ, Abayazid M, Patil S, Alterovitz R, Misra S (2014) Needle path planning and steering in a three-dimensional non-static environment using two-dimensional ultrasound images. Int J Robot Res 33(10): 0278364914526627

  28. Waine M, Rossa C, Sloboda R, Usmani N, Tavakoli M (2015) 3D shape visualization of curved needles in tissue from 2D ultrasound images using ransac. In: 2015 IEEE international conference on, robotics and automation, pp 4723–4728. IEEE

  29. Yan P, Cheeseborough JC, Chao KC (2012) Automatic shape-based level set segmentation for needle tracking in 3-d trus-guided prostate brachytherapy. Ultrasound Med Biol 38(9):1626–1636

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

    Carlos Rossa & Mahdi Tavakoli

  2. Department of Oncology, University of Alberta, Edmonton, Canada

    Ron Sloboda & Nawaid Usmani

Authors
  1. Carlos Rossa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ron Sloboda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nawaid Usmani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahdi Tavakoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlos Rossa.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standard

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study. Approval for the study granted by the Alberta Cancer Research Ethics Committee under file number 25837.

Additional information

This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada under grant CHRP 446520, the Canadian Institutes of Health Research (CIHR) under Grant CPG 127768, and by the Alberta Innovates Health Solutions (AIHS) under Grant CRIO 201201232.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rossa, C., Sloboda, R., Usmani, N. et al. Estimating needle tip deflection in biological tissue from a single transverse ultrasound image: application to brachytherapy. Int J CARS 11, 1347–1359 (2016). https://doi.org/10.1007/s11548-015-1329-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-015-1329-4

Keywords

Search

Navigation