Advertisement

Gain determination of feedback force for an ultrasound scanning robot using genetic algorithm

  • Yeoun-Jae Kim
  • Chang-Kyu Park
  • Kwang Gi KimEmail author
Original Article

Abstract

Purpose

The remote medical diagnosis system (RMDS) is for providing medical diagnosis to the patients located in remote sites. To apply to RMDS and medical automation, many master–slave type ultrasound scanning robots are being developed and researched. One of the important research issue of the master–slave type ultrasound scanning robot is to determine the gains of the feedback force. Therefore, in this study, we suggest a gain determination method of feedback force for a master–slave type ultrasound thyroid scanning robot using a genetic algorithm.

Method

A master–slave type ultrasound thyroid scanning robot (NCCMSU) was constructed, and the optimal y and z direction feedback force gains were calculated for NCCMSU with genetic algorithm. The Hunt–Crossley model is used to model the elastic behavior of the thyroid phantom and the thyroid scanning procedure is embedded in genetic algorithm by modeling the procedure mathematically. The genetic algorithm solves the average feedback force–overall procedure time optimization problem to seek optimal y, z direction feedback gains candidates.

Results

The rating results show that although there are some deviations among the subjective ratings, the feedback force with the determined gain setting is within the appropriate range. By analyzing the subjective rating test, the optimal y, z direction feedback force gains were determined. The optimal gains were verified by thyroid phantom scanning test and the scanned ultrasound image analysis.

Conclusion

With the proposed method, the y, z direction optimal feedback force gains of the master–slave type ultrasound scanning robots can be determined. The proposed methods were verified by thyroid phantom scanning test.

Keywords

Ultrasound scanning robot Master–slave system Genetic algorithm (GA) Feedback force gain Thyroid scanning procedure 

Notes

Acknowledgements

This research was supported by the National Cancer Center NCC1610050-1.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Ethical approval

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. This article does not contain any studies with animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. 1.
    Eibenberger KL, Dock WI, Ammann ME, Dorffner R, Hrmann MF, Grabenwger F (1994) Quantification of pleural effusions: sonography versus radiography. Radiology.  https://doi.org/10.1148/radiology.191.3.8184046 CrossRefPubMedGoogle Scholar
  2. 2.
    Salonen JT, Salonen R (1993) Ultrasound B-mode imaging in observational studies of atherosclerotic progression. Circulation 87:II56–65PubMedGoogle Scholar
  3. 3.
    Pierrot F, Dombre E, Dgoulange E, Urbain L, Caron P, Boudet S, Garipy J, Mgnien JL (1999) Hippocrate: a safe robot arm for medical applications with force feedback. Med Image Anal.  https://doi.org/10.1016/S1361-8415(99)80025-5 CrossRefPubMedGoogle Scholar
  4. 4.
    Abolmaesumi P, Salcudean SE, Zhu WH, Sirouspour MR, DiMaio SP (2002) Image-guided control of a robot for medical ultrasound. IEEE Trans Robot Autom 18(1):11–23CrossRefGoogle Scholar
  5. 5.
    Salcudean SE, Zhu WH, Abolmaesume P, Bachmann S (1999) A robot system for medical ultrasound. In: 9th international symposium of robotics research (ISRR 99), pp 152–159Google Scholar
  6. 6.
    Vilchis A, Troccaz J, Cinquin P, Masuda K, Pellissier F (2003) A new robot architecture for tele-echography. IEEE Trans Robot Autom.  https://doi.org/10.1109/TRA.2003.817509 CrossRefGoogle Scholar
  7. 7.
    Koizumi N, Tsurumi T, Kato T, Warisawa S, Nagoshi M, Hashizume H, Mitsuishi M (2014) Remote ultrasound diagnostic system (RUDS). J Robot Mechatron.  https://doi.org/10.1002/chp.20084 CrossRefGoogle Scholar
  8. 8.
    Koizumi N, Warisawa S, Mitsuishi M, Hashizume H (2006) Automatic control switching according to diagnostic tasks for a remote ultrasound diagnostic system. In: Proceedings of the first IEEE/RAS-EMBS international conference on biomedical robotics and biomechatronics, 2006, BioRob 2006Google Scholar
  9. 9.
    Aoki Y, Kaneko K, Sakai T, Masuda K (2010) A study of scanning the ultrasound probe on body surface and construction of visual servo system based on echogram. J Robot Mechatron 22(3):273–279CrossRefGoogle Scholar
  10. 10.
    Masuda K, Takachi Y, Urayama Y, Yoshinaga T (2011) Development of support system to handle ultrasound probe by coordinated motion with medical robot. In: Proceedings of the annual international conference of the IEEE Engineering in Medicine and Biology Society, EMBSGoogle Scholar
  11. 11.
    Onogi S, Urayama Y, Irisawa S, Masuda K (2013) Robotic ultrasound probe handling auxiliary by active compliance control. Adv Robot.  https://doi.org/10.1080/01691864.2013.776941 CrossRefGoogle Scholar
  12. 12.
    Santos L, Cortesao R (2012) Joint space torque control with task space posture reference for robotic-assisted tele-echography. In: Proceedings—IEEE international workshop on robot and human interactive communicationGoogle Scholar
  13. 13.
    Santos L, Corteso R (2013) Admittance control for robotic-assisted tele-echography. In: 2013 16th international conference on advanced robotics, ICAR 2013Google Scholar
  14. 14.
    Santos L, Corteso R (2015) A dynamically consistent hierarchical control architecture for robotic-assisted tele-echography with motion and contact dynamics driven by a 3D time-of-flight camera and a force sensor. In: Proceedings—IEEE international conference on robotics and automationGoogle Scholar
  15. 15.
    Vieyres P, Josserand L, Chiccoli M, Sandoval J, Morette N, Novales C, Fonte A, Avgousti S, Voskarides S, Kasparis T (2012) A predictive control approach and interactive GUI to enhance distal environment rendering during robotized tele-echography: interactive platform for robotized telechography. In: IEEE 12th international conference on bioinformatics and bioengineering, BIBE 2012Google Scholar
  16. 16.
    Conti F, Park J, Khatib O (2014) Interface design and control strategies for a robot assisted ultrasonic examination system. Exp Robot.  https://doi.org/10.1007/978-3-642-28572-1 CrossRefGoogle Scholar
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
  22. 22.
  23. 23.
    Haddadi A, Hashtrudi-Zaad K (2012) Real-time identification of Hunt–Crossley dynamic models of contact environments. IEEE Trans Robot.  https://doi.org/10.1109/TRO.2012.2183054 CrossRefGoogle Scholar
  24. 24.
    Li TH (2008) On exponentially weighted recursive least squares for estimating time-varying parameters and its application to computer workload forecasting. J Stat Theory Pract.  https://doi.org/10.1080/15598608.2008.10411879 CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Dan S (2013) Evolutionary optimization algorithms. Wiley, New YorkGoogle Scholar
  27. 27.
    Carlos CC, Gary BL (2007) Evolutionary algorithms for solving multi-objective problems (genetic and evolutionary computation). Springer, BerlinGoogle Scholar
  28. 28.
    Alain P, Sana B-H (2017) Evolutionary algorithms (computer engineering: metaheuristics). Wiley-ISTE, New YorkGoogle Scholar
  29. 29.
  30. 30.
    David EG (1989) Genetic algorithms in search, optimization, and machine learning, 1st edn. Addison-Wesley Professional, ReadingGoogle Scholar
  31. 31.
    Mitchell M (1998) An introduction to genetic algorithms (complex adaptive systems), Reprint edn. MIT Press, CambridgeGoogle Scholar
  32. 32.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  33. 33.
    Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ (1998) Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. J Comput Chem. https://doi.org/10.1002/(SICI)1096-987X(19981115)19:14\(<\)1639::AID-JCC10\(>\)3.0.CO;2-BGoogle Scholar
  34. 34.
    Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Probl Solving Nat PPSN VI.  https://doi.org/10.1007/3-540-45356-3_83 CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Biomedical Engineering Branch, Division of Precision Medicine and Cancer InformaticsNational Cancer CenterGoyang-siKorea
  2. 2.Department of Ship and OceanVision University of JeonjuJeonjuKorea
  3. 3.Department of Biomedical Engineering, Gil Medical Center, School of MedicineGachon UniversityIncheonKorea

Personalised recommendations