On the feasibility of transperineal 3D ultrasound image guidance for robotic radical prostatectomy

  • Prateek MathurEmail author
  • Golnoosh Samei
  • Keith Tsang
  • Julio Lobo
  • Septimiu Salcudean
Original Article



Prostate cancer is the most prevalent form of male-specific cancers. Robot-assisted laparoscopic radical prostatectomy (RALRP) using the da Vinci surgical robot has become the gold-standard treatment for organ-confined prostate cancer. To improve intraoperative visualization of anatomical structures, many groups have developed techniques integrating transrectal ultrasound (TRUS) into the surgical workflow. TRUS, however, is intrusive and does not provide real-time volumetric imaging.


We propose a proof-of-concept system offering an alternative noninvasive transperineal view of the prostate and surrounding structures using 3D ultrasound (US), allowing for full-volume imaging in any anatomical plane desired. The system aims to automatically track da Vinci surgical instruments and display a real-time US image registered to preoperative MRI. We evaluate the approach using a custom prostate phantom, an iU22 (Philips Healthcare, Bothell, WA) US machine with an xMATRIX X6-1 transducer, and a custom probe fixture. A novel registration method between the da Vinci kinematic frame and 3D US is presented. To evaluate the entire registration pipeline, we use a previously developed MRI to US deformable registration algorithm.


Our US calibration technique yielded a registration error of 0.84 mm, compared to 1.76 mm with existing methods. We evaluated overall system error with a prostate phantom, achieving a target registration error of 2.55 mm.


Transperineal imaging using 3D US is a promising approach for image guidance during RALRP. Preliminary results suggest this system is comparable to existing guidance systems using TRUS. With further development and testing, we believe our system has the potential to improve patient outcomes by imaging anatomical structures and prostate cancer in real time.


Image-guided surgery Surgical robotics Prostatectomy 3D ultrasound Medical image registration 



We would like to acknowledge the Canadian Institutes of Health Research (CIHR) and the Charles A. Laszlo Chair in Biomedical Engineering held by Dr. Septimiu Salcudean for their financial support.


Funding was provided by Canadian Institutes of Health Research (Grant No. MOP-142439).

Compliance with ethical standards

Conflict of interest

The authors declare that they have 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.

Informed consent

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


  1. 1.
    Adebar T, Salcudean S, Mahdavi S, Moradi M, Nguan C, Goldenberg L (2011) A robotic system for intra-operative trans-rectal ultrasound and ultrasound elastography in radical prostatectomy. In: Taylor RH, Yang GZ (eds) Information processing in computer-assisted interventions. Springer, Berlin, pp 79–89CrossRefGoogle Scholar
  2. 2.
    Camps SM, Fontanarosa D, De With PH, Verhaegen F, Vanneste BG (2018) The use of ultrasound imaging in the external beam radiotherapy workflow of prostate cancer patients. BioMed Res Int. Google Scholar
  3. 3.
    De Carlo F, Celestino F, Verri C, Masedu F, Liberati E, Di Stasi SM (2014) Retropubic, laparoscopic, and robot-assisted radical prostatectomy: surgical, oncological, and functional outcomes—a systematic review francesco. Urol Int 93(4):373–383. CrossRefGoogle Scholar
  4. 4.
    De Rooij M, Hamoen EHJ, Fütterer JJ, Barentsz JO, Rovers MM (2014) Accuracy of multiparametric MRI for prostate cancer detection: a meta-analysis. Am J Roentgenol 202(2):343–351. CrossRefGoogle Scholar
  5. 5.
    DiMaio S, Hasser C (2008) The da Vinci research interface. MIDAS J.
  6. 6.
    Echeverría R, Cortes C, Bertelsen A, Macia I, Ruiz ÓE, Flórez J (2016) Robust CT to US 3D–3D registration by using principal component analysis and Kalman filtering. In: Vrtovec T, Yao J, Glocker B, Klinder T, Frangi A, Zheng G, Li S (eds) Computational methods and clinical applications for spine imaging. Springer, Cham, pp 52–63CrossRefGoogle Scholar
  7. 7.
    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. CrossRefGoogle Scholar
  8. 8.
    Fornage BD (1986) Normal US anatomy of the prostate. Ultrasound Med Biol 12(12):1011–1021. CrossRefGoogle Scholar
  9. 9.
    Fütterer JJ, Briganti A, De Visschere P, Emberton M, Giannarini G, Kirkham A, Taneja SS, Thoeny H, Villeirs G, Villers A (2015) Can clinically significant prostate cancer be detected with multiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol 68(6):1045–1053. CrossRefGoogle Scholar
  10. 10.
    Goksel O, Salcudean SE (2009) B-mode ultrasound image simulation in deformable 3-D medium. IEEE Trans Med Imaging 28(11):1657–1669CrossRefGoogle Scholar
  11. 11.
    Griffiths KA, Ly LP, Jin B, Chan L, Handelsman DJ (2007) Transperineal ultrasound for measurement of prostate volume: validation against transrectal ultrasound. J Urol 178(4):1375–1380. CrossRefGoogle Scholar
  12. 12.
    Hunter C, Cesante M, Xu S, Wood BJ, Seifabadi R (2017) Sensor-less fully transperineal fusion-guided prostate biopsy. In: 2017 design of medical devices conference, Minneapolis, Minnesota.
  13. 13.
    Li H, Hartley R (2007) The 3D–3D registration problem revisited. In: 2007 IEEE 11th international conference on computer vision, pp 1–8.
  14. 14.
    Mohareri O, Ischia J, Black PC, Schneider C, Lobo J, Goldenberg L, Salcudean SE (2015) Intraoperative registered transrectal ultrasound guidance for robot-assisted laparoscopic radical prostatectomy. J Urol 193(1):302–312. CrossRefGoogle Scholar
  15. 15.
    Mohareri O, Nir G, Lobo J, Savdie R, Black P, Salcudean SA (2015) System for MR–ultrasound guidance during robot-assisted laparoscopic radical prostatectomy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 9349, pp 497–504. arXiv:1602.07360
  16. 16.
    Mohareri O, Schneider C, Adebar TK, Yip MC, Black P, Nguan CY, Bergman D, Seroger J, DiMaio S, Salcudean SE (2013) Ultrasound-based image guidance for robot-assisted laparoscopic radical prostatectomy: initial in-vivo results. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7915 LNCS, pp 40–50.
  17. 17.
    O’Shea TP, Garcia LJ, Rosser KE, Harris EJ, Evans PM, Bamber JC (2014) 4D ultrasound speckle tracking of intra-fraction prostate motion: a phantom-based comparison with X-ray fiducial tracking using CyberKnife. Phys Med Biol 59(7):1701–1720. CrossRefGoogle Scholar
  18. 18.
    Samei G, Goksel O, Lobo J, Mohareri O, Black P, Rohling R, Salcudean S (2018) Real-time FEM-based registration of 3-D to 2.5-D transrectal ultrasound images. IEEE Trans Med Imaging 37(8):1877–1886. CrossRefGoogle Scholar
  19. 19.
    Samei G, Tsang K, Lobo J, Kesch C, Chang S, Black P, Salcudean S (2018) Fused MRI-ultrasound augmented-reality guidance system for robot-assisted laparoscopic radical prostatectomy. In: Hamlyn symposium on medical robotics, pp 79–80. The Hamlyn Centre, Imperial College London, LondonGoogle Scholar
  20. 20.
    Ukimura O, Gill IS (2006) Real-time transrectal ultrasound guidance during nerve sparing laparoscopic radical prostatectomy: pictorial essay. J Urol 175(4):1311–1319. CrossRefGoogle Scholar
  21. 21.
    Ukimura O, Gill IS, Desai MM, Steinberg AP, Kilciler M, Ng CS, Abreu SC, Spaliviero M, Ramani AP, Kaouk JH, Kawauchi A, Miki T (2004) Real-time transrectal ultrasonography during laparoscopic radical prostatectomy. J Urol 172(1):112–118. CrossRefGoogle Scholar
  22. 22.
    U.S. Cancer Statistics Working Group: U.S. Cancer Statistics Data Visualizations Tool, based on November 2017 submission data (1999–2015) (2018). Accessed 14 Oct 2018
  23. 23.
    Yang J, Li H, Jia Y (2013) GO-ICP: solving 3D registration efficiently and globally optimally. In: 2013 IEEE international conference on computer vision, pp 1457–1464.
  24. 24.
    Yu AS, Najafi M, Hristov DH, Phillips T (2017) Intrafractional tracking accuracy of a transperineal ultrasound image guidance system for prostate radiotherapy. Technol Cancer Res Treat 16(6):1067–1078. CrossRefGoogle Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of British ColumbiaVancouverCanada

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