Dual-robot ultrasound-guided needle placement: closing the planning-imaging-action loop
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Precise needle placement is an important task during several medical procedures. Ultrasound imaging is often used to guide the needle toward the target region in soft tissue. This task remains challenging due to the user’s dependence on image quality, limited field of view, moving target, and moving needle. In this paper, we present a novel dual-robot framework for robotic needle insertions under robotic ultrasound guidance.
We integrated force-controlled ultrasound image acquisition, registration of preoperative and intraoperative images, vision-based robot control, and target localization, in combination with a novel needle tracking algorithm. The framework allows robotic needle insertion to target a preoperatively defined region of interest while enabling real-time visualization and adaptive trajectory planning to provide safe and quick interactions. We assessed the framework by considering both static and moving targets embedded in water and tissue-mimicking gelatin.
The presented dual-robot tracking algorithms allow for accurate needle placement, namely to target the region of interest with an error around 1 mm.
To the best of our knowledge, we show the first use of two independent robots, one for imaging, the other for needle insertion, that are simultaneously controlled using image processing algorithms. Experimental results show the feasibility and demonstrate the accuracy and robustness of the process.
KeywordsRobotic system and software Software architecture Robotics Robotic architecture and devices Ultrasound Instrument and patient localization and tracking
Supplementary material 1 (mp4 12286 KB)
- 3.Yılmaz S, Özdoğan M, Cevener M, Ozluk A, Kargi A, Kendiroglu F, Ogretmen I, Yildiz A (2016) Use of cryoablation beyond the prostate, Insights into imaging, pp. 1–10Google Scholar
- 5.Esposito M, Busam B, Hennersperger C, Rackerseder J, Lu A, Navab N, Frisch B (2015) Cooperative robotic gamma imaging: enhancing us-guided needle biopsy, in Medical Image Computing and Computer-Assisted Intervention-MICCAI. Springer, BerlinGoogle Scholar
- 6.Graumann C, Fuerst B, Hennersperger C, Bork F, Navab N (2016) Robotic ultrasound trajectory planning for volume of interest coverage. In: IEEE international conference on robotics and automation (ICRA)Google Scholar
- 7.Zettinig O, Fuerst B, Kojcev R, Esposito M, Salehi M, Wein W, Rackerseder J, Sinibaldi E, Frisch B, Navab N (2016) Toward real-time 3d ultrasound registration-based visual servoing for interventional navigation. In: IEEE international conference on robotics and automation (ICRA)Google Scholar
- 9.Wei Z, Ding M, Downey D, Fenster A (2005) 3d trus guided robot assisted prostate brachytherapy. In Medical image computing and computer-assisted intervention-MICCAI. Springer, Berlin, pp 17–24Google Scholar
- 11.Alterovitz R, Goldberg K, Okamura A (2005) Planning for steerable bevel-tip needle insertion through 2d soft tissue with obstacles, in Robotics and Automation, 2005. Proceedings of the IEEE international conference on IEEE, pp. 1640–1645Google Scholar
- 18.Karamalis A, Wein W, Kutter O, Navab N (2009) Fast hybrid freehand ultrasound volume reconstruction. In: SPIE medical imaging. International society for optics and photonics, pp 726 114–726 114Google Scholar
- 19.Albu-Schäffer A, Ott C, Frese U, Hirzinger G (2003) Cartesian impedance control of redundant robots: Recent results with the DLR-Light-Weight-arms. In Proceedings on international conference on robotics and automation, vol. 3. IEEE, 2003, pp. 3704–3709Google Scholar
- 20.Wein W, Ladikos A, Fuerst B, Shah A, Sharma K, Navab N (2013) Global registration of ultrasound to mri using the lc2 metric for enabling neurosurgical guidance. In: Medical image computing and computer-assisted intervention–MICCAI. Springer, Berlin, pp. 34–41Google Scholar
- 22.Powell MJ (2009) The BOBYQA algorithm for bound constrained optimization without derivativesGoogle Scholar
- 23.Chatelain P, Krupa A, Marchal M (2013) Real-time needle detection and tracking using a visually servoed 3d ultrasound probe. In Robotics and Automation (ICRA), 2013 IEEE International Conference on IEEE, pp 1676–1681Google Scholar
- 24.Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27Google Scholar
- 25.Uherčík M, Liebgott H, Kybic J, Cachard C (2009) Needle localization methods in 3D ultrasound data. In International congress on ultrasonics, pp. 11–17Google Scholar
- 26.Julier s, Uhlmann J (2004) Unscented filtering and nonlinear estimation. In Proceedings of the IEEE, vol 92, no 3, pp. 401–422Google Scholar
- 27.Bischoff R, Kurth J, Schreiber G, Koeppe R, Albu-Schaeffer A, Beyer A, Eiberger O, Haddadin S, Stemmer A, Grunwald G, Hirzinger G (2010) The kuka-dlr lightweight robot arm—a new reference platform for robotics research and manufacturing, In Robotics (ISR), 2010 41st International Symposium on and 2010 6th German Conference on Robotics (ROBOTIK), June 2010, pp. 1–8Google Scholar
- 28.Schreiber G, Stemmer A, Bischoff R (2010) The fast research interface for the KUKA lightweight robot. In: IEEE workshop on innovative robot control architectures for demanding applications how to modify and enhance commercial controllersGoogle Scholar