IJAT Vol.11 No.3 pp. 450-458
doi: 10.20965/ijat.2017.p0450


Using Navi-Robot and a CT Scanner to Guide Biopsy Needles

Mario Donnici, Giorgia Lupinacci, Paola Nudo, Michele Perrelli, and Guido Danieli

Dipartimento di Ingegneria Meccaìnica, Energetica e Gestionale (DIMEG), Università della Calabria
Via Ponte Bucci Cubo 46C, 87036 Rence (CS), Italy

Corresponding author

August 4, 2016
October 25, 2016
Online released:
April 28, 2017
May 5, 2017
surgical robots, biopsy needle insertion, CT images guided biopsies
Purpose of this study was to control the suitability of Navi-Robot, a robotic system developed by our research group, to guide percutaneous needle placement under computed tomography (CT) in order to achieve lower radiation exposure and a shorter procedure. The system consists of a high precision six-degrees-of-freedom self-balanced arm, able to move both in passive and active modes, which allows the physician an accurate needle-insertion. The target and the needle entry points are selected by the surgeon on a desktop computer, that acquires DICOM images from the CT scan, and that, using software developed for this purpose, detects also the position of at least three radio opaque markers placed on the patient or on the stretcher. Once these data are obtained, a new system of reference is established based on the markers position, obtaining the coordinates of target and entry point in the new frame of reference. Going then to touch the tip of the spheres with the tip of the robot end effector in passive mode, and recording their position, the robot learns where the two points of interest are located in its frame of reference. A first test was performed on a Plexiglas board; the accuracy achieved was measured as the distance between the needle tip and the target. The results of the in vitro experiment showed that the system is able to reach the target with an accuracy of 1.2 mm.
Cite this article as:
M. Donnici, G. Lupinacci, P. Nudo, M. Perrelli, and G. Danieli, “Using Navi-Robot and a CT Scanner to Guide Biopsy Needles,” Int. J. Automation Technol., Vol.11 No.3, pp. 450-458, 2017.
Data files:
  1. [1] B. Butler and K. A. Poelstra, “Risks of Excessive Intraoperative Radiation,” Seminars in Spine Surgery, Vol.20, No.3, pp. 175-180, 2008.
  2. [2] N. Buls, J. Pagés, J. de Mey, and M. Osteaux, “Evaluation of patient and staff doses during various CT fluoroscopy guided interventions,” Health Phys., Vol.85, No.2, pp. 165-173, 2003.
  3. [3] L. Krille, G. P. Hammera, and H. Merzenicha, “Systematic review on physician’s knowledge about radiation doses and radiation,” European J. of Radiology, pp. 36-41, 2010.
  4. [4] J. L. Hefti, M. Epitaux, D. Glauser, and H. Fankhauser, “Robotic three-dimensional positioning of a stimulation electrode in the brain,” Comput Aided Surg., Vol.3, No.1, pp. 1-10, 1998.
  5. [5] M. H. Loser and N. Navab, “A new robotic system for visually controlled percutaneous interventions under CT fluoroscopy,” Pittsburgh, PA, USA, Scott L. Delp, Anthony M. DiGoia, Branislav Jaramaz, pp. 887-896, 2000.
  6. [6] D. Stoianovici et al., “AcuBot: A Robot for Radiological Interventions,” IEEE Trans. on Robotics and Automation, Vol.19, No.5, pp. 927-930, 2003.
  7. [7] R. Pollock, P. Mozer, T. J. Guzzo, J. Marx, B. Matlaga, D. Petrisor, B. Vigaru, S. Badaan, D. Stoianovici, and M. E. Allaf, “Prospects in Percutaneous Ablative Targeting: Comparison of a Computer-Assisted Navigation System and the AcuBot Robotic System,” J. of Endourology, Vol.24, No.8, pp. 1269-1272, 2010.
  8. [8] Y. Koethe, S. Xu, G. Velusamy, B. J. Wood, and A. M. Venkatesan, “Accuracy and efficacy of percutaneous biopsy and ablation using robotic assistance under computed tomography guidance: a phantom study,” Eur Radiol, Vol.24, pp. 723-730, 2014.
  9. [9] F. Roser, M. Tatagiba, and G. Maier, “Spinal robotics: current Applications and Future Perspectives,” Neurosurgery, Vol.72, pp. A12-A18, 2013.
  10. [10] R. Seifabadi, S. Song, A. Krieger, N. B. Cho, J. Tokuda, G. Fichtinger, and I. Iordachita, “Robotic system for MRI-guided prostate biopsy: feasibility of teleoperated needle insertion and ex vivo phantom study,” Int. J. CARS, Vol.7, pp. 181-190, 2012.
  11. [11] G. Danieli, “Measuring open kinematic chain able to turn into a positioning robot,” European Patent, EP1843876, England, France, Germany, Italy, 2009.
  12. [12] M. Perrelli, P. Nudo, M. Donnici, G. Gatti, F. M. Colacino, C. Pace, and G. Danieli, “Navi – Robot, a multipurpose robot for medical application,” Problem of Mechanics, No.4, 41, pp. 22-33, 2010.
  13. [13] H. Makino, “Development of the SCARA,” J. of Robotics and Mechatronics, Vol.26, No.1, pp. 5-8, 2014.
  14. [14] D. Moschella, G. Gatti, E. Vitelli, A. Lecce, M. Perrelli, C. Pace, and G. Danieli “Experimental Validation of a Special Locking Drum Brake for Robotic Applications,” EDSA2008-59305, Congress in Haifa, July 7-9, 2008.
  15. [15] J. Denavit and R. S. Hartenberg, “A kinematic notation for lower-pair mechanism based on matrices,” ASME J. Appl. Mechan., Vol.77, pp. 215-221, 1955.
  16. [16] L. Sciavicco and B. Siciliano, “Modelling and Control of Robot Manipulators,” Advanced textbooks in Control and Signal Processing, 2nd Edition, Springer, 2012.
  17. [17] T. H. Davies, “Kirchhoff’s circulation law applied to multi-loop kinematic chains,” Mechanism and Machine Theory, Vol.16, pp. 171-183, 1981.
  18. [18] T. H. Davies, “Mechanical networks – I: Passivity and redundancy,” Mechanism and Machine Theory, Vol.18, No.2, pp. 95-101, 1983.
  19. [19] G. Gatti and G. Danieli, “Validation of a calibration technique for 6-DOF instrumented spatial linkages,” J. of Biomechanics, Vol.40, No.7, pp. 1455-1466, 2006.
  20. [20] S. Aoyagi, M. Suzuki, T. Takebashi, J. Fujioka, and Y. Kamiya, “Calibration of Kinematic Parameters of Robot Arm Using Laser Tracking System: Compensation for Non-Geometric Errors by Neural Networks and Selection of Optimal Measuring Points by Genetic Algorithm,” Int. J. of Automation Technology, Vol.6, No.1, pp. 29-37, 2012.
  21. [21] “ISO 9283 Manipulating Industrial Robots – Performance criteria and Related Test Methods,” Int. Standards Organization, 1998.
  22. [22] G. Danieli, “Robot riabilitativo auto-bilanciato in grado di lavorare sia in modo attivo che passivo sia su arti superiori che inferiori, sia destri che sinistri,” Domanda di Brevetto CS2015A000001, depositata 18.01.2015.
  23. [23] G. Danieli, G. Lupinacci, P. Nudo, and V. Loiero, “Design and simulation of a self – balanced rehabilitation Robot able to work in active and passive modes on both sides of upper and lower limbs,” 6th Int. Conf. on Bioscience and Bioinformatics (ICBB ’15), paper 72209-124, 2015.
  24. [24] M. Donnici, S. Meduri, M. Perrelli, D. Battaglia, G. Gatti, C. Pace, and G. Danieli, “Stochastic Deterministic Calibration of a Self Balanced Hybrid Parallel/Serial Robotic Structure,” Problems of Mechanics, No.2, 54, pp. 45-56, 2014.

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