A clinically applicable laser-based image-guided system for laparoscopic liver procedures

  • Matteo Fusaglia
  • Hanspeter Hess
  • Marius Schwalbe
  • Matthias Peterhans
  • Pascale Tinguely
  • Stefan Weber
  • Huanxiang Lu
Original Article

Abstract

Purpose

Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario.

Methods

In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process.

Results

Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of \(3.2\pm 0.57\,\hbox {mm}\).

Conclusions

We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

Keywords

Image-guided surgery Laparoscopic liver surgery Laser range scanner Surface registration 

References

  1. 1.
    Sjølie E, Langø T, Ystgaard B, Tangen G, Nagelhus Hernes T, Mårvik R (2003) 3D ultrasound-based navigation for radiofrequency thermal ablation in the treatment of liver malignancies. Surg Endosc 17(6):933–938CrossRefPubMedGoogle Scholar
  2. 2.
    Hill DL, Batchelor P (2001) Registration methodology: concepts and algorithms, Biomedical engineering. In: Hajnal JV, Hill D, Hawkes D (eds) Medical Image Registration. CRC Press, London, pp 39–70Google Scholar
  3. 3.
    Bao P, Warmath J, Galloway R Jr, Herline A (2005) Ultrasound-to-computer-tomography registration for image-guided laparoscopic liver surgery. Surg Endosc Other Interv Tech 19(3):424–429CrossRefGoogle Scholar
  4. 4.
    Herline AJ, Herring JL, Stefansic JD, Chapman WC, Galloway RL, Dawant BM (2000) Surface registration for use in interactive, image-guided liver surgery. Comput Aided Surg 5(1):11–17PubMedGoogle Scholar
  5. 5.
    Lange T, Eulenstein S, Hünerbein M, Schlag P-M (2003) Vessel-based non-rigid registration of MR/CT and 3D ultrasound for navigation in liver surgery. Comput Aided Surg 8(5):228–240CrossRefPubMedGoogle Scholar
  6. 6.
    Lathrop RA, Hackworth DM, Webster RJ (2010) Minimally invasive holographic surface scanning for soft-tissue image registration. Biomed Eng IEEE Trans 57(6):1497–1506CrossRefGoogle Scholar
  7. 7.
    Audette MA, Siddiqi K, Ferrie FP, Peters TM (2003) An integrated range-sensing, segmentation and registration framework for the characterization of intra-surgical brain deformations in image-guided surgery. Comput Vis Image Underst 89(2):226–251CrossRefGoogle Scholar
  8. 8.
    Schlaier J, Warnat J, Brawanski A (2002) Registration accuracy and practicability of laser-directed surface matching. Comput Aided Surg 7(5):284–290CrossRefPubMedGoogle Scholar
  9. 9.
    Mcdonald CP, Brownhill JR, King GJ, Johnson JA, Peters TM (2007) A comparison of registration techniques for computer-and image-assisted elbow surgery. Comput Aided Surg 12(4):208–214CrossRefPubMedGoogle Scholar
  10. 10.
    Joskowicz L, Shamir R, Freiman M, Shoham M, Zehavi E, Umansky F, Shoshan Y (2006) Image-guided system with miniature robot for precise positioning and targeting in keyhole neurosurgery. Comput Aided Surg 11(4):181–193CrossRefPubMedGoogle Scholar
  11. 11.
    Cash DM, Sinha TK, Chapman WC, Terawaki H, Dawant BM, Galloway RL, Miga MI (2003) Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking. Med Phys 30(7):1671–1682CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Dumpuri P, Clements LW, Dawant BM, Miga MI (2010) Model-updated image-guided liver surgery: preliminary results using surface characterization. Prog Biophys Mol Biol 103(2–3):197–207. doi:10.1016/j.pbiomolbio.2010.09.014 CrossRefPubMedGoogle Scholar
  13. 13.
    Hayashibe M, Suzuki N, Nakamura Y (2006) Laser-scan endoscope system for intraoperative geometry acquisition and surgical robot safety management. Med Image Anal 10(4):509–519CrossRefPubMedGoogle Scholar
  14. 14.
    Hayashibe M, Suzuki N, Hattori A, Nakamura Y (2002) Intraoperative fast 3D shape recovery of abdominal organs in laparoscopy. In: Medical image computing and computer-assisted intervention—MICCAI 2002. Springer, pp 356–363Google Scholar
  15. 15.
    Friets E, Bieszczad J, Kynor D, Norris J, Davis B, Allen L, Chambers R, Wolf J, Glisson C, Herrell SD, Galloway RL (2013) Endoscopic laser range scanner for minimally invasive, image guided kidney surgery. In: SPIE medical imaging. International society for optics and photonics, pp 867105–867108Google Scholar
  16. 16.
    Hess-Flores M, Recker S, Joy K (2014) Uncertainty, baseline, and noise analysis for L1 error-based multi-view triangulation. In: Pattern recognition (ICPR), 2014 22nd international conference on. IEEE, pp 4074–4079Google Scholar
  17. 17.
    Beraldin J-A (2004) Integration of laser scanning and close-range photogrammetry—the last decade and beyond. In: Proceedings of the 20th congress international society for photogrammetry and remote sensing. Istanbul, Turkey, pp 972–983Google Scholar
  18. 18.
    Zhang Z (2000) A flexible new technique for camera calibration. Pattern Anal Mach Intell IEEE Trans 22(11):1330–1334CrossRefGoogle Scholar
  19. 19.
    Besl PJ,McKay ND (1992) Method for registration of 3-D shapes. In: Proceedings of the SPIE 1611, sensor fusion IV: control paradigms and data structures, 586 (April 30, 1992). doi:10.1117/12.57955
  20. 20.
    Arun KS, Huang TS, Blostein SD (1987) Least-squares fitting of two 3-D point sets. Pattern Anal Mach Intell IEEE Trans 5:698–700CrossRefGoogle Scholar
  21. 21.
    Mahnken AH, Ricke J (2009) CT-and MR-guided Interventions in Radiology, vol 22. Springer, New YorkCrossRefGoogle Scholar
  22. 22.
    Rohlfing T, Maurer CR Jr, O’Dell WG, Zhong J (2004) Modeling liver motion and deformation during the respiratory cycle using intensity-based nonrigid registration of gated MR images. Med Phys 31(3):427–432CrossRefPubMedGoogle Scholar
  23. 23.
    Biro P, Spahn D, Pfammatter T (2009) High-frequency jet ventilation for minimizing breathing-related liver motion during percutaneous radiofrequency ablation of multiple hepatic tumours. Br J Anaesth 102(5):650–653. doi:10.1093/bja/aep051 CrossRefPubMedGoogle Scholar
  24. 24.
    Warner M, Warner M, Buck C, Segura J (1988) Clinical efficacy of high frequency jet ventilation during extracorporeal shock wave lithotripsy of renal and ureteral calculi: a comparison with conventional mechanical ventilation. J Urol 139(3):486–487PubMedGoogle Scholar
  25. 25.
    Herline AJ, Stefansic JD, Debelak JP, Hartmann SL, Pinson CW, Galloway RL, Chapman WC (1999) Image-guided surgery: preliminary feasibility studies of frameless stereotactic liver surgery. Arch Surg 134(6):644–650. doi:10.1001/archsurg.134.6.644 CrossRefPubMedGoogle Scholar
  26. 26.
    Zijlmans M, Langø T, Hofstad EF, Van Swol CF, Rethy A (2012) Navigated laparoscopy-liver shift and deformation due to pneumoperitoneum in an animal model. Minim Invasive Ther Allied Technol 21(3):241–248CrossRefPubMedGoogle Scholar
  27. 27.
    Heizmann O, Zidowitz S, Bourquain H, Potthast S, Peitgen H-O, Oertli D, Kettelhack C (2010) Assessment of intraoperative liver deformation during hepatic resection: prospective clinical study. World J Surg 34(8):1887–1893CrossRefPubMedGoogle Scholar
  28. 28.
    Blackall JM, King AP, Penney GP, Adam A, Hawkes DJ (2001) A statistical model of respiratory motion and deformation of the liver. In: Medical image computing and computer-assisted intervention—MICCAI 2001. Springer, pp 1338–1340Google Scholar
  29. 29.
    Schnabel JA, Rueckert D, Quist M, Blackall JM, Castellano-Smith AD, Hartkens T, Penney GP, Hall WA, Liu H, Truwit CL, Gerritsen FA, Hill DLG, Hawkes DJ (2001) A generic framework for non-rigid registration based on non-uniform multi-level free-form deformations. In: Medical image computing and computer-assisted intervention—MICCAI 2001. Springer, pp 573–581Google Scholar
  30. 30.
    Masutani Y, Kimura F (2001) Modally controlled free form deformation for non-rigid registration in image-guided liver surgery. In: Medical image computing and computer-assisted intervention—MICCAI 2001. Springer, pp 1275–1278Google Scholar
  31. 31.
    Li H, Sumner RW, Pauly M (2008) Global correspondence optimization for non-rigid registration of depth scans. Comput Graph Forum 27:1421–1430. doi:10.1111/j.1467-8659.2008.01282.x
  32. 32.
    Sinha TK, Dawant BM, Duay V, Cash DM, Weil RJ, Thompson RC, Weaver KD, Miga MI (2005) A method to track cortical surface deformations using a laser range scanner. Med Imaging IEEE Trans 24(6):767–781CrossRefGoogle Scholar
  33. 33.
    Simpson AL, Burgner J, Glisson CL, Herrell SD, Ma B, Pheiffer TS, Webster RJ, Miga M (2013) Comparison study of intraoperative surface acquisition methods for surgical navigation. Biomed Eng IEEE Trans 60(4):1090–1099CrossRefGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Matteo Fusaglia
    • 1
  • Hanspeter Hess
    • 1
  • Marius Schwalbe
    • 1
  • Matthias Peterhans
    • 1
  • Pascale Tinguely
    • 2
  • Stefan Weber
    • 1
  • Huanxiang Lu
    • 1
  1. 1.Artorg Center for Biomedical Engineering Research, IGTUniversity of BernBernSwitzerland
  2. 2.Department of Visceral SurgeryUniversity Hospital of BernBernSwitzerland

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