Journal of Gastrointestinal Surgery

, Volume 17, Issue 7, pp 1274–1282

Evolution of Image-Guided Liver Surgery: Transition from Open to Laparoscopic Procedures

  • T. Peter Kingham
  • Shiva Jayaraman
  • Logan W. Clements
  • Michael A. Scherer
  • James D. Stefansic
  • William R. Jarnagin
Original Article



Indications for liver surgery to treat primary and secondary hepatic malignancies are broadening. Utilizing data from B-mode or 2-D intraoperative ultrasound, it is often challenging to replicate the findings from preoperative CT or MRI scans. Additional data from more recently developed image-guidance technology, which registers preoperative axial imaging to a 3-D real-time model, may be used to improve operative planning, locate difficult to find hepatic tumors, and guide ablations.


Laparoscopic liver procedures are often more challenging than their open counterparts. Image-guidance technology can assist in overcoming some of the obstacles to minimally invasive liver procedures by enhancing ultrasound findings and ablation guidance. This manuscript describes a protocol that evaluated an open image-guidance system, and a subsequent protocol that directly compared, for validation, a laparoscopic with an open image-guidance system. Both protocols were limited to ablations within the liver.


The laparoscopic image-guidance system successfully creates a 3-D model at both 7 and 14 mm Hg that is similar to the open 3-D model. Ultimately, improving intraoperative image guidance can help expand the ability to perform both laparoscopic and open liver surgeries.


Image-guided surgery Liver surgery Minimally invasive 


  1. 1.
    Fong, Y., et al., Clinical score for predicting recurrence after hepatic resection for metastatic colorectal cancer: analysis of 1001 consecutive cases. Ann Surg, 1999. 230(3): p. 309–18; discussion 318–21.PubMedCrossRefGoogle Scholar
  2. 2.
    Rees, M., et al., Evaluation of long-term survival after hepatic resection for metastatic colorectal cancer: a multifactorial model of 929 patients. Ann Surg, 2008. 247(1): p. 125–35.PubMedCrossRefGoogle Scholar
  3. 3.
    Ferenci, P., et al., World Gastroenterology Organisation Guideline. Hepatocellular carcinoma (HCC): a global perspective. J Gastrointestin Liver Dis, 2010. 19(3): p. 311–7.PubMedGoogle Scholar
  4. 4.
    Cash, D.M., et al., Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking. Med Phys, 2003. 30(7): p. 1671–82.PubMedCrossRefGoogle Scholar
  5. 5.
    Herline, A.J., et al., Image-guided surgery: preliminary feasibility studies of frameless stereotactic liver surgery. Arch Surg, 1999. 134(6): p. 644–9; discussion 649–50.PubMedCrossRefGoogle Scholar
  6. 6.
    Cash, D.M., et al., Concepts and preliminary data toward the realization of image-guided liver surgery. J Gastrointest Surg, 2007. 11(7): p. 844–59.PubMedCrossRefGoogle Scholar
  7. 7.
    Herline, A., et al., Technical advances toward interactive image-guided laparoscopic surgery. Surg Endosc, 2000. 14(7): p. 675–9.PubMedCrossRefGoogle Scholar
  8. 8.
    Clements, L.W., et al., Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation. Med Phys, 2008. 35(6): p. 2528–40.PubMedCrossRefGoogle Scholar
  9. 9.
    Kingham, T.P., et al., Patterns of recurrence after ablation of colorectal cancer liver metastases. Ann Surg Oncol, 2012. 19(3): p. 834–41.PubMedCrossRefGoogle Scholar
  10. 10.
    Cash, D.M., et al., Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements. IEEE Trans Med Imaging, 2005. 24(11): p. 1479–91.PubMedCrossRefGoogle Scholar
  11. 11.
    Herline, A.J., et al., Surface registration for use in interactive, image-guided liver surgery. Comput Aided Surg, 2000. 5(1): p. 11–7.PubMedGoogle Scholar
  12. 12.
    Stefansic, J.D., et al., Design and implementation of a PC-based image-guided surgical system. Comput Methods Programs Biomed, 2002. 69(3): p. 211–24.PubMedCrossRefGoogle Scholar
  13. 13.
    Kingham, T.P., et al., Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound. HPB (Oxford), 2012. 14(9): p. 594–603.CrossRefGoogle Scholar
  14. 14.
    Sinha, T.K., et al., A method to track cortical surface deformations using a laser range scanner. IEEE Trans Med Imaging, 2005. 24(6): p. 767–81.PubMedCrossRefGoogle Scholar
  15. 15.
    Risholm, P., A.J. Golby, and W. Wells, 3rd, Multimodal image registration for preoperative planning and image-guided neurosurgical procedures. Neurosurg Clin N Am, 2011. 22(2): p. 197–206, viii.PubMedCrossRefGoogle Scholar
  16. 16.
    Arun, K., T. Huang, and S. Blostein, Least squares fitting of two 3-D point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987. 9: p. 698–700.PubMedCrossRefGoogle Scholar
  17. 17.
    Horn, B., Closed form solution of absolute orientation using unit quaternions. J Opt Soc Amer, 1987. 4: p. 629–642.CrossRefGoogle Scholar
  18. 18.
    Shonemann, P. and R. Carroll, Fitting one matrix to another under choice of a central dilation and rigid motion. Psychometrika, 1970. 35: p. 245–255.CrossRefGoogle Scholar

Copyright information

© The Society for Surgery of the Alimentary Tract 2013

Authors and Affiliations

  • T. Peter Kingham
    • 1
    • 4
  • Shiva Jayaraman
    • 2
  • Logan W. Clements
    • 3
  • Michael A. Scherer
    • 3
  • James D. Stefansic
    • 3
  • William R. Jarnagin
    • 1
  1. 1.Department of Surgery, Hepatopancreatobiliary ServiceMemorial Sloan-Kettering Cancer CenterNew YorkUSA
  2. 2.Department of SurgeryUniversity of Toronto, St. Joseph’s Health CentreTorontoCanada
  3. 3.Pathfinder Technologies, Inc.NashvilleUSA
  4. 4.Memorial Sloan-Kettering Cancer CenterNew YorkUSA

Personalised recommendations