Advertisement

Clinical application of a surgical navigation system based on virtual laparoscopy in laparoscopic gastrectomy for gastric cancer

  • Yuichiro HayashiEmail author
  • Kazunari Misawa
  • Masahiro Oda
  • David J Hawkes
  • Kensaku Mori
Original Article

Abstract

Purpose

Knowledge of the specific anatomical information of a patient is important when planning and undertaking laparoscopic surgery due to the restricted field of view and lack of tactile feedback compared to open surgery. To assist this type of surgery, we have developed a surgical navigation system that presents the patient’s anatomical information synchronized with the laparoscope position. This paper presents the surgical navigation system and its clinical application to laparoscopic gastrectomy for gastric cancer.

Methods

The proposed surgical navigation system generates virtual laparoscopic views corresponding to the laparoscope position recorded with a three-dimensional (3D) positional tracker. The virtual laparoscopic views are generated from preoperative CT images. A point-based registration aligns coordinate systems between the patient’s anatomy and image coordinates. The proposed navigation system is able to display the virtual laparoscopic views using the registration result during surgery.

Results

We performed surgical navigation during laparoscopic gastrectomy in 23 cases. The navigation system was able to present the virtual laparoscopic views in synchronization with the laparoscopic position. The fiducial registration error was calculated in all 23 cases, and the average was 14.0 mm (range 6.1–29.8).

Conclusion

The proposed surgical navigation system can provide CT-derived patient anatomy aligned to the laparoscopic view in real time during surgery. This system enables accurate identification of vascular anatomy as a guide to vessel clamping prior to total or partial gastrectomy.

Keywords

Surgical navigation Laparoscopy  Virtual laparoscopy Stomach Laparoscopic gastrectomy Gastric cancer 

Notes

Acknowledgments

The authors thank our colleagues for suggestions and advice. This work was supported in part by a Grant-In-Aid for Scientific Research (KAKENHI) from the Ministry of Education, Culture, Sports, Science and Technology and the Japan Society for the Promotion of Science, by a Health and Labour Sciences Research Grant from the Ministry of Health, Labour and Welfare, and by the Practical Research for Innovative Cancer Control from Japan Agency for Medical Research and Development, AMED.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the institutional review board of the Aichi Cancer Center.

Informed consent

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

Supplementary material

11548_2015_1293_MOESM1_ESM.mpeg (10.8 mb)
Supplementary material 1 (mpeg 11074 KB)

References

  1. 1.
    Kitano S, Iso Y, Moriyama M, Sugimachi K (1994) Laparoscopy-assisted Billroth I gastrectomy. Surg Laparosc Endosc 4:146–148PubMedGoogle Scholar
  2. 2.
    Etoh T, Inomata M, Shiraishi N, Kitano S (2013) Minimally invasive approaches for gastric cancer-Japanese experiences. J Surg Oncol 107:282–288. doi: 10.1002/jso.23128
  3. 3.
    Watanabe E, Watanabe T, Manaka S, Mayanagi Y, Takakura K (1987) Three-dimensional digitizer (Neuronavigator): new equipment for computed tomography-guided stereotaxic surgery. Surg Neurol 27:543–547CrossRefPubMedGoogle Scholar
  4. 4.
    Kato A, Yoshimine T, Hayakawa T, Tomita Y, Ikeda T, Mitomo M, Harada K, Mogami H (1991) A frameless, armless navigational system for computer-assisted neurosurgery. J Neurosurg 74:845–849CrossRefPubMedGoogle Scholar
  5. 5.
    Nakamura N, Nishii T, Kitada M, Iwana D, Sugano N (2013) Application of computed tomography-based navigation for revision total hip arthroplasty. J Arthroplasty 28:1806–1810. doi: 10.1016/j.arth.2012.11.015 CrossRefPubMedGoogle Scholar
  6. 6.
    Kohan D, Jethanamest D (2012) Image-guided surgical navigation in otology. Laryngoscope 122:2291–2299. doi: 10.1002/lary.23522 CrossRefPubMedGoogle Scholar
  7. 7.
    Peterhans M, vom Berg A, Dagon B, Inderbitzin D, Baur C, Candinas D, Weber S (2011) A navigation system for open liver surgery: design, workflow and first clinical applications. Int J Med Robot 7:7–16. doi: 10.1002/rcs.360 CrossRefPubMedGoogle Scholar
  8. 8.
    Marescaux J, Rubino F, Arenas M, Mutter D, Soler L (2004) Augmented-reality-assisted laparoscopic adrenalectomy. JAMA 292:2211–2215. doi: 10.1001/jama.292.18.2214-c Google Scholar
  9. 9.
    Nicolau S, Soler L, Mutter D, Marescaux J (2011) Augmented reality in laparoscopic surgical oncology. Surg Oncol 20:189–201. doi: 10.1016/j.suronc.2011.07.002 CrossRefPubMedGoogle Scholar
  10. 10.
    Langø T, Tangen GA, Mårvik R, Ystgaard B, Yavuz Y, Kaspersen JH, Solberg OV, Hernes TA (2008) Navigation in laparoscopy -prototype research platform for improved image-guided surgery. Minim Invasive Ther Allied Technol 17:17–33. doi: 10.1080/13645700701797879 CrossRefPubMedGoogle Scholar
  11. 11.
    Mårvik R, Langø T, Tangen GA, Andersen JO, Kaspersen JH, Ystgaard B, Sjølie E, Fougner R, Fjøsne HE, Nagelhus Hernes TA (2004) Laparoscopic navigation pointer for three-dimensional image-guided surgery. Surg Endosc 18:1242–1248. doi: 10.1007/s00464-003-9190-x CrossRefPubMedGoogle Scholar
  12. 12.
    Teber D, Guven S, Simpfendörfer T, Baumhauer M, Güven EO, Yencilek F, Gözen AS, Rassweiler J (2009) Augmented reality: a new tool to improve surgical accuracy during laparoscopic partial nephrectomy? Preliminary in vitro and in vivo results. Eur Urol 56:332–338. doi: 10.1016/j.eururo.2009.05.017 CrossRefPubMedGoogle Scholar
  13. 13.
    Ieiri S, Uemura M, Konishi K, Souzaki R, Nagao Y, Tsutsumi N, Akahoshi T, Ohuchida K, Ohdaira T, Tomikawa M, Tanoue K, Hashizume M, Taguchi T (2012) Augmented reality navigation system for laparoscopic splenectomy in children based on preoperative CT image using optical tracking device. Pediatr Surg Int 28:341–346. doi: 10.1007/s00383-011-3034-x CrossRefPubMedGoogle Scholar
  14. 14.
    Tsutsumi N, Tomikawa M, Uemura M, Akahoshi T, Nagao Y, Konishi K, Ieiri S, Hong J, Maehara Y, Hashizume M (2013) Image-guided laparoscopic surgery in an open MRI operating theater. Surg Endosc 27:2178–2184. doi: 10.1007/s00464-012-2737-y CrossRefPubMedGoogle Scholar
  15. 15.
    Takiguchi S, Fujiwara Y, Yamasaki M, Miyata H, Nakajima K, Nishida T, Sekimoto M, Hori M, Nakamura H, Mori M, Doki Y (2014) Laparoscopic intraoperative navigation surgery for gastric cancer using real-time rendered 3D CT images. Surg Today 45:618–624. doi: 10.1007/s00595-014-0983-4 CrossRefPubMedGoogle Scholar
  16. 16.
    Feuerstein M, Mussack T, Heining SM, Navab N (2008) Intraoperative laparoscope augmentation for port placement and resection planning in minimally invasive liver resection. IEEE Trans Med Imag 27:355–369. doi: 10.1109/TMI.2007.907327 CrossRefGoogle Scholar
  17. 17.
    Konishi K, Nakamoto M, Kakeji Y, Tanoue K, Kawanaka H, Yamaguchi S, Ieiri S, Sato Y, Maehara Y, Tamura S, Hashizume M (2007) A real-time navigation system for laparoscopic surgery based on three-dimensional ultrasound using magneto-optic hybrid tracking configuration. Int J CARS 2:1–10. doi: 10.1007/s11548-007-0078-4 CrossRefGoogle Scholar
  18. 18.
    Thompson S, Totz J, Song Y, Johnsen S, Stoyanov D, Ourselin S, Gurusamy K, Schneider C, Davidson B, Hawkes D, Clarkson MJ (2015) Accuracy validation of an image guided laparoscopy system for liver resection. In: Yaniv ZR, Webster RJ (eds) Proceedings of SPIE medical imaging 2015: image-guided procedures, robotic interventions, and modeling, SPIE, 941509. doi: 10.1117/12.2080974
  19. 19.
  20. 20.
  21. 21.
  22. 22.
    Lee SW, Shinohara H, Matsuki M, Okuda J, Nomura E, Mabuchi H, Nishiguchi K, Takaori K, Narabayashi I, Tanigawa N (2003) Preoperative simulation of vascular anatomy by three-dimensional computed tomography imaging in laparoscopic gastric cancer surgery. J Am Coll Surg 197:927–936. doi: 10.1016/j.jamcollsurg.2003.07.021 CrossRefPubMedGoogle Scholar
  23. 23.
    Iino I, Sakaguchi T, Kikuchi H, Miyazaki S, Fujita T, Hiramatsu Y, Ohta M, Kamiya K, Ushio T, Takehara Y, Konno H (2013) Usefulness of three-dimensional angiographic analysis of perigastric vessels before laparoscopic gastrectomy. Gastric Cancer 16:355–361. doi: 10.1007/s10120-012-0194-x CrossRefPubMedGoogle Scholar
  24. 24.
    Hayashi Y, Misawa K, Oda M, Mori K (2014) Clinical application of 3D virtual navigation system to laparoscopic gastrectomy. Int J CARS 9:S311–S312CrossRefGoogle Scholar
  25. 25.
    Fitzpatrick JM, Hill DLG, Maurer CR Jr (2000) Image registration. In: Sonka M, Fitzpatrick JM (eds) Handbook of medical imaging, medical image processing and analysis, vol 2. SPIE, Bellingham, pp 447–513Google Scholar
  26. 26.
    Horn BKP (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A 4:629–642CrossRefGoogle Scholar
  27. 27.
    Mori K, Urano A, Hasegawa J, Toriwaki J, Anno H, Katada K (1996) Virtualized endoscope system-an application of virtual reality technology to diagnostic aid-. IEICE Trans Inf Syst E79–D:809–819Google Scholar
  28. 28.
    Mori K, Suenaga Y, Toriwaki J (2002) Fast volume rendering based on software optimization using multimedia instructions on PC platform. In: Lemke HU, Vannier MW, Inamura K, Farman K, Doi K, Reiber JHC (eds) CARS 2002. Springer, Berlin, pp 467–472Google Scholar
  29. 29.
    Glocker B, Sotiras A, Komodakis N, Paragios N (2011) Deformable medical image registration: setting the state of the art with discrete methods. Annu Rev Biomed Eng 13:219–244. doi: 10.1146/annurev-bioeng-071910-124649 CrossRefPubMedGoogle Scholar
  30. 30.
    Komodakis N, Tziritas G, Paragios N (2007) Fast, approximately optimal solutions for single and dynamic MRFs. IEEE Conf Comput Vis Pattern Recognit 2007:1–8Google Scholar
  31. 31.
    Mori K, Hasegawa J, Toriwaki J, Anno H, Katada K (1996) Recognition of bronchus in three-dimensional X-ray CT images with applications to virtualized bronchoscopy system, In: Proceedings of the 13th international conference on pattern recognition, vol 3. IEEE, pp 528–532Google Scholar
  32. 32.
    Boykov Y, Kolmogorov V (2004) An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans Pattern Anal Mach Intell 26:1124–1137. doi: 10.1109/TPAMI.2004.60 CrossRefPubMedGoogle Scholar
  33. 33.
    Grady L, Jolly MP (2008) Weights and topology: a study of the effects of graph construction on 3D image segmentation. In: Metaxas D, Axel L, Fichtinger G, Székely G (eds) Medical image computing and computer-assisted intervention - MICCAI 2008 LNCS 5241. Springer, Berlin, pp 153–161Google Scholar
  34. 34.
    Fitzpatrick JM, West JB, Maurer CR Jr (1988) Predicting error in rigid-body point-based registration. IEEE Trans Med Imag 17:694–702. doi: 10.1109/42.736021
  35. 35.
    Langø T, Vijayan S, Rethy A, Våpenstad C, Solberg OV, Mårvik R, Johnsen G, Hernes TN (2012) Navigated laparoscopic ultrasound in abdominal soft tissue surgery: technological overview and perspectives. Int J CARS 7:585–99. doi: 10.1007/s11548-011-0656-3 CrossRefGoogle Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Yuichiro Hayashi
    • 1
    Email author
  • Kazunari Misawa
    • 2
  • Masahiro Oda
    • 3
  • David J Hawkes
    • 4
    • 5
  • Kensaku Mori
    • 1
    • 3
  1. 1.Information & CommunicationsNagoya UniversityNagoyaJapan
  2. 2.Department of Gastroenterological SurgeryAichi Cancer Center HospitalNagoyaJapan
  3. 3.Graduate School of Information Science, Nagoya UniversityNagoyaJapan
  4. 4.Information Technology CenterNagoya UniversityNagoyaJapan
  5. 5.Centre for Medical Image ComputingUniversity College LondonLondonUK

Personalised recommendations