Zusammenfassung
Die präoperative computergestützte Resektionsplanung ist die Grundlage für jede Navigation. Dank moderner Algorithmen sind die Voraussetzungen geschaffen, eine virtuelle Resektionsplanung und Risikoanalyse vorzunehmen. So sind individuelle Segmentresektionen in jeder denkbaren Kombination exakt planbar. Problematisch ist nach wie vor, Planungsinformationen und Resektionsvorschläge in den Operationssaal zu transferieren. Die sog. stereotaktische Lebernavigation unterstützt die genaue, intraoperative Umsetzung der geplanten Resektionsstrategie und stellt dem Chirurgen während der Resektion dreidimensionale Information zu Resektionsgrenzen und kritischen Strukturen dar. Dies wird durch ein chirurgisches Navigationssystem ermöglicht, das die Position von chirurgischen Instrumenten misst und diese dann zusammen mit den präoperativen chirurgischen Planungsdaten darstellt. Obwohl chirurgische Navigationssysteme in der Neuro- und Wirbelsäulenchirurgie seit Jahren nicht mehr wegzudenken sind, konnten diese Verfahren bis jetzt in der Leberchirurgie nicht als Standard etabliert werden. Dies liegt v. a. an der technischen Herausforderung der Navigation an einem beweglichen Organ. Da sich die Leber während der Operation durch Atmung und chirurgische Manipulation laufend bewegt und verformt, muss das chirurgische Navigationssystem diese Deformation messen können, um die präoperativen Navigationsdaten laufend an die aktuelle Situation anzupassen. Trotz dieser Fortschritte bedarf es noch weiterer Entwicklungen, bis die navigierte Leberresektion in die klinische Routine kommt. Es lässt sich jetzt jedoch schon absehen, dass die laparoskopische Leberchirurgie und die Roboterchirurgie am meisten von der Navigationstechnologie profitieren werden.
Abstract
The preoperative computer-assisted resection planning is the basis for every navigation. Thanks to modern algorithms, the prerequisites have been created to carry out a virtual resection planning and a risk analysis. Thus, individual segment resections can be precisely planned in any conceivable combination. The transfer of planning information and resection suggestions to the operating theater is still problematic. The so-called stereotactic liver navigation supports the exact intraoperative implementation of the planned resection strategy and provides the surgeon with real-time three-dimensional information on resection margins and critical structures during the resection. This is made possible by a surgical navigation system that measures the position of surgical instruments and then presents them together with the preoperative surgical planning data. Although surgical navigation systems have been indispensable in neurosurgery and spinal surgery for many years, these procedures have not yet become established as standard in liver surgery. This is mainly due to the technical challenge of navigating a moving organ. As the liver is constantly moving and deforming during surgery due to respiration and surgical manipulation, the surgical navigation system must be able to measure these alterations in order to adapt the preoperative navigation data to the current situation. Despite these advances, further developments are required until navigated liver resection enters clinical routine; however, it is already clear that laparoscopic liver surgery and robotic surgery will benefit most from navigation technology.
Literatur
Asakuma M, Fujimoto Y, Bourquain H et al (2007) Graft selection algorithm based on congestion volume for adult living donor liver transplantation. Am J Transplant 7:1788–1796
Banz VM, Muller PC, Tinguely P et al (2016) Intraoperative image-guided navigation system: development and applicability in 65 patients undergoing liver surgery. Langenbecks Arch Surg 401:495–502
Bao P, Warmath J, Galloway R Jr. et al (2005) Ultrasound-to-computer-tomography registration for image-guided laparoscopic liver surgery. Surg Endosc 19:424–429
Beller S, Hünerbein M, Eulenstein S et al (2007) Feasibility of navigated resection of liver tumors using multiplanar visualization of Intraoperative 3‑dimensional ultrasound data. Ann Surg 246:288–294
Chlebus G, Meine H, Moltz HJ et al (2017) Neureal network-based automatic liver tumor segmentation with random forest-based candidate filtering. CoRR. http://arxiv.org/abs/1706.00842. Zugegriffen: 10. Mai 2018
Clements LW, Collins JA, Weis JA et al (2016) Evaluation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound. J Med Imaging (Bellingham) 3:15003
Conrad C, Fusaglia M, Peterhans M et al (2016) Augmented reality navigation surgery facilitates laparoscopic rescue of failed portal vein embolization. J Am Coll Surg 223:e31–e34
Engstrand J, Nilsson H, Jansson A et al (2014) A multiple microwave ablation strategy in patients with initially unresectable colorectal cancer liver metastases—a safety and feasibility study of a new concept. Eur J Surg Oncol 40:1488–1493
Fasel JH, Schenk A (2013) Concepts for liver segment classification: neither old ones nor new ones, but a comprehensive one. J Clin Imaging Sci 3:48
Haouchine N, Dequidt J, Peterlik I, Kerrien E, Berger M, Cotin S (2013) Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Adelaide, SA, 2013, pp. 199–208. https://doi.org/10.1109/ISMAR.2013.6671780
Herline AJ, Herring JL, Stefansic JD et al (2000) Surface registration for use in interactive, image-guided liver surgery. Comput Aided Surg 5:11–17
Herline AJ, Stefansic JD, Debelak JP et al (1999) Image-guided surgery: preliminary feasibility studies of frameless stereotactic liver surgery. Arch Surg 134:644–650
Kingham TP, Jayaraman S, Clements LW et al (2013) Evolution of image-guided liver surgery: transition from open to laparoscopic procedures. J Gastrointest Surg 17:1274–1282
Kingham TP, Scherer MA, Neese BW et al (2012) Image-guided liver surgery: intraoperative projection of computed tomography images utilizing tracked ultrasound. HPB (Oxford) 14:594–603
Oldhafer KJ, Stavrou GA, Prause G et al (2009) How to operate a liver tumor you cannot see. Langenbecks Arch Surg 394:489–494
Panaro F, Habibeh H, Pessaux P et al (2015) Navigation liver surgery for complex hydatid cyst with biliary tree communication. Int J Surg Case Rep 12:112–116
Peterhans M, Vom Berg A, Dagon B et al (2011) A navigation system for open liver surgery: design, workflow and first clinical applications. Int J Med Robot 7:7–16
Peterlik I, Courtecuisse H, Rohling R et al (2018) Fast elastic registration of soft tissues under large deformations. Med Image Anal 45:24–40
Radtke A, Sgourakis G, Sotiropoulos GC et al (2010) Donor/recipient algorithm for management of the middle hepatic vein in right graft live donor liver transplantation. Am J Surg 199:708–715
Reichard D, Bodenstedt S, Suwelack S et al (2015) Intraoperative on-the-fly organ-mosaicking for laparoscopic surgery. J Med Imaging (Bellingham) 2:45001
Robu MR, Edwards P, Ramalhinho J et al (2017) Intelligent viewpoint selection for efficient CT to video registration in laparoscopic liver surgery. Int J Comput Assist Radiol Surg 12:1079–1088
Selle D, Preim B, Schenk A et al (2002) Analysis of vasculature for liver surgical planning. IEEE Trans Med Imaging 21:1344–1357
Stavrou GA, Donati M, Ringe KI et al (2012) Liver remnant hypertrophy induction—how often do we really use it in the time of computer assisted surgery? Adv Med Sci 57:251–258
Stillstrom D, Nilsson H, Jesse M et al (2017) A new technique for minimally invasive irreversible electroporation of tumors in the head and body of the pancreas. Surg Endosc 31:1982–1985
Suwelack S, Rohl S, Bodenstedt S et al (2014) Physics-based shape matching for intraoperative image guidance. Med Phys 41:111901
Tinguely P, Fusaglia M, Freedman J et al (2017) Laparoscopic image-based navigation for microwave ablation of liver tumors—a multi-center study. Surg Endosc 31:4315–4324
Tinguely P, Ribes D, Worni M, Peterhans M, Weber S, Candinas D (2014) Preliminary experience withmultiple microwave ablation facilitated by computer-assisted liver navigation in advanced neuroendocrine liver metastasis. Abstracts of the 11th World Congress of the International Hepato-Pancreato-Biliary Association, 22–27 March 2014, Seoul Korea, HPB , Volume 16, 406–549, Elsevier, New York
Yabushita Y, Matsuyama R, Mori R et al (2017) iPad guided right hemihepatectomy with a new application designed specifically for navigation surgery. In: 6th Biennial Congress of the Asian-Pacific Hepato-Pancreato-Biliary Association Yokohama. J Hepatobiliary Pancreat Sci 24(Suppl 1):A107
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K.J. Oldhafer, M. Peterhans, A. Kantas, A. Schenk, G. Makridis, S. Pelzl, K.C. Wagner, S. Weber, G.A. Stavrou und M. Donati geben an, dass kein Interessenkonflikt besteht.
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Oldhafer, K.J., Peterhans, M., Kantas, A. et al. Navigierte Leberchirurgie. Chirurg 89, 769–776 (2018). https://doi.org/10.1007/s00104-018-0713-3
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DOI: https://doi.org/10.1007/s00104-018-0713-3