Title |
Navigierte Leberchirurgie
|
---|---|
Published in |
Die Chirurgie, September 2018
|
DOI | 10.1007/s00104-018-0713-3 |
Pubmed ID | |
Authors |
K. J. Oldhafer, M. Peterhans, A. Kantas, A. Schenk, G. Makridis, S. Pelzl, K. C. Wagner, S. Weber, G. A. Stavrou, M. Donati |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Germany | 3 | 50% |
Unknown | 3 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 34 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 6 | 18% |
Student > Master | 5 | 15% |
Student > Ph. D. Student | 5 | 15% |
Student > Doctoral Student | 3 | 9% |
Student > Postgraduate | 2 | 6% |
Other | 3 | 9% |
Unknown | 10 | 29% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 10 | 29% |
Engineering | 5 | 15% |
Neuroscience | 3 | 9% |
Agricultural and Biological Sciences | 1 | 3% |
Social Sciences | 1 | 3% |
Other | 3 | 9% |
Unknown | 11 | 32% |