Gian Andrea Prevost1, Benjamin Eigl2,3, Iwan Paolucci2, Tobias Rudolph3, Matthias Peterhans3, Stefan Weber2, Guido Beldi4, Daniel Candinas4, Anja Lachenmayer4. 1. Department of Visceral Surgery and Medicine, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland. gianandrea.prevost@insel.ch. 2. ARTORG Center for Biomedical Engineering Research, University of Bern, 3010, Bern, Switzerland. 3. CAScination AG, 3008, Bern, Switzerland. 4. Department of Visceral Surgery and Medicine, Inselspital, University Hospital Bern, University of Bern, 3010, Bern, Switzerland.
Abstract
BACKGROUND: To investigate efficiency, accuracy and clinical benefit of a new augmented reality system for 3D laparoscopic liver surgery. METHODS: All patients who received laparoscopic liver resection by a new image-guided surgery system with augmented 3D-imaging in a university hospital were included for analysis. Digitally processed preoperative cross-sectional imaging was merged with the laparoscopic image. Intraoperative efficiency of the procedure was measured as time needed to achieve sufficient registration accuracy. Technical accuracy was reported as fiducial registration error (FRE). Clinical benefit was assessed trough a questionnaire, reporting measures in a 5-point Likert scale format ranging from 1 (high) to 5 (low). RESULTS: From January to March 2018, ten laparoscopic liver resections of a total of 18 lesions were performed using the novel augmented reality system. Median time for registration was 8:50 min (range 1:31-23:56). The mean FRE was reduced from 14.0 mm (SD 5.0) in the first registration attempt to 9.2 mm (SD 2.8) in the last attempt. The questionnaire revealed the ease of use of the system (1.2, SD 0.4) and the benefit for resection of vanishing lesions (1.0, SD 0.0) as convincing positive aspects, whereas image registration accuracy for resection guidance was consistently judged as too inaccurate. CONCLUSIONS: Augmented reality in 3D laparoscopic liver surgery with landmark-based registration technique is feasible with only little impact on the intraoperative workflow. The benefit for detecting particularly vanishing lesions is high. For an additional benefit during the resection process, registration accuracy has to be improved and non-rigid registration algorithms will be required to address intraoperative anatomical deformation.
BACKGROUND: To investigate efficiency, accuracy and clinical benefit of a new augmented reality system for 3D laparoscopic liver surgery. METHODS: All patients who received laparoscopic liver resection by a new image-guided surgery system with augmented 3D-imaging in a university hospital were included for analysis. Digitally processed preoperative cross-sectional imaging was merged with the laparoscopic image. Intraoperative efficiency of the procedure was measured as time needed to achieve sufficient registration accuracy. Technical accuracy was reported as fiducial registration error (FRE). Clinical benefit was assessed trough a questionnaire, reporting measures in a 5-point Likert scale format ranging from 1 (high) to 5 (low). RESULTS: From January to March 2018, ten laparoscopic liver resections of a total of 18 lesions were performed using the novel augmented reality system. Median time for registration was 8:50 min (range 1:31-23:56). The mean FRE was reduced from 14.0 mm (SD 5.0) in the first registration attempt to 9.2 mm (SD 2.8) in the last attempt. The questionnaire revealed the ease of use of the system (1.2, SD 0.4) and the benefit for resection of vanishing lesions (1.0, SD 0.0) as convincing positive aspects, whereas image registration accuracy for resection guidance was consistently judged as too inaccurate. CONCLUSIONS: Augmented reality in 3D laparoscopic liver surgery with landmark-based registration technique is feasible with only little impact on the intraoperative workflow. The benefit for detecting particularly vanishing lesions is high. For an additional benefit during the resection process, registration accuracy has to be improved and non-rigid registration algorithms will be required to address intraoperative anatomical deformation.
Authors: Philip C Müller; Caroline Haslebacher; Daniel C Steinemann; Beat P Müller-Stich; Thilo Hackert; Matthias Peterhans; Benjamin Eigl Journal: Surg Endosc Date: 2020-04-06 Impact factor: 4.584
Authors: Jasper N Smit; Koert F D Kuhlmann; Oleksandra V Ivashchenko; Bart R Thomson; Thomas Langø; Niels F M Kok; Matteo Fusaglia; Theo J M Ruers Journal: Int J Comput Assist Radiol Surg Date: 2022-05-27 Impact factor: 3.421
Authors: C Schneider; S Thompson; J Totz; Y Song; M Allam; M H Sodergren; A E Desjardins; D Barratt; S Ourselin; K Gurusamy; D Stoyanov; M J Clarkson; D J Hawkes; B R Davidson Journal: Surg Endosc Date: 2020-08-11 Impact factor: 4.584