Efficiency, Accuracy and Clinical Applicability of a New Image-Guided Surgery System in 3D Laparoscopic Liver Surgery

JOURNAL OF GASTROINTESTINAL SURGERY(2019)

引用 32|浏览10
暂无评分
摘要
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.
更多
查看译文
关键词
Augmented reality,3D,Image-guided surgery,Laparoscopy,Liver
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要