Abstract
METHODS
A pilot study was conducted in 25 patients undergoing resection of liver metastases. The first 5 cases served to optimize the workflow. Intraoperatively, an electromagnetic sensor compensated for organ motion, after which an ultrasound volume was acquired. Vasculature was segmented automatically and tumors semi-automatically using region-growing (n = 15) or a deep learning algorithm (n = 5). The resulting 3D model was visualized alongside tracked surgical instruments. Accuracy was assessed by comparing the distance between surgical clips and tumors in the navigation software with the same distance on a postoperative CT of the resected specimen.
CONCLUSION
Navigation based solely on intraoperative ultrasound is feasible and accurate for liver surgery. This approach paves the way for simpler and more accurate image guidance systems.
RESULTS
Navigation was successfully established in all 20 patients. However, 4 cases were excluded from the accuracy assessment due to intraoperative sensor detachment (n = 3) or incorrect data recording (n = 1). The complete navigation workflow was operational within 5-10 min. In 16 evaluable patients, 78 clip-to-tumor distances were analyzed. The median navigation accuracy was 3.2 mm [IQR: 2.8-4.8 mm], and an R0 resection was achieved in 15/16 (93.8%) patients, and one patient had an R1 vascular resection.
PURPOSE
This proof-of-concept study evaluates the feasibility and accuracy of an ultrasound-based navigation system for open liver surgery. Unlike most conventional systems that rely on registration to preoperative imaging, the proposed system provides navigation-guided resection using 3D models generated from intraoperative ultrasound.