Automating neurosurgical tumor resection surgery: Volumetric laser ablation of cadaveric porcine brain with integrated surface mapping.

Journal Article (Journal Article)

OBJECTIVE: Current surgical instruments for soft tissue resection including neurosurgical procedures rely on the accuracy and precision of the human operator and are fundamentally constrained by the human hand. Automated surgical action with the integration of intraoperative data sources can enable highly accurate and fast tissue manipulation using laser ablation. This study presents the first experiments with a prototype designed for automated tumor resection via laser ablation. We demonstrate targeted soft tissue resection in porcine brain with an integrated device that combines 3D scanning capabilities with a steerable surgical laser and discuss implications for future automated robotic neurosurgical procedures. STUDY DESIGN AND METHODS: A device consisting of a two-axis galvanometer for steering a cutting laser and a 3D surface profiler is used to perform volumetric removal of tissue of ex vivo porcine brain. Three-dimensional surface profiles are gathered between cuts and used to estimate ablation rate. RESULTS: Volumetric ablation of porcine brain tissue is performed and subsequently surface profiled. The average ablation rates across the area cutting areas were 2.6 mm3 /s and 3.7 mm3 /s for the initial and subsequent cuts, respectively. A Kruskal-Wallis and post-hoc Tukey test show statistical significance between the initial and subsequent cuts. Accuracy between cuts when benchmarked against a human surgeon varied from 47 to 88%. CONCLUSION: A feed-forward volumetric resection is demonstrated with sensing and cutting housed within a single device, thereby opening the potential for automated soft tissue resection as necessary during the surgical removal of pathologic tissues. High variance around target cut depths motivates future work in developing a closed-loop ablation tool as well as characterization of laser-tissue interactions for predictive modelling. Objective Lasers Surg. 50:1017-1024, 2018. © 2018 Wiley Periodicals, Inc.

Full Text

Duke Authors

Cited Authors

  • Ross, WA; Hill, WM; Hoang, KB; Laarakker, AS; Mann, BP; Codd, PJ

Published Date

  • December 2018

Published In

Volume / Issue

  • 50 / 10

Start / End Page

  • 1017 - 1024

PubMed ID

  • 29984837

Pubmed Central ID

  • 29984837

Electronic International Standard Serial Number (EISSN)

  • 1096-9101

Digital Object Identifier (DOI)

  • 10.1002/lsm.23000


  • eng

Conference Location

  • United States