Danying Hu1, Yuanzheng Gong2, Eric J Seibel2, Laligam N Sekhar3, Blake Hannaford1. 1. Biorobotics Laboratory, Department of Electrical Engineering, University of Washington, Seattle, WA, USA. 2. Human Photonics Laboratory, Department of Mechanical Engineering, University of Washington, Seattle, WA, USA. 3. Department of Neurological Surgery, School of Medicine, University of Washington, Seattle, WA, USA.
Abstract
BACKGROUND: Complete brain tumour resection is an extremely critical factor for patients' survival rate and long-term quality of life. This paper introduces a prototype medical robotic system that aims to automatically detect and clean up brain tumour residues after the removal of tumour bulk through conventional surgery. METHODS: We focus on the development of an integrated surgical robotic system for image-guided robotic brain surgery. The Behavior Tree framework is explored to coordinate cross-platform medical subtasks. RESULTS: The integrated system was tested on a simulated laboratory platform. Results and performance indicate the feasibility of supervised semi-automation for residual brain tumour ablation in a simulated surgical cavity with sub-millimetre accuracy. The modularity in the control architecture allows straightforward integration of further medical devices. CONCLUSIONS: This work presents a semi-automated laboratory setup, simulating an intraoperative robotic neurosurgical procedure with real-time endoscopic image guidance and provides a foundation for the future transition from engineering approaches to clinical application.
BACKGROUND: Complete brain tumour resection is an extremely critical factor for patients' survival rate and long-term quality of life. This paper introduces a prototype medical robotic system that aims to automatically detect and clean up brain tumour residues after the removal of tumour bulk through conventional surgery. METHODS: We focus on the development of an integrated surgical robotic system for image-guided robotic brain surgery. The Behavior Tree framework is explored to coordinate cross-platform medical subtasks. RESULTS: The integrated system was tested on a simulated laboratory platform. Results and performance indicate the feasibility of supervised semi-automation for residual brain tumour ablation in a simulated surgical cavity with sub-millimetre accuracy. The modularity in the control architecture allows straightforward integration of further medical devices. CONCLUSIONS: This work presents a semi-automated laboratory setup, simulating an intraoperative robotic neurosurgical procedure with real-time endoscopic image guidance and provides a foundation for the future transition from engineering approaches to clinical application.
Authors: Pramod V Butte; Adam Mamelak; Julia Parrish-Novak; Doniel Drazin; Faris Shweikeh; Pallavi R Gangalum; Alexandra Chesnokova; Julia Y Ljubimova; Keith Black Journal: Neurosurg Focus Date: 2014-02 Impact factor: 4.047