Complete resection of tumor tissue remains one of the most important factors for survival in patients with cancer. Surgical removal is the most common front-line cancer therapy. Tumor resection in the brain is exceptionally difficult because leaving residual tumor tissue leads to decreased survival and removing normal healthy brain tissue leads to life-long neurological deficits. Brain surgery requires a very high degree of dexterity, accurate navigation, and niicro-precision cutting over long durations;thus it is an idel candidate for robotically assisted surgery. However, tumor resection is compounded by the need to make a small opening (keyhole) in the skull, and the difficulty of distinguishing normal from diseased tissue in an intraoperative setting. A minimally-invasive robotic system that allows surgeons to directly visualize and accurately discriminate neoplastic (cancer) from non-neoplastic tissue in a real-time intra-operative setting is currently not available, but an ideal gal for NRI. We propose to overcome two major limitations affecting robotically-assisted surgery in a team approach: inability to (1) automatically and (2) optically guide treatments in a miriimally-invasive intraoperative environment with advanced photonics and new cancer biomarkers. Dr. Hannaford will lead the integration of the RAVEN II open hardware and softvvare robotic system with laser-based endoscopic imaging. A single robot arm will hold a standard surgical tool for resecting/removing tumor, and a novel scanning fluorescence and reflectance imaging system to provide the advanced photonics in an ultra-small size. A team of three research neurosurgeons (Drs. Olson, Ellenbogen, Sekhar) will help develop clinically relevant phantoms and biological models of future image-guided brain surgery. PI, Dr. Seibel will provide a new multi-modal scanning fiber endoscope (mmSFE) technology that allows advanced laser imaging, diagnostic, and therapeutic biophotonics approaches to intra-operative keyhole surgery for improved performance and safety.

Public Health Relevance

Minimally-invasive, advanced biomedical imaging is required to distinguish between healthy and cancerous brain tissue during a keyhole robotic surgical procedure. This research project develops new fluorescence cancer imaging techniques and effective robot-assisted surgical procedures for enhancing cancer treatment which encourages multidisciplinary engineering graduate study with three participating neurosurgeons.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01EB016457-03
Application #
8717419
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Krosnick, Steven
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of Washington
Department
Engineering (All Types)
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Seattle
State
WA
Country
United States
Zip Code
98195
Gong, Yuanzheng; Meng, De; Seibel, Eric J (2015) Bound constrained bundle adjustment for reliable 3D reconstruction. Opt Express 23:10771-85
Hu, Danying; Gong, Yuanzheng; Hannaford, Blake et al. (2015) Path Planning for Semi-automated Simulated Robotic Neurosurgery. Rep U S 2015:2639-2645
Hu, Danying; Gong, Yuanzheng; Hannaford, Blake et al. (2015) Semi-autonomous Simulated Brain Tumor Ablation with RavenII Surgical Robot using Behavior Tree. IEEE Int Conf Robot Autom 2015:3868-3875
Gong, Yuanzheng; Hu, Danying; Hannaford, Blake et al. (2015) Toward real-time endoscopically-guided robotic navigation based on a 3D virtual surgical field model. Proc SPIE Int Soc Opt Eng 9415:94150C
Gong, Yuanzheng; Johnston, Richard S; Melville, C David et al. (2015) Axial-Stereo 3-D Optical Metrology for Inner Profile of Pipes Using a Scanning Laser Endoscope. Int J Optomechatronics 9:238-247
McVeigh, Patrick Z; Sacho, Raphael; Weersink, Robert A et al. (2014) High-resolution angioscopic imaging during endovascular neurosurgery. Neurosurgery 75:171-80; discussion 179-80
Gong, Yuanzheng; Hu, Danying; Hannaford, Blake et al. (2014) Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot. J Med Imaging (Bellingham) 1:035002
Yang, Chenying; Hou, Vivian W; Girard, Emily J et al. (2014) Target-to-background enhancement in multispectral endoscopy with background autofluorescence mitigation for quantitative molecular imaging. J Biomed Opt 19:76014
Yang, Chenying; Hou, Vivian; Nelson, Leonard Y et al. (2013) Mitigating fluorescence spectral overlap in wide-field endoscopic imaging. J Biomed Opt 18:86012