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)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB016457-01
Application #
8458644
Study Section
Special Emphasis Panel (ZEB1-OSR-A (M1))
Program Officer
Krosnick, Steven
Project Start
2012-09-01
Project End
2016-08-31
Budget Start
2012-09-01
Budget End
2013-08-31
Support Year
1
Fiscal Year
2012
Total Cost
$329,147
Indirect Cost
$107,086
Name
University of Washington
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
605799469
City
Seattle
State
WA
Country
United States
Zip Code
98195
Savastano, Luis E; Seibel, Eric J (2017) Scanning Fiber Angioscopy: A Multimodal Intravascular Imaging Platform for Carotid Atherosclerosis. Neurosurgery 64:188-198
Yeoh, Ivan L; Reinhall, Per G; Berg, Martin C et al. (2017) Run-to-Run Optimization Control Within Exact Inverse Framework for Scan Tracking. J Dyn Syst Meas Control 139:0910111-9101112
Gong, Yuanzheng; Seibel, Eric J (2017) Three-dimensional measurement of small inner surface profiles using feature-based 3-D panoramic registration. Opt Eng 56:
Savastano, Luis E; Zhou, Quan; Smith, Arlene et al. (2017) Multimodal laser-based angioscopy for structural, chemical and biological imaging of atherosclerosis. Nat Biomed Eng 1:
Gong, Yuanzheng; Seibel, Eric J (2016) Feature-based three-dimensional registration for repetitive geometry in machine vision. J Inf Technol Softw Eng 6:
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; 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; 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

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