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.
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.
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|Gong, Yuanzheng; Seibel, Eric J (2016) Feature-based three-dimensional registration for repetitive geometry in machine vision. J Inf Technol Softw Eng 6:|
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|Gong, Yuanzheng; Meng, De; Seibel, Eric J (2015) Bound constrained bundle adjustment for reliable 3D reconstruction. Opt Express 23:10771-85|
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