This project will provide new registration and visualization tools for functional endoscopic sinus surgery (FESS) using widely available high-definition endoscopic video. These tools will provide higher accuracy navigation accuracy to the surgeon, and will make it possible to accurately measure change as surgery progresses. The key innovation in the project is the integration of algorithms for computational vision with traditional navigation methods to provide these enhancements. The algorithms will be evaluated retrospectively on video and navigation data acquired during FESS procedures. The project has four specific aims:
Aim 1 : Develop video-CT registration algorithms that are accurate to CT resolution.
Aim 2 : Develop methods for surface shape estimation from endoscopic images.
Aim 3 : Perform comparative evaluation of video-CT-based navigation on patient data.
Aim 4 : Assess the accuracy and reliability of intraoperative surface estimation on patient data. The significance of improved navigation is to 1) enhancement patient safety and outcomes by reducing potential complications and radiation exposure, and 2) to reduce cost by improving clinical workflow and clarity of intraoperative visualization. In the United States, it is estimate that there are more than 200,000 sinus surgery procedures performed annually. All of these are performed under endoscopic guidance, and a large fraction can or could employ surgical navigation. Thus, even moderate improvements in outcome and workflow efficiency can lead to significant benefits to both patients and the health care system. The innovation of the proposed approach is the use of the images from the endoscope itself as the basis for: 1) registration to pre-operative or intra-operative volumes, and 2) reconstruction of anatomic surfaces. Prior work has demonstrated that these problems are both solvable. The project will combine the efforts of an experienced team consisting of engineering and clinical faculty, and will focus on translation of the research to clinically relevant data. The methodology of the project will be to develop and validate algorithms extensively on cadaver models with the goal of achieving 0.5 mm accuracy for both registration and surface reconstruction. Once these goals are achieved, the algorithms will be assessed on patient data acquired during FESS procedures. Although aimed at FESS, the proposed methods are widely applicable to other areas of endoscopy and laparoscopy.
This project will provide new registration and visualization tools for sinus surgery using widely available high-definition endoscopic video. These tools will provide higher accuracy navigation to the surgeon, and will make it possible to accurately measure change as surgery progresses. The impact of these tools will be to enhance patient safety, reduce operative time, and reduce the need for intraoperative CT or cone beam imaging.
|Billings, Seth D; Sinha, Ayushi; Reiter, Austin et al. (2016) Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm. Med Image Comput Comput Assist Interv 9902:133-141|
|Leonard, Simon; Reiter, Austin; Sinha, Ayushi et al. (2016) Image-Based Navigation for Functional Endoscopic Sinus Surgery Using Structure From Motion. Proc SPIE Int Soc Opt Eng 9784:|
|Sinha, Ayushi; Leonard, Simon; Reiter, Austin et al. (2016) Automatic segmentation and statistical shape modeling of the paranasal sinuses to estimate natural variations. Proc SPIE Int Soc Opt Eng 9784:|
|Reiter, A; Leonard, S; Sinha, A et al. (2016) Endoscopic-CT: Learning-Based Photometric Reconstruction for Endoscopic Sinus Surgery. Proc SPIE Int Soc Opt Eng 9784:|
|Otake, Y; Leonard, S; Reiter, A et al. (2015) Rendering-Based Video-CT Registration with Physical Constraints for Image-Guided Endoscopic Sinus Surgery. Proc SPIE Int Soc Opt Eng 9415:|
|Xiang, Xiang; Mirota, Daniel; Reiter, Austin et al. (2014) Is Multi-model Feature Matching Better for Endoscopic Motion Estimation? Comput Assist Robot Endosc (2014) 8899:88-98|
|Wang, Hanzi; Mirota, Daniel; Hager, Gregory D (2010) A generalized Kernel Consensus-based robust estimator. IEEE Trans Pattern Anal Mach Intell 32:178-84|
|Mirota, Daniel; Wang, Hanzi; Taylor, Russell H et al. (2009) Toward Video-Based Navigation for Endoscopic Endonasal Skull Base Surgery. Med Image Comput Comput Assist Interv 5761:91-99|