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Proceedings Paper

High-accuracy 3D image-based registration of endoscopic video to C-arm cone-beam CT for image-guided skull base surgery
Author(s): Daniel J. Mirota; Ali Uneri; Sebastian Schafer; Sajendra Nithiananthan; Douglas D. Reh; Gary L. Gallia; Russell H. Taylor; Gregory D. Hager; Jeffrey H. Siewerdsen
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Paper Abstract

Registration of endoscopic video to preoperative CT facilitates high-precision surgery of the head, neck, and skull-base. Conventional video-CT registration is limited by the accuracy of the tracker and does not use the underlying video or CT image data. A new image-based video registration method has been developed to overcome the limitations of conventional tracker-based registration. This method adds to a navigation system based on intraoperative C-arm cone-beam CT (CBCT), in turn providing high-accuracy registration of video to the surgical scene. The resulting registration enables visualization of the CBCT and planning data within the endoscopic video. The system incorporates a mobile C-arm, integrated with an optical tracking system, video endoscopy, deformable registration of preoperative CT with intraoperative CBCT, and 3D visualization. Similarly to tracker-based approach, the image-based video-CBCT registration the endoscope is localized with optical tracking system followed by a direct 3D image-based registration of the video to the CBCT. In this way, the system achieves video-CBCT registration that is both fast and accurate. Application in skull-base surgery demonstrates overlay of critical structures (e.g., carotid arteries) and surgical targets with sub-mm accuracy. Phantom and cadaver experiments show consistent improvement of target registration error (TRE) in video overlay over conventional tracker-based registration-e.g., 0.92mm versus 1.82mm for image-based and tracker-based registration, respectively. The proposed method represents a two-fold advance-first, through registration of video to up-to-date intraoperative CBCT, and second, through direct 3D image-based video-CBCT registration, which together provide more confident visualization of target and normal tissues within up-to-date images.

Paper Details

Date Published: 2 March 2011
PDF: 10 pages
Proc. SPIE 7964, Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling, 79640J (2 March 2011); doi: 10.1117/12.877803
Show Author Affiliations
Daniel J. Mirota, Johns Hopkins Univ. (United States)
Ali Uneri, Johns Hopkins Univ. (United States)
Sebastian Schafer, Johns Hopkins Univ. (United States)
Sajendra Nithiananthan, Johns Hopkins Univ. (United States)
Douglas D. Reh, Johns Hopkins Hospital (United States)
Gary L. Gallia, Johns Hopkins Hospital (United States)
Russell H. Taylor, Johns Hopkins Univ. (United States)
Gregory D. Hager, Johns Hopkins Univ. (United States)
Jeffrey H. Siewerdsen, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 7964:
Medical Imaging 2011: Visualization, Image-Guided Procedures, and Modeling
Kenneth H. Wong; David R. Holmes, Editor(s)

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