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Proceedings Paper

Automatic masking for robust 3D-2D image registration in image-guided spine surgery
Author(s): M. D. Ketcha; T. De Silva; A. Uneri; G. Kleinszig; S. Vogt; J.-P. Wolinsky; J. H. Siewerdsen
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Paper Abstract

During spinal neurosurgery, patient-specific information, planning, and annotation such as vertebral labels can be mapped from preoperative 3D CT to intraoperative 2D radiographs via image-based 3D-2D registration. Such registration has been shown to provide a potentially valuable means of decision support in target localization as well as quality assurance of the surgical product. However, robust registration can be challenged by mismatch in image content between the preoperative CT and intraoperative radiographs, arising, for example, from anatomical deformation or the presence of surgical tools within the radiograph. In this work, we develop and evaluate methods for automatically mitigating the effect of content mismatch by leveraging the surgical planning data to assign greater weight to anatomical regions known to be reliable for registration and vital to the surgical task while removing problematic regions that are highly deformable or often occluded by surgical tools. We investigated two approaches to assigning variable weight (i.e., "masking") to image content and/or the similarity metric: (1) masking the preoperative 3D CT ("volumetric masking"); and (2) masking within the 2D similarity metric calculation ("projection masking"). The accuracy of registration was evaluated in terms of projection distance error (PDE) in 61 cases selected from an IRB-approved clinical study. The best performing of the masking techniques was found to reduce the rate of gross failure (PDE > 20 mm) from 11.48% to 5.57% in this challenging retrospective data set. These approaches provided robustness to content mismatch and eliminated distinct failure modes of registration. Such improvement was gained without additional workflow and has motivated incorporation of the masking methods within a system under development for prospective clinical studies.

Paper Details

Date Published: 18 March 2016
PDF: 7 pages
Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860A (18 March 2016); doi: 10.1117/12.2216913
Show Author Affiliations
M. D. Ketcha, Johns Hopkins Univ. (United States)
T. De Silva, Johns Hopkins Univ. (United States)
A. Uneri, Johns Hopkins Univ. (United States)
G. Kleinszig, Siemens Healthcare (Germany)
S. Vogt, Siemens Healthcare (Germany)
J.-P. Wolinsky, Johns Hopkins Univ. (United States)
J. H. Siewerdsen, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 9786:
Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling
Robert J. Webster; Ziv R. Yaniv, Editor(s)

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