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Proceedings Paper

Dual-projection 3D-2D registration for surgical guidance: preclinical evaluation of performance and minimum angular separation
Author(s): A. Uneri; Y. Otake; A. S. Wang; G. Kleinszig; S. Vogt; G. L. Gallia; D. Rigamonti; J.-P. Wolinsky; Ziya L. Gokaslan; A. J. Khanna; J. H. Siewerdsen
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Paper Abstract

An algorithm for 3D-2D registration of CT and x-ray projections has been developed using dual projection views to provide 3D localization with accuracy exceeding that of conventional tracking systems. The registration framework employs a normalized gradient information (NGI) similarity metric and covariance matrix adaptation evolution strategy (CMAES) to solve for the patient pose in 6 degrees of freedom. Registration performance was evaluated in anthropomorphic head and chest phantoms, as well as a human torso cadaver, using C-arm projection views acquired at angular separations (Δ𝜃) ranging 0–178°. Registration accuracy was assessed in terms target registration error (TRE) and compared to that of an electromagnetic tracker. Studies evaluated the influence of C-arm magnification, x-ray dose, and preoperative CT slice thickness on registration accuracy and the minimum angular separation required to achieve TRE ~2 mm. The results indicate that Δ𝜃 as small as 10–20° is adequate to achieve TRE <2 mm with 95% confidence, comparable or superior to that of commercial trackers. The method allows direct registration of preoperative CT and planning data to intraoperative fluoroscopy, providing 3D localization free from conventional limitations associated with external fiducial markers, stereotactic frames, trackers, and manual registration. The studies support potential application to percutaneous spine procedures and intracranial neurosurgery.

Paper Details

Date Published: 12 March 2014
PDF: 8 pages
Proc. SPIE 9036, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 90362E (12 March 2014); doi: 10.1117/12.2043561
Show Author Affiliations
A. Uneri, Johns Hopkins Univ. (United States)
Y. Otake, Johns Hopkins Univ. (United States)
A. S. Wang, Johns Hopkins Univ. (United States)
G. Kleinszig, Siemens Healthcare (Germany)
S. Vogt, Siemens Healthcare (Germany)
G. L. Gallia, Johns Hopkins Medical Institute (United States)
D. Rigamonti, Johns Hopkins Medical Institute (United States)
J.-P. Wolinsky, Johns Hopkins Medical Institute (United States)
Ziya L. Gokaslan, Johns Hopkins Medical Institute (United States)
A. J. Khanna, Johns Hopkins Medical Institute (United States)
J. H. Siewerdsen, Johns Hopkins Univ. (United States)


Published in SPIE Proceedings Vol. 9036:
Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling
Ziv R. Yaniv; David R. Holmes, Editor(s)

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