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Jonić S, Thévenaz P, Zheng G, et al. An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images. Int J Biomed Imaging. 2006;2006:47197doi: 10.1155/IJBI/2006/47197.
Jonić, S., Thévenaz, P., Zheng, G., Nolte, L. P., & Unser, M. (2006). An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images. International journal of biomedical imaging, 200647197. https://doi.org/10.1155/IJBI/2006/47197
Jonić, S, et al. "An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images." International journal of biomedical imaging vol. 2006 (2006): 47197. doi: https://doi.org/10.1155/IJBI/2006/47197
Jonić S, Thévenaz P, Zheng G, Nolte LP, Unser M. An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images. Int J Biomed Imaging. 2006;2006:47197. doi: 10.1155/IJBI/2006/47197. Epub 2006 Apr 06. PMID: 23165033; PMCID: PMC2324037.
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Int J Biomed Imaging. 2006;2006:47197. doi: 10.1155/IJBI/2006/47197. Epub 2006 Apr 06.

An Optimized Spline-Based Registration of a 3D CT to a Set of C-Arm Images.

International journal of biomedical imaging

S Jonić, P Thévenaz, G Zheng, L-P Nolte, M Unser

Affiliations

  1. Biomedical Imaging Group, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne VD CH-1015, Switzerland ; Institut de Minéralogie et de Physique des Milieux Condensés, Université Pierre et Marie Curie, Paris 75015, France.

PMID: 23165033 PMCID: PMC2324037 DOI: 10.1155/IJBI/2006/47197

Abstract

We have developed an algorithm for the rigid-body registration of a CT volume to a set of C-arm images. The algorithm uses a gradient-based iterative minimization of a least-squares measure of dissimilarity between the C-arm images and projections of the CT volume. To compute projections, we use a novel method for fast integration of the volume along rays. To improve robustness and speed, we take advantage of a coarse-to-fine processing of the volume/image pyramids. To compute the projections of the volume, the gradient of the dissimilarity measure, and the multiresolution data pyramids, we use a continuous image/volume model based on cubic B-splines, which ensures a high interpolation accuracy and a gradient of the dissimilarity measure that is well defined everywhere. We show the performance of our algorithm on a human spine phantom, where the true alignment is determined using a set of fiducial markers.

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