2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images
Item
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Title
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en_US
2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images
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Creator
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Zollei, Lilla
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Date
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2004-10-20T20:28:33Z
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Date Available
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2004-10-20T20:28:33Z
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Date Issued
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2001-08-01
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Identifier
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AITR-2002-001
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Abstract
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The registration of pre-operative volumetric datasets to intra- operative two-dimensional images provides an improved way of verifying patient position and medical instrument loca- tion. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information- based registration algorithm to establish the proper align- ment. For optimization purposes, we compare the perfor- mance of the non-gradient Powell method and two slightly di erent versions of a stochastic gradient ascent strategy: one using a sparsely sampled histogramming approach and the other Parzen windowing to carry out probability density approximation. Our main contribution lies in adopting the stochastic ap- proximation scheme successfully applied in 3D-3D registra- tion problems to the 2D-3D scenario, which obviates the need for the generation of full DRRs at each iteration of pose op- timization. This facilitates a considerable savings in compu- tation expense. We also introduce a new probability density estimator for image intensities via sparse histogramming, de- rive gradient estimates for the density measures required by the maximization procedure and introduce the framework for a multiresolution strategy to the problem. Registration results are presented on uoroscopy and CT datasets of a plastic pelvis and a real skull, and on a high-resolution CT- derived simulated dataset of a real skull, a plastic skull, a plastic pelvis and a plastic lumbar spine segment.
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Extent
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128 p.
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21043480 bytes
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1712245 bytes
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Format
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application/postscript
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application/pdf
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Language
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en_US
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Relation
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en_US
AITR-2002-001
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Subject
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AI
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registration
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medical imaging