2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images

Item

Title
en_US 2D-3D Rigid-Body Registration of X-Ray Fluoroscopy and CT Images
Creator
en_US Zollei, Lilla
Date
2004-10-20T20:28:33Z
Date Available
2004-10-20T20:28:33Z
Date Issued
en_US 2001-08-01
Identifier
en_US AITR-2002-001
uri
http://hdl.handle.net/1721.1/7078
Abstract
en_US 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.
Extent
en_US 128 p.
21043480 bytes
1712245 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-2002-001
Subject
en_US AI
en_US registration
en_US medical imaging
Item sets
AI Technical Reports (1964 - 2004)