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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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