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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Manifold learning for 4D CT reconstruction of the lung
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Manfred Georg, Washington University in St. Louis, MO 63130 USA
Richard Souvenir, UNC Charlotte, NC 28223 USA
Andrew Hope, University of Toronto, ON, M5G 2M9, Canada
Robert Pless, Washington University in St. Louis, MO 63130 USA
Computed Tomography is used to create models of lung dynamics because it provides high contrast images of lung tissue. Creating 4D CT models which capture dynamics is complicated because clinical CT scanners capture data in slabs that comprise only a small part of the tissue. Commonly, creating 4D reconstruction requires stitching together different lung segments based on an external measure of lung volume. This paper presents a novel method for assembling 4D CT datasets using only the CT data. We use a manifold learning algorithm to parameterize each slab data with respect to the breathing cycle, and an alignment method to coordinate these parameterizations for different sections of the lung. Comparing this data driven parameterization with physiological measurements captured by a belt around the abdomen, we are able to generate slightly smoother reconstructions.
Citation:
Manfred Georg, Richard Souvenir, Andrew Hope, Robert Pless, "Manifold learning for 4D CT reconstruction of the lung," cvprw, pp.1-8, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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