Improving Intensity-Based Lung CT Registration Accuracy Utilizing Vascular InformationReport as inadecuate




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International Journal of Biomedical ImagingVolume 2012 2012, Article ID 285136, 17 pages

Research Article

Biomedical Image Analysis Lab, GE Global Research Center, Niskayuna, NY 12309, USA

Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA

Department of Biomedical Engineering, The University of Iowa, Iowa City, IA 52242, USA

Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA

Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA

Received 18 June 2012; Revised 14 September 2012; Accepted 28 September 2012

Academic Editor: Ayman El-Baz

Copyright © 2012 Kunlin Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate pulmonary image registration is a challenging problem when the lungs have a deformation with large distance. In this work, we present a nonrigid volumetric registration algorithm to track lung motion between a pair of intrasubject CT images acquired at different inflation levels and introduce a new vesselness similarity cost that improves intensity-only registration. Volumetric CT datasets from six human subjects were used in this study. The performance of four intensity-only registration algorithms was compared with and without adding the vesselness similarity cost function. Matching accuracy was evaluated using landmarks, vessel tree, and fissure planes. The Jacobian determinant of the transformation was used to reveal the deformation pattern of local parenchymal tissue. The average matching error for intensity-only registration methods was on the order of 1 mm at landmarks and 1.5 mm on fissure planes. After adding the vesselness preserving cost function, the landmark and fissure positioning errors decreased approximately by 25% and 30%, respectively. The vesselness cost function effectively helped improve the registration accuracy in regions near thoracic cage and near the diaphragm for all the intensity-only registration algorithms tested and also helped produce more consistent and more reliable patterns of regional tissue deformation.





Author: Kunlin Cao, Kai Ding, Joseph M. Reinhardt, and Gary E. Christensen

Source: https://www.hindawi.com/



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