Improving intensity-based lung CT registration accuracy utilizing vascular information

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number285136
JournalInternational Journal of Biomedical Imaging
Volume2012
DOIs
StatePublished - 2012
Externally publishedYes

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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