Rationale and Objectives
Accurate assessment of air density used to quantitatively characterize amount and
distribution of emphysema in chronic obstructive pulmonary disease (COPD) subjects
has remained challenging. Hounsfield units (HU) within tracheal air can be considerably
less negative than –1000 HU. This study has sought to characterize the effects of
improved scatter correction used in dual-source pulmonary computed tomography (CT).
Materials and Methods
Dual-source dual-energy (DSDE) and single-source (SS) scans taken at multiple energy
levels and scan settings were acquired for quantitative comparison using anesthetized
ovine (n = 6), swine (n = 13), and a lung phantom. Data were evaluated for the lung, inferior vena cava,
and tracheal segments. To minimize the effect of cross-scatter, the phantom scans
in the DSDE mode were obtained by reducing the current of one of the tubes to near
zero.
Results
A significant shift in mean HU values in the tracheal regions of animals and the phantom
is observed, with values consistently closer to −1000 HU in DSDE mode. HU values associated
with SS mode demonstrated a positive shift of up to 32 HU. In vivo tracheal air measurements
demonstrated considerable variability with SS scanning, whereas these values were
more consistent with DSDE imaging. Scatter effects in the lung parenchyma differed
from adjacent tracheal measures.
Conclusion
Data suggest that the scatter correction introduced into the dual-energy mode of imaging
has served to provide more accurate CT lung density measures sought to quantitatively
assess the presence and distribution of emphysema in COPD subjects. Data further suggest
that CT images, acquired without adequate scatter correction, cannot be corrected
by linear algorithms given the variability in tracheal air HU values and the independent
scatter effects on lung parenchyma.
Key Words
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Article info
Publication history
Accepted:
April 9,
2013
Received:
October 17,
2012
Identification
Copyright
© 2013 AUR. Published by Elsevier Inc. All rights reserved.
