Purpose:Radiotherapy (RT) that selectively avoids irradiating highly functional
lung
regions may reduce pulmonary toxicity, which is substantial in lung
cancer RT.
Single-energy computed
tomography
(CT)
pulmonary perfusion imaging has several advantages
(e.g., higher resolution) over other modalities and has great potential for
widespread clinical implementation, particularly in RT. The purpose of this study
was to establish proof-of-principle for single-energy CT perfusion
imaging.Methods:Single-energy CT perfusion imaging is based on the following:
(1) acquisition of end-inspiratory breath-hold CT scans before and
after intravenous injection of iodinated contrast agents, (2)
deformable image
registration (DIR) for spatial mapping of
those two CT
image
data sets,
and (3) subtraction of the precontrast image
data set
from the postcontrast image
data set,
yielding a map of regional Hounsfield unit (HU) enhancement, a surrogate for
regional perfusion. In a protocol approved by the institutional animal care and
use committee, the authors acquired CT scans in the prone position for a total of 14
anesthetized canines (seven canines with normal lungs and seven
canines with diseased lungs). The elastix algorithm was used for DIR. The accuracy
of DIR was evaluated based on the target registration error (TRE) of 50 anatomic
pulmonary landmarks per subject for 10 randomly selected subjects as well as on
singularities (i.e., regions where the displacement vector field is not
bijective). Prior to perfusion computation, HUs of the precontrast end-inspiratory
image were corrected for variation in the lung inflation level
between the precontrast and postcontrast end-inspiratory CT scans, using a
model built from two additional precontrast CT scans at
end-expiration and midinspiration. The authors also assessed spatial heterogeneity
and gravitationally directed gradients of regional perfusion for normal
lung
subjects and diseased lung subjects using a two-sample two-tailed
t-test.Results:The mean TRE (and standard deviation) was 0.6 ± 0.7 mm (smaller than the voxel
dimension) for DIR between pre contrast and postcontrast end-inspiratory
CT
image
data sets.
No singularities were observed in the displacement vector fields. The mean HU
enhancement (and standard deviation) was 37.3 ± 10.5 HU for normal lung subjects and 30.7
± 13.5 HU for diseased lung subjects. Spatial heterogeneity of regional perfusion was
found to be higher for diseased lung subjects than for normal lung subjects, i.e., a
mean coefficient of variation of 2.06 vs 1.59 (p = 0.07). The
average gravitationally directed gradient was strong and significant
(R2 = 0.99, p < 0.01) for
normal lung dogs, whereas it was moderate and nonsignificant
(R2 = 0.61, p = 0.12) for diseased
lung
dogs.Conclusions:This canine study demonstrated the accuracy of DIR with subvoxel TREs on average,
higher spatial heterogeneity of regional perfusion for diseased
lung
subjects than for normal lung subjects, and a strong gravitationally directed gradient
for normal lung subjects, providing p...