  Consistency and duplicability in Computed Tomography (CT) output is essential
to quantitative imaging for lung cancer detection and monitoring. This study of
CT-detected lung nodules investigated the reproducibility of volume-, density-,
and texture-based features (outcome variables) over routine ranges of
radiation-dose, reconstruction kernel, and slice thickness. CT raw data of 23
nodules were reconstructed using 320 acquisition/reconstruction conditions
(combinations of 4 doses, 10 kernels, and 8 thicknesses). Scans at 12.5%, 25%,
and 50% of protocol dose were simulated; reduced-dose and full-dose data were
reconstructed using conventional filtered back-projection and
iterative-reconstruction kernels at a range of thicknesses (0.6-5.0 mm).
Full-dose/B50f kernel reconstructions underwent expert segmentation for
reference Region-Of-Interest (ROI) and nodule volume per thickness; each ROI
was applied to 40 corresponding images (combinations of 4 doses and 10
kernels). Typical texture analysis metrics (including 5 histogram features, 13
Gray Level Co-occurrence Matrix, 5 Run Length Matrix, 2 Neighboring Gray-Level
Dependence Matrix, and 2 Neighborhood Gray-Tone Difference Matrix) were
computed per ROI. Reconstruction conditions resulting in no significant change
in volume, density, or texture metrics were identified as "compatible pairs"
for a given outcome variable. Our results indicate that as thickness increases,
volumetric reproducibility decreases, while reproducibility of histogram- and
texture-based features across different acquisition and reconstruction
parameters improves. In order to achieve concomitant reproducibility of
volumetric and radiomic results across studies, balanced standardization of the
imaging acquisition parameters is required.
