Standardizing CT lung density measure across scanner manufacturers

Abstract

Purpose: Computed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty. Methods: Data were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization. Results: We show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [–862.0 HU, –851.3 HU] before standardization, and [−859.0 HU, –853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of –855.9 HU with 95% CI of [–857.4 HU, –854.5 HU] based on the calibration and the uncertainty in the SRM certified density. Conclusions: This study provides a quantitative assessment of the variations expected in CT ...

Total Airway Count on Computed Tomography and the Risk of Chronic Obstructive Pulmonary Disease Progression. Findings from a Population-based Study

“…We note that our image acquisition protocol was based on the image acquisition protocol used in the COPDGene cohort study (18). Furthermore, we acknowledge efforts of the Quantitative Imaging Biomarkers Alliance Lung Density Committee to conduct phantom studies to assess the measurement variation attributed to different CT manufacturers (26). Reducing this nonbiologic CT measurement variability will allow for more reproducible measurements, which is clearly important for future multicenter studies.…”

Autocalibration method for non-stationary CT bias correction

“…This transition has heretofore been limited by inter- and intra-scanner variability which has inhibited efforts to perform and interpret large-scale cross-sectional and longitudinal studies (Chen-Mayer et al, 2017; Parr et al, 2004). Any observed variability in such efforts has been ascribed to an inseparable admixture of poor standardization and biology.…”

“…One of those is Chen-Mayer et al (2017), in which a phantom study provides an assessment of the accuracy and precision of the density metrics across platforms due to machine calibration. This study, however, does not consider the potential effects of non-stationary noise and spatially variant biases as the ones observed in Parr et al (2004).…”

The Modern Art of Reading Computed Tomography Images of the Lungs: Quantitative CT

“…Although there are no formal standards yet for CT imaging of the lung, there are new working groups that aim to better understand and minimize this interscanner variability, such as the Quantitative Imaging Biomarker Alliance (QIBA) with the Radiological Society of North America (RSNA) [15]. The SubPopluations and Intermediate Outcome Measures In COPD (SPIROMICS) multi-site, cohort study has also recently outlined the best practice for QCT imaging for multi-site studies in which images were acquired using multiple CT scanner makes and models [16]; a standardized imaging protocol was detailed for various CT scanners, where spatial (1-mm slices), temporal resolution, use of a standard CTDvol across body sizes, and a particular reconstruction kernel for the various scanners was provided.…”