Purpose: The purpose of this study was to detect and analyze anomalies between a large number of computed tomography (CT) scanners, tracked over time, utilized to collect human pulmonary CT data for a national multicenter study: chronic obstructive pulmonary disease genetic epidemiology study (COPDGene).
Methods: A custom designed CT reference standard "Test Object" has been developed to evaluate the relevant differences in CT attenuation between CT scanners in COPDGene. The materials used in the Test Object to assess CT scanner accuracy and precision included lung equivalent foam (-856 HU), internal air (-1000 HU), water (0 HU), and acrylic (120 HU). Nineteen examples of the Test Object were manufactured. Initially, all Test Objects were scanned on the same CT scanner before the Test Objects were sent to the 20 specific sites and 42 individual CT scanners that were used in the study. The Test Objects were scanned over 17 months while the COPDGene study continued to recruit subjects. A mixed linear effect statistical analysis of the CT scans on the 19 Test Objects was performed. The statistical model reflected influence of reconstruction kernels, tube current, individual Test Objects, CT scanner models, and temporal consistency on CT attenuation.
Results: Depending on the Test Object material, there were significant differences between reconstruction kernels, tube current, individual Test Objects, CT scanner models, and temporal consistency. The two Test Object materials of most interest were lung equivalent foam and internal air. With lung equivalent foam, there were significant (p < 0.05) differences between the Siemens B31 (-856.6, ±0.82; mean ± SE) and the GE Standard (-856.6 ± 0.83) reconstruction kernel relative to the Siemens B35 reference standard (-852.5 ± 1.4). Comparing lung equivalent foam attenuation there were also significant differences between CT scanner models (p < 0.01), tube current (p < 0.005), and in temporal consistency (p < 0.005) at individual sites. However, there were no significant effects measurable using different examples of the Test Objects at the various sites compared to the reference scans of the 19 Test Objects. For internal air, significant (p < 0.005) differences were found between all reconstruction kernels (Siemens B31, GE Standard, and Phillips B) compared to the reference standard. There were significant differences between CT models (p < 0.005), and tube current (p < 0.005). There were no significant effects measurable using different examples of the Test Objects at the various sites compared to the reference scans of the 19 Test Objects. Differences, across scanners, between external air and internal air measures in this simple (relative to the in vivo lung) test object varied by as much as 15 HU.
Conclusions: The authors conclude that the Test Object designed for this study was able to detect significant effects regarding individual CT scanners that altered the CT attenuation measurements relevant to the study that are used to determine lung density. Through an understanding of individual scanners, the Test Object analysis can be used to detect anomalies in an individual CT scanner and to statistically model out scanner differences and individual scanner changes over time in a large multicenter trial.