Optimization of Computed Tomography Protocols: Limitations of a Methodology Employing a Phantom with Location-Known Opacities

Abstract

This study aimed to determine if phantom-based methodologies for optimization of hepatic lesion detection with computed tomography (CT) require randomization of lesion placement and inclusion of normal images. A phantom containing fixed opacities of varying size (diameters, 2.4, 4.8, and 9.5 mm) was scanned at various exposure and slice thickness settings. Two image sets were compared: All images in the first image set contained opacities with known location; the second image set contained images with opacities in random locations. Following Institutional Review Board approval, nine experienced observers scored opacity visualization using a 4-point confidence scale. Comparisons between image sets were performed using Spearman, Kappa, and Wilcoxon techniques. Observer scores demonstrated strong correlation between both approaches when all opacity sizes were combined (r=0.92, p<0.0001), for the 9.5 mm opacity (r=0.96, p<0.0001) and for the 2.4 mm opacity (r=0.64, p<0.05). There was no significant correlation for the 4.8 mm opacity. A significantly higher sensitivity score for the known compared with the unknown location was found for the 9.5 mm opacity and 4.8 mm opacity for a single slice thickness and exposure condition (p<0.05). Phantom-based optimization of CT hepatic examinations requires randomized lesion location when investigating challenging conditions; however, a standard phantom with fixed lesion location is suitable for the optimization of routine liver protocols. The development of more sophisticated phantoms or methods than those currently available is indicated for the optimization of CT protocols for diagnostic tasks involving the detection of subtle change.

A novel objective method for assessing high‐contrast spatial resolution in CT based on the Rayleigh criterion

“…Previous research indicated that variation in subjective evaluation results mainly depends on observer expertise. Therefore, five junior QA testers and three experienced medical imaging QA professionals were invited to form volunteer and expert groups for comparison.…”