Vaishnav, J. Y.

PurposeLow contrast detectability (LCD) is a metric of fundamental importance in computed tomography (CT) imaging. In spite of this, its measurement is challenging in the context of nonlinear data processing. We introduce a new framework for objectively characterizing LCD with a single scan of a special‐purpose phantom and automated analysis software. The output of the analysis software is a “machine LCD” metric which is more representative of LCD than contrast‐noise ratio (CNR). It is not intended to replace human observer or model observer studies.MethodsFollowing preliminary simulations, we fabricated a phantom containing hundreds of low‐contrast beads. These beads are acrylic spheres (1.6 mm, net contrast ~10 HU) suspended and randomly dispersed in a background matrix of nylon pellets and isoattenuating saline. The task was to search for and localize the beads. A modified matched filter was used to automatically scan the reconstruction and select candidate bead localizations of varying confidence. These were compared to bead locations as determined from a high‐dose reference scan to produce free‐response ROC curves. We compared iterative reconstruction (IR) and filtered backpropagation (FBP) at multiple dose levels between 40 and 240 mAs. The scans at 60, 120, and 180 mAs were performed three times each to estimate uncertainty.ResultsExperimental scans demonstrated the feasibility of our technique. Our metric for machine LCD was the area under the exponential transform of the FROC curve (AUC). AUC increased monotonically from 0.21 at 40 mAs to 0.84 at 240 mAs. The sample standard deviation of AUC was approximately 0.02. This measurement uncertainty in AUC corresponded to a change in tube current of 4% to 8%. Surprisingly, we found that AUCs for IR were slightly worse than AUCs for FBP. While the phantom was sufficient for these experiments, it contained small air bubbles and alternative fabrication methods will be necessary for widespread utilization.ConclusionsIt is feasible to measure machine LCD using a search task on a phantom with hundreds of beads and to obtain tight error bars using only a single scan. Our method could facilitate routine quality assurance or possibly enable comparisons between different protocols and scanners.

Background The rapid development and complexity of new x‐ray computed tomography (CT) technologies and the need for evidence‐based optimization of image quality with respect to radiation and contrast media dose call for an updated approach towards CT performance evaluation. Aims This report offers updated testing guidelines for testing CT systems with an enhanced focus on the operational performance including iterative reconstructions and automatic exposure control (AEC) techniques. Materials and Methods The report was developed based on a comprehensive review of best methods and practices in the scientific literature. The detailed methods include the assessment of 1) CT noise (magnitude, texture, nonuniformity, inhomogeneity), 2) resolution (task transfer function under varying conditions and its scalar reflections), 3) task‐based performance (detectability, estimability), and 4) AEC performance (spatial, noise, and mA concordance of attenuation and exposure modulation). The methods include varying reconstruction and tube current modulation conditions, standardized testing protocols, and standardized quantities and metrology to facilitate tracking, benchmarking, and quantitative comparisons. Results The methods, implemented in cited publications, are robust to provide a representative reflection of CT system performance as used operationally in a clinical facility. The methods include recommendations for phantoms and phantom image analysis. Discussion In line with the current professional trajectory of the field toward quantitation and operational engagement, the stated methods offer quantitation that is more predictive of clinical performance than specification‐based approaches. They can pave the way to approach performance testing of new CT systems not only in terms of acceptance testing (i.e., verifying a device meets predefined specifications), but also system commissioning (i.e., determining how the system can be used most effectively in clinical practice). Conclusion We offer a set of common testing procedures that can be utilized towards the optimal clinical utilization of CT imaging devices, benchmarking across varying systems and times, and a basis to develop future performance‐based criteria for CT imaging.