Impact of image formation factors on material discrimination in spectral CT.

The accuracy of material decomposition in spectral computed tomography (CT) depends on the information quality captured in image acquisition, a factor that cannot be adequately assessed using conventional image quality metrologies due to the multi-energy nature of spectral CT. This work used metrologies specific to spectral CT to evaluate the impact of acquisition conditions on the quality of spectral CT images and accuracy of material decomposition techniques.Computational phantoms were created with cylindrical shapes and variable sizes (20-40 cm), containing inserts of iodine and gadolinium (1-8 mg ml). The phantoms were imaged using a validated CT simulator modeling a clinical photon-counting CT scanner. The acquisitions were done at different detector energy thresholds (50-90 keV) and tube currents (25-250 mAs). The images were used to develop and train a data-driven material identification and quantification algorithm. Two spectral metrologies, multivariate contrast-to-noise ratio (CNR) and separability index, were used to characterize the impact of energy threshold, tube current, phantom size, and material concentration on signal quality. The results were interpreted in terms of figures of merit of accuracy for classification and mean absolute error (MAE) and root mean squared error (RMSE) for regression.. Signal quality for iodine and gadolinium was maximized with a low energy threshold, high tube current, and small phantom size. While conventional CNR terms predicted variable image quality for two-thirds of all conditions, multivariate CNR was above 10 for half of those. Separability index showed that for a phantom size greater than 30 cm, a minimum of 75-110 mAs is required to separate 2 mg mlof iodine and gadolinium. For both classification and regression tasks, a random forest model with a local statistics dataset provided the best performance. Across conditions, classification performance was 0.66-0.99 for I accuracy, 0.72-0.99 for Gd accuracy. Regression performance was 0.02-0.91 mg mlI and 0.02-0.59 mg mlGd for MAE and 0.11-1.08 mg mlI and 0.07-0.76 mg mlGd for RMSE.Multivariate CNR and separability index metrologies can predict material decomposition performance. Theses metrics demonstrated that the decomposition of iodine and gadolinium have higher separability when the acquisition is done at a lower energy threshold, with a higher tube current, and when the imaged object has a smaller size. Object size had the largest impact on metrics and decomposition performance.