The purpose of this study was to evaluate the potential of a prototype photon-counting CT system scanner to characterize liver texture and lung lesion morphology features. We utilized a multi-tiered phantom (Mercury Phantom 4.0) to characterize the noise power spectrum and task-transfer functions of both conventional and photoncounting modes on the scanner. Using these metrics, we blurred three textures models and fifteen model lesions for four doses (CTDI<sub>vol</sub>: 4, 8, 16, 24 mGy), and three slice thicknesses (1.6, 2.5, 4 mm), for a total of 12 imaging conditions. Twenty texture features and twenty-one morphology features were evaluated. Performance was characterized in terms of accuracy (percent bias of features across different conditions) and variability (coefficient of variation of features due to repeats and averaged across conditions). Compared to conventional CT, photon-counting CT had comparable accuracy and variability for texture features. For morphology features, photon-counting CT had comparable accuracy and less variability than conventional CT. For both imaging modes, change in dose showed slight variation in features and increasing slice thickness caused a monotonic change with feature dependent directionality. Photon-counting CT can improve the characterization of morphology features without compromising texture features.
The purpose of this study was to examine the effect of energy threshold selection on the quantification of contrast agents in photon-counting CT (PCCT). A phantom was devised consisting of vials of iodine (4, 8, 16 mg/mL), gadolinium (4, 8, 16 mg/mL), and bismuth (5, 10, 15 mg/mL) within a cylindrical water container. The phantom was scanned on a prototype photon-counting CT scanner. The detected photons were binned into two energy bins using a fixed lower threshold of 20 keV and an upper threshold that varied between 50 to 90 keV. An image containing all the spectral information (threshold 1) was examined along with both binned images. Images were evaluated for the mean and standard deviation of CT number in each vial and contrast-to-noise ratio (CNR) for each concentration. CT number values in the threshold 1 image remained mostly unchanged as energy threshold was increased. Vials of iodine and gadolinium had slightly higher CT numbers in lower energy bin images than the threshold 1 images, but the percentage difference varied slightly (6-37% for iodine and 5-22% for gadolinium) with energy threshold. In higher energy bin images, CT numbers were lower (20-68% for iodine and 10-59% for gadolinium) than threshold 1 and the difference decreased with increasing energy threshold. For bismuth, the percentage difference in the lower bin decreased (by 11-19%) with energy level while it increased (by 18-23%) in the upper bin. CNR varied only slightly in the lower energy bins and decreased with increasing energy threshold for all materials.
In this study, we examined image quality in photon-counting CT images due to variation in energy thresholds. Images of an ACR quality control phantom were acquired using a prototype photon-counting CT scanner with two variable energy thresholds. The lower threshold, which varied between 20 to 50 keV, and the higher threshold, which varied between 50 to 90 keV, were used to separate the data into two energy bins. This produced a total of four images: threshold 1, containing signal between the lower threshold and the maximal value, threshold 2, containing signal between the higher threshold and the maximal value, bin 1, containing signal between the lower and higher threshold, and bin 2, containing signal between the higher threshold and maximal value. Thirteen pairs of energy thresholds were evaluated spanning the entire energy threshold space. An automated program was used to analyze images for standard quality control metrics including noise measurement, resolution, low contrast detectability, and contrast-to-noise ratio (CNR). Metrics were compared between image types and across energy thresholds. Threshold 1 images showed the least variation despite change in thresholds. Increasing the higher threshold degraded image quality in threshold 2 and bin 2 images, but improved performance in bin 1 images. Increasing the lower threshold decreased performance for bin 1 images. Resolution was largely unaffected by change in energy threshold.