Signal separability is an important factor in the differentiation of materials in spectral computed tomography. In this work, we evaluated the separability of two such materials, iodine and gadolinium with k-edges of 33.1 keV and 50.2 keV, respectively, with an investigational photon-counting CT scanner (Siemens, Germany). A 20 cm water equivalent phantom containing vials of iodine and gadolinium was imaged. Two datasets were generated by either varying the amount of contrast (iodine – 0.125-10 mg/mL, gadolinium 0.125-12 mg/mL) or by varying the tube current (50-300 mAs). Regions of interest were drawn within vials and then used to construct multivariate Gaussian models of signal. We evaluated three separation metrics using the Gaussian models: the area under the curve (AUC) of the receiver operating characteristic curve, the mean Mahalanobis distance, and the Jaccard index. For the dataset with varying contrast, all three metrics showed similar trends by indicating a higher separability when there was a large difference in signal magnitude between iodine and gadolinium. For the dataset with varying tube current, AUC showed the least variation due to change in noise condition and had a higher coefficient of determination (0.99, 0.97) than either mean Mahalanobis distance (0.69, 0.62) or Jaccard index (0.80, 0.75) when compared to material decomposition results for iodine or gadolinium respectively.
In this work, we define a theoretical approach to characterizing the signal-to-noise ratio (SNR) of multi-channeled systems such as spectral computed tomography image series. Spectral image datasets encompass multiple near-simultaneous acquisitions that share information. The conventional definition of SNR is applicable to a single image and thus does not account for the interaction of information between images in a series. We propose an extension of the conventional SNR definition into a multivariate space where each image in the series is treated as a separate information channel thus defining a spectral SNR matrix. We apply this to the specific case of contrast-to-noise ratio (CNR). This matrix is able to account for the conventional CNR of each image in the series as well as a covariance weighted CNR (Cov-CNR), which accounts for the covariance between two images in the series. We evaluate this experimentally with data from an investigational photon-counting CT scanner (Siemens).
In photon-counting CT, detector energy thresholds directly affect image quality attributes such as contrast and noise. The purpose of this study was to identify optimum energy thresholds using a comprehensive virtual clinical trial platform. The virtual trial was done using a computational, anthropomorphic phantom and a photon-counting CT simulator. The phantom (adult male, 50th percentile body mass index) was chosen from the library of extended cardiac-torso (XCAT) phantoms. A vessel growth algorithm was used to model detailed hepatic arteries within the liver. Five computational lesions were inserted to the liver. The phantom was “imaged” with iodinated contrast at three contrast phases, i.e. arterial, portal venous, and delayed phases. All virtual sinograms were simulated using a validated CT simulator (DukeSim) modeling a prototype photon-counting scanner (CounT, Siemens Healthcare). The simulator included an energy- and threshold-dependent detector model accounting for X-ray and electronic effects and noise. At each contrast phase, the phantom was imaged at 35 detector energy threshold combinations with five lower-energy thresholds (20-40 keV) and seven upper-energy thresholds (50-80 keV). For each scan, two “threshold” images (photons detected beyond a threshold) and one “bin” image (photons detected between the two thresholds) were acquired. Noise magnitude, image contrast, and contrast-to-noise ratio (CNR) were measured in the aorta and the liver lesions. Optimum energy thresholds were identified as those yielding higher CNRs. From the simulations, noise magnitude was found to increase and CNR decrease with the increase in energy thresholds. Keeping the low threshold constant, the noise decreased and CNR increased with the increase in the upper energy threshold. Therefore, our results suggest that a combination of a lower threshold at 20-30 keV and upper threshold at 80 keV maximize the image quality. This study demonstrates an application of a virtual trial platform to quantify and optimize the quality of photon-counting CT.
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.
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.
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 (CTDIvol: 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.