In 2021, the first clinical photon-counting CT (PCCT) was introduced. The purpose of this study is to evaluate the image quality of polyenergetic and virtual monoenergetic reconstructions in unenhanced PCCTs of the head. A total of 49 consecutive patients with unenhanced PCCTs of the head were retrospectively included. The signals ± standard deviations of the gray and white matter were measured at three different locations in axial slices, and a measure of the artifacts below the cranial calvaria and in the posterior fossa between the petrous bones was also obtained. The signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated for all reconstructions. In terms of the SNRs and CNRs, the polyenergetic reconstruction is superior to all virtual monoenergetic reconstructions (p < 0.001). In the MERs, the highest SNR is found in the 70 keV MER, and the highest CNR is in the 65 keV MER. In terms of artifacts below the cranial calvaria and in the posterior fossa, certain MERs are superior to polyenergetic reconstruction (p < 0.001). The PCCT provided excellent image contrast and low-noise profiles for the differentiation of the grey and white matter. Only the artifacts below the calvarium and in the posterior fossa still underperform, which is attributable to the lack of an artifact reduction algorithm in image postprocessing. It is conceivable that the usual improvements in image postprocessing, especially with regard to glaring artifacts, will lead to further improvements in image quality.

Background: The purpose of the present study was the evaluation of the image quality of polyenergetic and monoenergetic reconstructions (PERs and MERs) of CT angiographies (CTAs) of the head and neck acquired with the novel photon counting CT (PCCT) method in clinical routine. Methods: Thirty-seven patients were enrolled in this retrospective study. Quantitative image parameters of the extracranial, intracranial and cerebral arteries were evaluated for the PER and MER (40–120 keV). Additionally, two radiologists rated the perceived image quality. Results: The mean CTDI_vol used in the PCCT was 8.31 ± 1.19 mGy. The highest signal within the vessels was detected in the 40 keV MER, whereas the lowest noise was detected in the 115 keV MER. The most favorable contrast-to-noise-ratio (CNR) and signal-to-noise-ratio (SNR) were detected in the PER and low keV MER. In the qualitative image analysis, the PER was superior to the MER in all rated criteria. For MER, 60–65 keV was rated as best image quality. Conclusion: Overall, PCCT offers excellent image quality for CTAs of the head and neck. At the current state, the PER of the PCCT seems to be the most favorable reconstruction for diagnostic reporting.

Coronary computed tomography angiography (CCTA) is becoming increasingly important for the diagnostic workup of coronary artery disease, nevertheless, imaging of in-stent stenosis remains challenging. For the first time, spectral imaging in Ultra High Resolution (UHR) is now possible in clinically available photon counting CT. The aim of this work is to determine the optimal virtual monoenergetic image (VMI) for imaging in-stent stenoses in cardiac stents. 6 stents with inserted hypodense stenoses were scanned in an established phantom in UHR mode. Images were reconstructed with 3 different kernels for spectral data (Qr56, Qr64, Qr72) with varying levels of sharpness. Based on region of interest (ROI) measurements image quality parameters including contrast-to-noise ratio (CNR) were analyzed for all available VMI (40 keV–190 keV). Finally, based on quantitative results and VMI used in clinical routine, a set of VMI was included in a qualitative reading. CNR showed significant variations across different keV levels (p < 0.001). Due to reduced noise there was a focal maximum in the VMI around 65 keV. The peak values were observed for kernel Qr56 at 116 keV with 19.47 ± 8.67, for kernel Qr64 at 114 keV with 13.56 ± 6.58, and for kernel Qr72 at 106 keV with 12.19 ± 3.25. However, in the qualitative evaluation the VMI with lower keV (55 keV) performed best. Based on these experimental results, a photon counting CCTA in UHR with stents should be reconstructed with the Qr72 kernel for the assessment of in-stent stenoses, and a VMI 55 keV should be computed for the evaluation.

Purpose: To determine the optimal virtual monoenergetic image (VMI) for detecting and assessing intracranial hemorrhage in unenhanced photon counting CT of the head based on the evaluation of quantitative and qualitative image quality parameters. Methods: Sixty-three patients with acute intracranial hemorrhage and unenhanced CT of the head were retrospectively included. In these patients, 35 intraparenchymal, 39 intraventricular, 30 subarachnoidal, and 43 subdural hemorrhages were selected. VMIs were reconstructed using all available monoenergetic reconstruction levels (40–190 keV). Multiple regions of interest measurements were used for evaluation of the overall image quality, and signal, noise, signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR) of intracranial hemorrhage. Based on the results of the quantitative analysis, specific VMIs were rated by five radiologists on a 5-point Likert scale. Results: Signal, noise, SNR, and CNR differed significantly between different VMIs (p < 0.001). Maximum CNR for intracranial hemorrhage was reached in VMI with keV levels > 120 keV (intraparenchymal 143 keV, intraventricular 164 keV, subarachnoidal 124 keV, and subdural hemorrhage 133 keV). In reading, no relevant superiority in the detection of hemorrhage could be demonstrated using VMIs above 66 keV. Conclusion: For the detection of hemorrhage in unenhanced CT of the head, the quantitative analysis of the present study on photon counting CT is generally consistent with the findings from dual-energy CT, suggesting keV levels just above 120 keV and higher depending on the location of the hemorrhage. However, on the basis of the qualitative analyses, no reliable statement can yet be made as to whether an additional VMI with higher keV is truly beneficial in everyday clinical practice.

Objectives: Glioses appear as hypodense lesions in non-contrast CT examinations of the head. Photon counting CT (PCCT) enables the calculation of virtual monoenergetic images (VMI). The aim of this study is to investigate in which VMI hypodense gliotic lesions can be delineated best. Materials and methods: 35 patients with an MRI-confirmed gliotic lesion and a non-contrast PCCT of the head were retrospectively included. All available VMI from 40 keV to 190 keV were calculated. In a quantitative analysis, conventional image quality parameters were calculated, in particular the contrast-to-noise ratio (CNR) of the hypodense lesion compared to the white matter. In a qualitative analysis, selected VMI were rated by experienced radiologists. Results: The absolute maximum of CNR was 8.12 ± 5.64 in the VMI 134 keV, in post hoc testing, there were significant differences in comparison to VMI with keV ≤110 and keV ≥180 (corrected p < .05). In the qualitative analysis, there were only very slight differences in the rating of the VMI with 66 keV, 80 keV, 100 keV, and 134 keV with overall low agreement between the readers. Conclusions: The quantitative superiority of VMI 134 keV for the delineation of hypodense gliotic lesions did not translate into a superiority in the qualitative analysis. Therefore, it remains uncertain if the reconstruction of a high keV VMIs for the detection of hypodense gliotic lesions is useful in everyday clinical practice. However, more studies, are necessary to further assess this issue.