Imaging of hypodense gliotic lesions in photon counting computed tomography using virtual monoenergetic images.

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.

Photon counting computed tomography of in-stent-stenosis in a phantom: Optimal virtual monoenergetic imaging in ultra high resolution.

Rationale and objectives: 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. Materials and methods: 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. Results: 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. Conclusions: 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.

Imaging of intracranial hemorrhage in photon counting computed tomography using virtual monoenergetic images.

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.

Nonenhanced Photon Counting CT of the Head : Impact of the keV Level, Iterative Reconstruction and Calvaria on Image Quality in Monoenergetic Images.

PURPOSE: Nonenhanced computed tomography (CT) of the head is among the most commonly performed CT examinations. The spectral information acquired by photon counting CT (PCCT) allows generation of virtual monoenergetic images (VMI). At the same time, image noise can be reduced using quantum iterative reconstruction (QIR). In this study, the image quality of VMI was evaluated depending on the keV level and the QIR level. Furthermore, the influence of the cranial calvaria was investigated to determine the optimal reconstruction for clinical application. METHODS: A total of 51 PCCT (NAEOTOM Alpha, Siemens Healthineers, Erlangen, Germany) of the head were retrospectively analyzed. In a quantitative analysis, gray and white matter ROIs were evaluated in different brain areas at all available keV levels and QIR levels with respect to signal, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The distance to the cranial calvaria of the ROIs was included in the analysis. This was followed by a qualitative reading by five radiologists including experienced neuroradiologists. RESULTS: In most ROIs, signal and noise varied significantly between keV levels (p < 0.0001). The CNR had a focal maximum at 66 keV and an absolute maximum at higher keV, slightly differently located depending on ROI and QIR level. With increasing QIR level, a significant reduction in noise was achieved (p < 0.0001) except just beneath the cranial calvaria. The cranial calvaria had a strong effect on the signal (p < 0.0001) but not on gray and white matter noise. In the qualitative reading, the 60 keV VMI was rated best. CONCLUSION: In nonenhanced PCCT of the head the selected keV level of the VMI and the QIR level have a crucial influence on image quality in VMI. The 60 keV and 66 keV VMI with high QIR level provided optimal subjective and objective image quality for clinical use. The cranial calvaria has a significant influence on the visualization of the adjacent brain matter; currently, this substantially limits the use of low keV VMIs (< 60 keV).

Dedicated virtual non-contrast images adapted for liver tissue in clinical photon counting CT improve virtual non-contrast imaging in various organs beyond the liver.

PURPOSE: Purpose of this study is to re-evaluate the accuracy and diagnostic reliability of virtual non-contrast (VNC) images acquired with the photon-counting computed tomography (PCCT) after an update of the CT scanner software. METHODS: Fifty-four patients were retrospectively enrolled. VNC images were reconstructed from true non-contrast (TNC) images (VNCn) and contrast-enhanced images in portal venous contrast phase (VNCv). Additionally, a liver-specific VNC (VNCl) was assessed. Quantitative image properties of VNC and TNC images were compared and consistency between VNC images was evaluated. Regions of interest were drawn in the liver, spleen, renal cortex, aorta, muscle and subcutaneous fat. RESULTS: Attenuation values on all VNC images differed significantly from TNC images in the liver, renal cortex, aorta and fat. A mean offset of <10HU between TNC and all VNC images was found in the liver, spleen and muscle. The comparison of TNC and VNCl images revealed an offset < 10HU in fat. Differences ≤ 10HU between TNC and VNCv and between TNC and VNCl were found in 68%, respectively in 75%. Differences ≤ 15HU were found in 79%, respectively in 92% of all measurements. Differences ≤ 10HU between TNC and VNCn were found in 79% and differences ≤ 15HU in 85%. CONCLUSION: Although there are statistically significant differences between HU values measured on TNC and VNC images in certain tissues, the minor offsets measured in liver and spleen suggest a good clinical applicability of VNCv and VNCl images. The significantly lower offset in subcutaneous fat on VNCl images suggests a superiority for measurements in adipose tissues.

Coronary stent imaging in photon counting computed Tomography: Optimization of reconstruction kernels in a phantom.

PURPOSE: Imaging stents and in-stent stenosis remains a challenge in coronary computed tomography angiography (CCTA). In comparison to conventional Computed Tomography, Photon Counting CT (PCCT) provides decisive clinical advantages, among other things by providing low dose ultra-high resolution imaging of coronary arteries. This work investigates the image quality in CCTA using clinically established kernels and those optimized for the imaging of cardiac stents in PCCT, both for in-vitro stent imaging in 400 µm standard resolution mode (SRM) and 200 µm Ultra High Resolution Mode (UHR). METHODS: Based on experimental scans, vascular reconstruction kernels (Bv56, Bv64, Bv72) were optimized. In an established phantom, 10 different coronary stents with 3 mm diameter were scanned in the first clinically available PCCT. Scans were reconstructed with clinically established and optimized kernels. Four readers measured visible stent lumen, performed ROI-based density measurements and rated image quality. RESULTS: Regarding the visible stent lumen, UHR is significantly superior to SRM (p < 0.001). In all levels, the optimized kernels are superior to the clinically established kernels (p < 0.001). One optimized kernel showed a significant reduction of noise compared to the clinically established kernels. Overall image quality is improved with optimized kernels. CONCLUSIONS: In a phantom study PCCT UHR with optimized kernels for stent imaging significantly improves the ability to assess the in-stent lumen of small cardiac stents. We recommend using UHR with an optimized sharp vascular reconstruction kernel (Bv72uo) for imaging of cardiac stent.

Evaluation of the clinical performance of an AI-based application for the automated analysis of chest X-rays.

The AI-Rad Companion Chest X-ray (AI-Rad, Siemens Healthineers) is an artificial-intelligence based application for the analysis of chest X-rays. The purpose of the present study is to evaluate the performance of the AI-Rad. In total, 499 radiographs were retrospectively included. Radiographs were independently evaluated by radiologists and the AI-Rad. Findings indicated by the AI-Rad and findings described in the written report (WR) were compared to the findings of a ground truth reading (consensus decision of two radiologists after assessing additional radiographs and CT scans). The AI-Rad can offer superior sensitivity for the detection of lung lesions (0.83 versus 0.52), consolidations (0.88 versus 0.78) and atelectasis (0.54 versus 0.43) compared to the WR. However, the superior sensitivity is accompanied by higher false-detection-rates. The sensitivity of the AI-Rad for the detection of pleural effusions is lower compared to the WR (0.74 versus 0.88). The negative-predictive-values (NPV) of the AI-Rad for the detection of all pre-defined findings are on a high level and comparable to the WR. The seemingly advantageous high sensitivity of the AI-Rad is partially offset by the disadvantage of a high false-detection-rate. At the current stage of development, therefore, the high NPVs may be the greatest benefit of the AI-Rad giving radiologists the possibility to re-insure their own negative search for pathologies and thus boosting their confidence in their reports.

Photon-counting CT Material Decomposition: Initial Experience in Assessing Adrenal Adenoma.

See also the editorial by Pourmorteza in this issue.

Photon Counting CT Angiography of the Head and Neck: Image Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions.

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 CTDIvol 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.

Image-Quality Assessment of Polyenergetic and Virtual Monoenergetic Reconstructions of Unenhanced CT Scans of the Head: Initial Experiences with the First Photon-Counting CT Approved for Clinical Use.

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.