The purpose of this study was to evaluate whether the iodine contrast in blood and solid organs differs between men and women and to evaluate the effect of BMI, height, weight, and blood volume (BV) on sex-specific contrast in staging CT. Patients receiving a venous-phase thoracoabdominal Photon-Counting Detector CT (PCD-CT) scan with 100- or 120-mL CM between 08/2021 and 01/2022 were retrospectively included in this single-center study. Image analysis was performed by measuring iodine contrast in the liver, portal vein, spleen, left atrium, left ventricle, pulmonary trunk, ascending and descending aorta on spectral PCD-CT datasets. Univariable and multivariable analyses were performed to assess the impact of sex, age, BMI, height, weight, and BV on the iodine contrast. A total of 274 patients were included (mean age 68 years ± 12 SD, 168 men). Iodine contrast in organs and blood attenuation was significantly higher in women when using the same volume of CM. Sex, age, BMI, height, weight, and BV significantly influenced iodine contrast. After adjusting for confounding variables, sex remained a significant factor, with women having higher parenchymal and vascular iodine contrast. Standardized or weight-adapted use of CM in venous-phase thoracoabdominal CT scans results in significantly higher contrast in women compared to men. Customizing the CM dose to the patient’s BV could result in a similar contrast between sexes. This approach has the potential to reduce the amount of CM, resulting in cost savings, and to decrease the risks associated with CM, particularly for the female sex. Question This study addresses whether current standardized iodinated contrast media protocols lead to systematically higher iodine enhancement in women than in men during thoracoabdominal CT. Findings Women consistently show greater iodine enhancement in blood and abdominal organs compared to BMI-matched men when receiving identical volumes of contrast media. Clinical relevance Adjusting contrast media dosage based on blood volume in venous-phase CT scans could equalize parenchymal and intravascular iodine enhancement across sexes. This approach may reduce unnecessary contrast exposure in women, lower associated risks, and optimize healthcare resource allocation.

Introduction: The number of incidental renal lesions identified in CT scans of the abdomen is increasing. Objective: The aim of this study was to determine whether hyperdense renal lesions without solid components in a portal venous CT scan can be clearly classified as vascular or non-vascular by material decomposition into iodine and water. Methods: This retrospective single-center study included 26 patients (mean age 72 years ± 9; 16 male) with 42 hyperdense renal lesions (>20 HU) in a contrast-enhanced Photon-Counting Detector CT scan (PCD-CT) between May and December 2022. Spectral decomposition into virtual non-contrast (VNC) images and iodine quantification maps was performed, and HU values were quantified within the lesions. Further imaging and histopathological reports served as reference standards. Results: Mean VNC values were 55.7 (±24.2) HU for non-vascular and 32.2 (±11.1) HU for vascular renal lesions. Mean values in the iodine maps were 5.7 (±7.8) HU for non-vascular and 33.3 (±19.0) HU for vascular renal lesions. Using a threshold of >20.3 HU in iodine maps, a total of 7/8 (87.5%) vascular lesions were correctly identified. Conclusion: This proof-of-principle study suggests that the routine use of spectral information acquired in PCD-CT scans might be able to reduce the necessary workup for hyperdense renal lesions without solid components. Further studies with larger patient cohorts are necessary to validate the results of this study and to determine the usefulness of this method in clinical routine.

The study evaluates the impact of body mass index (BMI), heart rate and rhythm on coronary artery calcium scoring (CACS) derived from calcium-sensitive virtual non-contrast (VNC) series of photon-counting detector (PCD) computed tomography angiography (CTA) compared to true non-contrast (TNC) series. Patients who underwent cardiac imaging with TNC and CTA on a PCD-CT were included. Agatston scores from TNC and VNC images were used to assign CACS risk category. Analyses considered BMI, heart rhythm and heart rate. Distributions were tested for differences between TNC and VNC derived scores and their correlation was assessed. The final cohort included 88 patients. CACS on VNC showed an underestimation of TNC derived values on median Agatston score TNC = 542 (IQR 200–1294), on median Agatston score VNC = 449 (IQR 130–1183), p < 0.001, percentage difference − 11%. However, linear correlation coefficient was high (r 2 = 0.95), and the CAC severity was categorized equivalent in 80%. In approximately 11% of the study cohort, the misclassification of CAC severity could have potentially led to inappropriate treatment following established guidelines. An impact on the significance and extent of the difference in CACS for BMI > 28 kg/m 2 and heart rate groups > 69 bpm was found. VNC reconstructions from PCD-CT reliably estimates TNC CACS for BMI ≤ 28 kg/m 2 and heart rate ≤ 69 bpm in patients with severe coronary artery disease. Potential underestimation of risk category, especially with increased BMI and heart rate, must be considered for clinical decision making.

The aim of this retrospective study is to compare photon-counting detector computed tomography (PCD-CT) derived virtual non-contrast (VNC) images of the liver reconstructed from both arterial and portal venous phase using conventional and liver-specific VNC algorithm to true non-contrast images, in context of the body mass index (BMI). VNC images reconstructed from multiphase (non-contrast, arterial and portal venous phase) PCD-CT scans performed between April 2021 and February 2023 were analysed retrospectively. For each patient, four VNC series were generated: two series (arterial and portal venous) using a conventional VNC algorithm (VNCconvart; VNCconvpv) and two using a liver-specific “Liver VNC” algorithm (VNCLiverart; VNCLiverpv). Regions of interest were placed in the left and right liver lobes and in the spleen, avoiding large vessels and focal lesions. The VNC CT-values were then compared to those of the corresponding true non-contrast images (TNC). The subsequent analysis involved the calculation of both correlation and mean offsets. The median split was utilised to ascertain distinct cohorts of patients with elevated and reduced body mass indices. These cohorts were then subjected to a comparative analysis of attenuation values to discern potential disparities between them. The results were compared by using parametric and non-parametric tests; Pearson’s correlation coefficient was employed. Bland-Altman plots were utilised to visually assess the agreement between results and Passing-Bablok regression, thereby quantifying the observed agreement. The study population comprised 42 patients (mean age 70.0 ± 10.2 years, 33 males). Mean offsets between TNC and VNCconvart was 0.62 ± 5.23 HU, TNC-VNCconvpv 1.24 ± 6.67 HU, TNC-VNCLiverart -0.94 ± 5.59 and TNC-VNCLiverpv -0.35 ± 6.99 with no significant difference. Significant differences were found for VNCconvart, VNCconvpv and VNCLiverart images regarding spleen attenuation. Bland-Altman plots demonstrated good agreement and the absence of any systematic difference in liver attenuation. As for the TNC-VNCconvart, TNC-VNCconvpv, TNC-VNCLiverart and TNC-VNCLiverpv variables, strong correlations were obtained (Pearson’s coefficient: 0.79, 0.69, 0.79 and 0.7, all p < 0.001). The investigation revealed no statistically significant disparities between the BMI groups with respect to the mean offset of liver density (p-value:TNC-VNCconvart 0.51; VNCconvpv 0.61; VNCLiverart 0.68; VNCLiverpv 0.45). Furthermore, no significant offset between TNC and VNC images was detected within each BMI group. A Passing-Bablok regression analysis revealed no systematic or proportional difference between the two methods. It is evident that PCD-CT-derived VNC images generally constitute a corresponding alternative to TNC images. However, caution is advised in the interpretation of images, as there are outliers with differences exceeding 15 HU are present. In general, the mean values obtained from the analysis of, VNC images reconstructed from arterial and portal venous phases employing both the liver-specific and general VNC reconstruction algorithm did not demonstrate any clincially significant difference when compared with TNC images. Furthermore, no significant discrepancy was observed in the utilisation of the conventional and the liver-specific algorithm. The findings of this study demonstrated that, within the limitations of the study, the patients’ BMI did not have a significant impact on the VNC images.

The introduction of photon-counting detector CT (PCD-CT) marks a remarkable leap in innovation in CT imaging. The new detector technology allows X-rays to be converted directly into an electrical signal without an intermediate step via a scintillation layer and allows the energy of individual photons to be measured. Initial data show high spatial resolution, complete elimination of electronic noise, and steady availability of spectral image data sets. In particular, the new technology shows promise with respect to the imaging of osseous structures. Recently, PCD-CT was implemented in the clinical routine. The aim of this review was to summarize recent studies and to show our first experiences with photon-counting detector technology in the field of musculoskeletal radiology.We performed a literature search using Medline and included a total of 90 articles and reviews that covered recent experimental and clinical experiences with the new technology.In this review, we focus on (1) spatial resolution and delineation of fine anatomic structures, (2) reduction of radiation dose, (3) electronic noise, (4) techniques for metal artifact reduction, and (5) possibilities of spectral imaging. This article provides insight into our first experiences with photon-counting detector technology and shows results and images from experimental and clinical studies. · This review summarizes recent experimental and clinical studies in the field of photon-counting detector CT and musculoskeletal radiology.. · The potential of photon-counting detector technology in the field of musculoskeletal radiology includes improved spatial resolution, reduction in radiation dose, metal artifact reduction, and spectral imaging.. · PCD-CT enables imaging at lower radiation doses while maintaining or even enhancing spatial resolution, crucial for reducing patient exposure, especially in repeated or prolonged imaging scenarios.. · It offers promising results in reducing metal artifacts commonly encountered in orthopedic or dental implants, enhancing the interpretability of adjacent structures in postoperative and follow-up imaging.. · With its ability to routinely acquire spectral data, PCD-CT scans allow for material classification, such as detecting urate crystals in suspected gout or visualizing bone marrow edema, potentially reducing reliance on MRI in certain cases.. Bette S, Risch F, Becker J et al. Photon-counting detector CT - first experiences in the field of musculoskeletal radiology. Fortschr Röntgenstr 2024; DOI 10.1055/a-2312-6914.

Objective.Proton therapy administers a highly conformal dose to the tumour region, necessitating accurate prediction of the patient's 3D map of proton relative stopping power (RSP) compared to water. This remains challenging due to inaccuracies inherent in single-energy computed tomography (SECT) calibration. Recent advancements in spectral x-ray CT (xCT) and proton CT (pCT) have shown improved RSP estimation compared to traditional SECT methods. This study aims to provide the first comparison of the imaging and RSP estimation performance among dual-energy CT (DECT) and photon-counting CT (PCCT) scanners, and a pCT system prototype.Approach.Two phantoms were scanned with the three systems for their performance characterisation: a plastic phantom, filled with water and containing four plastic inserts and a wood insert, and a heterogeneous biological phantom, containing a formalin-stabilised bovine specimen. RSP maps were generated by converting CT numbers to RSP using a calibration based on low- and high-energy xCT images, while pCT utilised a distance-driven filtered back projection algorithm for RSP reconstruction. Spatial resolution, noise, and RSP accuracy were compared across the resulting images.Main results.All three systems exhibited similar spatial resolution of around 0.54 lp/mm for the plastic phantom. The PCCT images were less noisy than the DECT images at the same dose level. The lowest mean absolute percentage error (MAPE) of RSP,(0.28±0.07)%, was obtained with the pCT system, compared to MAPE values of(0.51±0.08)%and(0.80±0.08)%for the DECT- and PCCT-based methods, respectively. For the biological phantom, the xCT-based methods resulted in higher RSP values in most of the voxels compared to pCT.Significance.The pCT system yielded the most accurate estimation of RSP values for the plastic materials, and was thus used to benchmark the xCT calibration performance on the biological phantom. This study underlined the potential benefits and constraints of utilising such a novelex-vivophantom for inter-centre surveys in future.

PURPOSE:To assess the reliability of virtual non-contrast (VNC) derived coronary artery calcium quantities in relation to heart rate and the VNC algorithm used compared to reference true non-contrast (TNC), considering several clinically established acquisition modes. MATERIAL AND METHODS:An ad hoc built coronary phantom containing four calcified lesions and an iodinated lumen was scanned using three cardiac acquisition modes three times within an anthropomorphic cardiac motion phantom simulating different heart rates (0, 60, 80, 100 bpm) and reconstructed with a conventional (VNCconv) and a calcium-sensitive (VNCpc) VNC algorithm. TNC reference was scanned at 0 bpm with non-iodinated lumen. Calcium scores were assessed in terms of number of lesions detected, Agatston and volume scores and global noise was measured. Paired t-test and Wilcoxon test were performed to test measurements for significant difference. RESULTS:For both VNC algorithms used, calcium levels or noise were not significantly affected by heart rate. Measurements on VNCpc reconstructions best reproduced TNC results, but with increased variability (Agatston scores at 0 bpm for TNC, VNCconv, and VNCpc were 47.1 ± 1.1, 6.7 ± 2.8 (p < 0.001), and 45.3 ± 7.6 (p > 0.05), respectively). VNC reconstructions showed lower noise levels compared to TNC, especially for VNCpc (noiseheart on TNC, VNCconv and VNCpc at 0 bpm was 5.0 ± 0.4, 4.5 ± 0.2, 4.2 ± 0.2). CONCLUSION:No significant heart rate dependence of VNC-based calcium scores was observed in an intra-reconstruction comparison. VNCpc reproduces TNC scores better than VNCconv without significant differences and decreased noise, however, with an increasing average deviation with rising heart rates. VNC-based CACS should be used with caution as the measures show higher variability compared to reference TNC and therefore hold the potential of incorrect risk categorization.

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Objectives Aim of this study was to assess the value of virtual non-contrast (VNC) reconstructions in differentiating between adrenal adenomas and metastases on a photon-counting detector CT (PCD-CT). Material and methods Patients with adrenal masses and contrast-enhanced CT scans in portal venous phase were included. Image reconstructions were performed, including conventional VNC (VNC Conv ) and PureCalcium VNC (VNC PC ), as well as virtual monochromatic images (VMI, 40–90 keV) and iodine maps. We analyzed images using semi-automatic segmentation of adrenal lesions and extracted quantitative data. Logistic regression models, non-parametric tests, Bland–Altman plots, and a random forest classifier were used for statistical analyses. Results The final study cohort consisted of 90 patients (36 female, mean age 67.8 years [range 39–87]) with adrenal lesions (45 adenomas, 45 metastases). Compared to metastases, adrenal adenomas showed significantly lower CT-values in VNC Conv and VNC PC ( p = 0.007). Mean difference between VNC and true non-contrast (TNC) was 17.67 for VNC Conv and 14.85 for VNC PC . Random forest classifier and logistic regression models both identified VNC Conv and VNC PC as the best discriminators. When using 26 HU as the threshold in VNC Conv reconstructions, adenomas could be discriminated from metastases with a sensitivity of 86.7% and a specificity of 75.6%. Conclusion VNC algorithms overestimate CT values compared to TNC in the assessment of adrenal lesions. However, they allow a reliable discrimination between adrenal adenomas and metastases and could be used in clinical routine in near future with an increased threshold (e.g., 26 HU). Further (multi-center) studies with larger patient cohorts and standardized protocols are required. Clinical relevance statement VNC reconstructions overestimate CT values compared to TNC. Using a different threshold (e.g., 26 HU compared to the established 10 HU), VNC has a high diagnostic accuracy for the discrimination between adrenal adenomas and metastases. Key Points • Virtual non-contrast reconstructions may be promising tools to differentiate adrenal lesions and might save further diagnostic tests. • The conventional and a new calcium-preserving virtual non-contrast algorithm tend to systematically overestimate CT-values compared to true non-contrast images. • Therefore, increasing the established threshold for true non-contrast images (e.g., 10HU) may help to differentiate between adrenal adenomas and metastases on contrast-enhanced CT.

The aim of our study was to evaluate two different virtual non-contrast (VNC) algorithms applied to photon counting detector (PCD)-CT data in terms of noise, effectiveness of contrast media subtraction and aortic valve calcium (AVC) scoring compared to reference true non-contrast (TNC)-based results. Consecutive patients underwent TAVR planning examination comprising a TNC scan, followed by a CTA of the heart. VNC series were reconstructed using a conventional (VNCconv) and a calcium-preserving (VNCpc) algorithm. Noise was analyzed by means of the standard deviation of CT-values within the left ventricle. To assess the effectiveness of contrast media removal, heart volumes were segmented and the proportion of their histograms > 130HU was taken. AVC was measured by Agatston and volume score. 41 patients were included. Comparable noise levels to TNC were achieved with all VNC reconstructions. Contrast media was effectively virtually removed (proportions > 130HU from 81% to < 1%). Median calcium scores derived from VNCconv underestimated TNC-based scores (up to 74%). Results with smallest absolute difference to TNC were obtained with VNCpc reconstructions (0.4 mm, Br36, QIR 4), but with persistent significant underestimation (median 29%). Both VNC algorithms showed near-perfect (r²>0.9) correlation with TNC. Thin-slice VNC reconstructions provide equivalent noise levels to standard thick-slice TNC series and effective virtual removal of iodinated contrast. AVC scoring was feasible on both VNC series, showing near-perfect correlation, but with significant underestimation. VNCpc with 0.4 mm slices and Br36 kernel at QIR 4 gave the most comparable results and, with further advances, could be a promising replacement for additional TNC.

In this anthropomorphic head phantom study, samples containing blood and contrast agent with concentrations ranging from 0 to 6 mg iodine per milliliter and another set of samples without blood for reference were scanned with a photon-counting detector CT using a standard cranial protocol. It was demonstrated that photon-counting detector CT can reliably distinguish hemorrhage and contrast media, including density determination of the latter. The technology promises to add value in several neuroimaging applications.

Objectives: Virtual non-contrast (VNC) series reconstructed from contrast-enhanced cardiac scans acquired with photon counting detector CT (PCD-CT) systems have the potential to replace true non-contrast (TNC) series. However, a quantitative comparison of the image characteristics of TNC and VNC data is necessary to determine to what extent they are interchangeable. This work quantitatively evaluates the image similarity between VNC and TNC reconstructions by measuring the stability of multi-class radiomics features extracted in intra-patient TNC and VNC reconstructions. Methods: TNC and VNC series of 84 patients were retrospectively collected. For each patient, the myocardium and epicardial adipose tissue (EAT) were semi-automatically segmented in both VNC and TNC reconstructions, and 105 radiomics features were extracted in each mask. Intra-feature correlation scores were computed using the intraclass correlation coefficient (ICC). Stable features were defined with an ICC higher than 0.75. Results: In the myocardium, 41 stable features were identified, and the three with the highest ICC were glrlm_GrayLevelVariance with ICC3 of 0.98 [0.97, 0.99], ngtdm_Strength with ICC3 of 0.97 [0.95, 0.98], firstorder_Variance with ICC3 of 0.96 [0.94, 0.98]. For the epicardial fat, 40 stable features were found, and the three highest ranked are firstorder_Median with ICC3 of 0.96 [0.93, 0.97], firstorder_RootMeanSquared with ICC3 of 0.95 [0.92, 0.97], firstorder_Mean with ICC3 of 0.95 [0.92, 0.97]. A total of 24 features (22.8%; 24/105) showed stability in both anatomical structures. Conclusions: The significant differences in the correlation of radiomics features in VNC and TNC volumes of the myocardium and epicardial fat suggested that the two reconstructions may differ more than initially assumed. This indicates that they may not be interchangeable, and such differences could have clinical implications. Therefore, care should be given when selecting VNC as a substitute for TNC in radiomics research to ensure accurate and reliable analysis. Moreover, the observed variations may impact clinical workflows, where precise tissue characterization is critical for diagnosis and treatment planning.

PurposeTo investigate the diagnostic performance of an automatic pipeline for detection of hydronephrosis on kidney's parenchyma on unenhanced low-dose CT of the abdomen.MethodsThis retrospective study included 95 patients with confirmed unilateral hydronephrosis in an unenhanced low-dose CT of the abdomen. Data were split into training (n = 67) and test (n = 28) cohorts. Both kidneys for each case were included in further analyses, whereas the kidney without hydronephrosis was used as control. Using the training cohort, we developed a pipeline consisting of a deep-learning model for automatic segmentation (a Convolutional Neural Network based on nnU-Net architecture) of the kidney's parenchyma and a radiomics classifier to detect hydronephrosis. The models were assessed using standard classification metrics, such as area under the ROC curve (AUC), sensitivity and specificity, as well as semantic segmentation metrics, including Dice coefficient and Jaccard index.ResultsUsing manual segmentation of the kidney's parenchyma, hydronephrosis can be detected with an AUC of 0.84, a sensitivity of 75% and a specificity of 82%, a PPV of 81% and a NPV of 77%. Automatic kidney segmentation achieved a mean Dice score of 0.87 and 0.91 for the right and left kidney, respectively. Additionally, automatic segmentation achieved an AUC of 0.83, a sensitivity of 86%, specificity of 64%, PPV of 71%, and NPV of 82%.ConclusionOur proposed radiomics signature using automatic kidney's parenchyma segmentation allows for accurate hydronephrosis detection on unenhanced low-dose CT scans of the abdomen independently of widened renal pelvis. This method could be used in clinical routine to highlight hydronephrosis to radiologists as well as clinicians, especially in patients with concurrent parapelvic cysts and might reduce time and costs associated with diagnosing hydronephrosis.

OBJECTIVE:The aim of this study was to compare the effectiveness of common strategies for artifact reduction of dental material in photon-counting detector computed tomography data sets. MATERIALS AND METHODS:Patients with dental material who underwent clinically indicated CT of the neck were enrolled. Image series were reconstructed using a standard and sharp kernel, with and without iterative metal artifact reduction (IMAR) (Qr40, Qr40 IMAR , Qr60, Qr60 IMAR ) at different virtual monoenergetic imaging (VMI) levels (40-190 keV). On representative slice positions with and without dental artifacts, mean and standard deviation of CT values were measured in all series at identical locations. The mean absolute error of CT values ( ) and the artifact index (AIX) were calculated and analyzed focusing on 3 main comparisons: ( a ) different VMI levels versus 70 keV, ( b ) standard versus sharp kernel, and ( c ) nonuse or use of IMAR reconstruction. The Wilcoxon test was used to assess differences for nonparametric data. RESULTS:The final cohort comprised 50 patients. Artifact measures decreased for VMI levels >70 keV, yet only significantly so for reconstructions using IMAR (maximum reduction, 25%). The higher image noise of the sharp versus standard kernel is reflected in higher AIX values and is more pronounced in IMAR series (maximum increase, 38%). The most profound artifact reduction was observed for IMAR reconstructions (maximum reduction : 84%; AIX: 90%). CONCLUSIONS:Metal artifacts caused by large amounts of dental material can be substantially reduced by IMAR, regardless of kernel choice or VMI settings. Increasing the keV level of VMI series, on the other hand, only slightly reduces dental artifacts; this effect, however, is additive to the benefit conferred by IMAR reconstructions.

Purpose: To correlate CT values of the blood pool on VNC series with serum hemoglobin values for the detection of anemia in oncologic patients undergoing contrast-enhanced PCD-CT scans. Methods: This prospective study (NCT04989192) included consecutive oncologic patients undergoing contrastenhanced CT on a novel PCD-CT system between 08/2021 and 01/2022. The interval between complete blood count (CBC) and CT scan acquisition had to be no more than seven days. CT-values of the blood pool were measured on 70 keV VMI series (CT-values(BP)70keV) and on VNC series (CT-values(BP)VNC) at five anatomic positions (left atrium, left ventricle, main pulmonary artery, ascending and descending aorta) and averaged per patient. Pearson correlation analyses and ROC analyses were performed to identify relations between CT-values (BP)VNC, CBC parameters, and degrees of anemia as defined by the WHO (no anemia, mild, moderate, severe anemia). Results: A total of 329 patients (age 68 +/- 12 years; 200 men) were included. CT-values(BP) VNC showed a strong linear correlation to serum hemoglobin (r2 = 0.80, p <.001) and hematocrit (r2 = 0.76, p <.001) and were significantly different between anemia subgroups in both women and men (.HU: 3.5-11.4; all p <.01). ROC analyses yielded high diagnostic performance for the identification of patients without anemia, patients without and with mild anemia, and patients with severe anemia using gender-specific cutoffs for CT-value(BP) VNC (all AUC's > 0.90). Conclusions: The spectral information inherent in PCD-CT acquisitions allows the detection and quantification of anemia in contrast-enhanced CT acquisitions of oncologic patients with high diagnostic accuracy.

Purpose To analyze the conspicuity of pancreatic ductal adenocarcinoma (PDAC) in virtual monoenergetic images (VMI) on a novel photon-counting detector CT (PCD-CT) in comparison to energy-integrating CT (EID-CT). Methods Inclusion criteria comprised initial diagnosis of PDAC (reference standard: histopathological analysis) and standardized contrast-enhanced CT imaging either on an EID-CT or a PCD-CT. Patients were excluded due to different histopathological diagnosis or missing tumor delineation on CT. On the PCD-CT, 40–190 keV VMI reconstructions were generated. Image noise, tumor-to-pancreas ratio (TPR) and contrast-to-noise ratio (CNR) were analyzed by ROI-based measurements in arterial and portal venous contrast phase. Two board-certified radiologist evaluated image quality and tumor delineation at both, EID-CT and PCD-CT (40 and 70 keV). Results Thirty-eight patients (mean age 70.4 years ± 10.3 [range 45–91], 27 males; PCD-CT: n=19, EID-CT: n=19) were retrospectively included. On the PCD-CT, tumor conspicuity (reflected by low TPR and high CNR) was significantly improved at low-energy VMI series (≤ 70 keV compared to > 70 keV), both in arterial and in portal venous contrast phase (P < 0.001), reaching the maximum at 40 keV. Comparison between PCD-CT and EID-CT showed significantly higher CNR on the PCD-CT in portal venous contrast phase at < 70 keV (P < 0.016). On the PCD-CT, tumor conspicuity was improved in portal venous contrast phase compared to arterial contrast phase especially at the lower end of the VMI spectrum (≤ 70 keV). Qualitative analysis revealed that tumor delineation is improved in 40 keV reconstructions compared to 70 keV reconstructions on a PCD-CT. Conclusion PCD-CT VMI reconstructions (≤ 70 keV) showed significantly improved conspicuity of PDAC in quantitative and qualitative analysis in both, arterial and portal venous contrast phase, compared to EID-CT, which may be important for early detection of tumor tissue in clinical routine. Tumor delineation was superior in portal venous contrast phase compared to arterial contrast phase.

PURPOSE:Aim of this study was to determine which virtual non-contrast (VNC) reconstruction algorithm, applied to which contrast phase of computed tomography angiography, best matches true non-contrast (TNC) images in the assessment of active bleeding. METHOD:Patients who underwent a triphasic scan (pre-contrast, arterial, portal venous contrast) on a photon-counting detector CT (PCD-CT) (120 kV, image quality level 68) with suspected active (tumor, postoperative, spontaneous or other) bleeding were retrospectively included in this study. Conventional (VNCConv) and a calcium-preserving VNC algorithm (VNCPC) were derived from both arterial (art) and portal venous (pv) contrast scans, and analyzed quantitatively and qualitatively by two independent and blinded raters. RESULTS:40 patients (22 female, mean age 76 years) were included. Measurements of CT values showed significant albeit small differences between TNC and VNC for most analyzed tissue regions without clear superiority of a VNC algorithm or contrast phase (e.g. ΔHU fat TNC to VNCPCpv 3.1 HU). However, qualitative analysis showed a preference to VNCPCpv in terms of image quality (on a 5-point Likert scale VNCConvart = 3.5 ± 0.8, VNCPCart = 3.7 ± 0.7, VNCConvpv = 3.7 ± 0.7, VNCPCpv = 3.8 ± 0.7) and residual calcium contrast (VNCConvart = 3.0 ± 0.8, VNCPCart = 3.5 ± 0.7, VNCConvpv = 3.6 ± 0.7, VNCPCpv = 3.9 ± 0.6). CONCLUSIONS:When multiple post-contrast phases are available, VNCPC series based on portal venous phase are the most suitable replacement for an additional pre-contrast scan, with the prospect of a significant reduction in patient radiation dose.

In photon-counting detector CT (PCD-CT), coronary artery calcium scoring (CACS) can be performed using virtual non-contrast (VNC) series derived from coronary CT angiography (CCTA) datasets. Our study analyzed image characteristics of VNC series in terms of the efficacy of virtual iodine "removal" and image noise to determine whether the prerequisites for calcium quantification were satisfied. We analyzed 38 patients who had undergone non-enhanced CT followed by CCTA on a PCD-CT. VNC reconstructions were performed at different settings and algorithms (conventional VNCConv; PureCalcium VNCPC). Virtual iodine "removal" was investigated by comparing histograms of heart volumes. Noise was assessed within the left ventricular cavity. Calcium was quantified on the true non-contrast (TNC) and all VNC series. The histograms were comparable for TNC and all VNC. Image noise between TNC and all VNC differed slightly but significantly. VNCConv CACS showed a significant underestimation regardless of the reconstruction setting, while VNCPC CACS were comparable to TNC. Correlations between TNC and VNC were excellent, with a higher predictive accuracy for VNCPC. In conclusion, the iodine contrast can be effectively subtracted from CCTA datasets. The remaining VNC series satisfy the requirements for CACS, yielding results with excellent correlation compared to TNC-based CACS and high predicting accuracy.