BACKGROUND:In cardiovascular magnetic resonance (CMR), myocardial native T1 mapping enables quantitative, non-invasive tissue characterization and is sensitive to subclinical changes in myocardial structure and composition. However, data on the association between cardiometabolic risk and myocardial alterations are still limited. We therefore investigated how age, sex, and cardiometabolic risk factors are associated with myocardial T1 as a potential imaging marker of myocardial target-organ involvement in a population-based analysis within the German National Cohort (NAKO). METHODS:This cross-sectional study included 29,573 prospectively enrolled participants who underwent midventricular T1 mapping using 3.0T CMR along with deep clinical phenotyping. After artificial intelligence-assisted myocardial segmentation, a subset of 9,162 outlier cases was subjected to manual quality control according to clinical evaluation standards. Cardiometabolic risk factors were identified through self-reported, physician-diagnosed medical history, clinical chemistry, and blood pressure measurements. Associations with myocardial T1 were evaluated using multiple linear regression models adjusted for age, sex, heart rate, imaging site, and comorbidities. RESULTS:After quality control, 27,794 participants (12,401 [44.6%] women; 20-75 years) were included. Mean T1 was overall higher in women (1,231 ± 33 ms) than in men (1,209 ± 34 ms), with differences progressively declining with age. The sex difference was confirmed in a healthy subcohort (n = 3,910), whilst age interaction was less pronounced. In adjusted analyses, T1 was significantly higher in individuals with diabetes, kidney disease, and current smoking. Conversely, hyperlipidaemia was significantly associated with lower T1. Associations with hypertension showed strongly sex-specific patterns: women had lower T1 values, while T1 increased with hypertension severity in men. CONCLUSIONS:Myocardial native T1 varies by sex and age and shows distinct sex-specific associations with major cardiometabolic risk factors, supporting its role as a sensitive imaging marker of subtle myocardial alterations. Unexpectedly lower T1 times in participants with hyperlipidaemia suggest a potential direct effect of blood lipids on the heart, which warrants further investigation.

To investigate whether myocardial iodine attenuation and iodine/virtual non-contrast (VNC) ratios derived from photon-counting detector CT (PCCT) are associated with coronary artery stenosis severity as classified by CAD-RADS, and to evaluate their potential complementary value to anatomical grading. In this retrospective single-center study, 57 patients (42.1

The human spine commonly consists of seven cervical, twelve thoracic, and five lumbar vertebrae. However, enumeration anomalies may result in individuals having eleven or thirteen thoracic vertebrae and four or six lumbar vertebrae. Although the identification of enumer- ation anomalies has potential clinical implications for chronic back pain and operation planning, the thoracolumbar junction is often poorly as- sessed and rarely described in clinical reports. Additionally, even though multiple deep-learning-based vertebra labeling algorithms exist, there is a lack of methods to automatically label enumeration anomalies. Our work closes that gap by introducing "Vertebra Identification with Anomaly Handling" (VERIDAH), a novel vertebra labeling algorithm based on multiple classification heads combined with a weighted vertebra sequence prediction algorithm. We show that our approach surpasses existing mod- els on T2w TSE sagittal (98.30

Background: Anemia is common among patients with acute ischemic stroke (AIS) and is associated with poorer outcomes, including higher mortality and less favorable recovery. CT-based blood attenuation measurements have been investigated as a noninvasive imaging marker of hemoglobin levels. Objectives: This study evaluates the diagnostic performance of photon-counting detector CT (PCD-CT) for anemia detection using both true non-contrast (TNC) and virtual non-contrast (VNC) images in patients with AIS. Methods: This single-center, retrospective study included 45 AIS patients from a previously investigated cohort who underwent both unenhanced head CT and CT angiography of the supra-aortic vessels on the same PCD-CT system, enabling a vessel-matched comparison of TNC and VNC attenuation measurements in intracranial venous structures. Attenuation measurements were obtained from predefined venous structures, including the great cerebral vein (VCM), superior sagittal sinus (SSS), sigmoid sinus, confluence of sinuses, and internal jugular vein. Hemoglobin levels were classified according to WHO criteria, and correlation, regression, and ROC analyses were performed. Thresholds were selected exploratorily using the Youden index. Results: Forty-five AIS patients were included (20 females, 25 males; mean age 72.2 years), of whom 16 (35.6%) were anemic. TNC measurements in the VCM showed the strongest association with hemoglobin levels (r = 0.75, R2 = 0.56, P < 0.001) and the highest intracranial diagnostic performance for anemia detection (AUC = 0.87; threshold ≤ 42 HU; sensitivity 81.3%; specificity 75.9%). VCM-VNC measurements showed a weaker but significant association with hemoglobin levels (r = 0.51, R2 = 0.26, P < 0.001) and lower diagnostic performance (AUC = 0.72; threshold ≤ 36 HU; sensitivity 87.5%; specificity 62.1%). Among intracranial VNC markers, the sigmoid sinus showed the highest diagnostic performance (AUC = 0.84; threshold ≤ 32.5 HU; sensitivity 81.3%; specificity 79.3%). In the pragmatic non-vessel-matched comparison, internal jugular VNC showed AUC values similar to those of VCM-TNC (0.869 vs. 0.865; DeLong P = 0.485). Conclusions: Attenuation measurements from TNC and VNC PCD-CT images were associated with hemoglobin levels in AIS patients. TNC, particularly in the great cerebral vein, remained the most robust marker in vessel-matched intracranial comparisons. Internal jugular VNC may offer a pragmatic, screening-oriented marker in stroke workflows; however, this non-vessel-matched comparison conflates modality and anatomical-location effects. Prospective validation with time-matched laboratory data is required before clinical implementation.

Photon‑counting detector CT (PCD‑CT) enables ultra‑high resolution (UHR, 0.2 mm) and may improve visualization of coronary stents compared with standard‑resolution (SR, 0.4 mm). In this ex- vivo study, we utilized a phantom heart model to simulate physiological attenuation and imitated the most common coronary bifurcation stenting techniques by creating multiple stent layers and stent- crush situations (single layer, crushed, two‑ and three‑layer). We compared UHR and SR PCD‑CT using Bv72 and Bv56 kernels. Objective endpoints were in‑stent lumen visibility, signal-to-noise ratio, and percentage change in stent attenuation; subjective image quality was rated on a 5‑point Likert scale (1 = excellent to 5 = non‑diagnostic). Depending on distribution, two‑sided t‑tests (mean ± SD) or Mann–Whitney U tests (median[IQR]) were applied, p‑values were Bonferroni‑corrected for multiple comparisons. In the pooled Crush cohort, UHR Bv72 improved in‑stent lumen visibility when compared with both, SR Bv72 (single-layer: 68.08 ± 4.30% vs. 79.03 ± 3.79%; crushed: 63.62 ± 4.97% vs. 74.73 ± 2.51%) and SR Bv56 (single-layer: 63.63 ± 4.47% vs. 79.03 ± 3.79%; crushed: 62.18 ± 3.55% vs. 74.73 ± 2.51%), all p < 0.01. The effect was even more pronounced in small stents (single‑layer 65.45 ± 1.91% (SR) vs. 77.11 ± 2.55% (UHR) and crushed 60.67 ± 2.67% vs. 74.00 ± 2.67%), both p < 0.01. Regarding subjective image quality, UHR was associated with better sharpness and less blooming, e.g., UHR Bv72 vs. SR Bv56 (multilayer: sharpness 1[0] vs. 2[0], blooming 1[0] vs. 2[0]; crush: sharpness 1[0] vs. 2[0], blooming 1[0] vs. 2[1]; all p ≤ 0.01) and UHR Bv72 vs. SR Bv72 (multilayer: sharpness 1[0] vs. 1[1], blooming 1[0] vs. 1[1]; crush: sharpness 1[0] vs. 1[1]; all p ≤ 0.05). These findings indicate that UHR PCD‑CT with Bv72 might enable assessment of complex stents in a phantom setting; even though clinical studies are warranted.

To evaluate the patient-, vessel- and segment-based diagnostic performance of photon-counting detector CT (PCD-CT) compared to energy-integrating detector CT (EID-CT) for detecting ≥ 50

BACKGROUND:The increased specificity of ultrahigh-resolution (UHR) photon-counting detector (PCD)-CT over energy-integrating detector (EID)-CT for coronary CT angiography (CCTA) could defer unwarranted downstream tests. The objective of the study was to simulate the cost-effectiveness of UHR CCTA in stable chest pain patients with coronary calcifications. METHODS:A decision and simulation model was developed using Monte Carlo simulations with 1000 bootstrap resamples to estimate the costs associated with PCD-CT in lieu of EID-CT for CCTA and the referral for subsequent testing. The model was constructed using the diagnostic accuracy metrics of 55 coronary lesions in patients who underwent CCTA on both CT systems and subsequent invasive coronary angiography (ICA). Sensitivity and specificity were defined for each Coronary Artery Disease Reporting and Data System category. The aggregate healthcare expenditures were derived from the hospital billing system. RESULTS:Assuming a projected cohort of 15,000 patients over the lifetime of the PCD-CT, its implementation resulted in a 18.9 ​% reduction in the number of functional follow-up tests (6330.3 ​± ​59.5 vs. 5135.7 ​± ​60.6, p ​< ​0.001), a 6.0 ​% reduction in performed ICAs (1447.7 ​± ​36.2 vs. 1360.2 ​± ​34.7, p ​< ​0.001), and a 9.4 ​% decrease in major procedure-related complications. Over a 10-year expected life expectancy, PCD-CT led to an average cost saving of $794.50 ​± ​18.50 per patient and an overall cost difference of $11,917,500 ​± ​4,350,169. CONCLUSIONS:PCD-CT has the potential to reduce the financial burden on healthcare systems and procedure-related complications for stable chest pain patients with coronary calcification when compared to EID-CT.

To compare image quality and iodine attenuation intra-individually in portal venous phase photon-counting detector CT (PCD-CT) scans using protocols with different contrast medium (CM) volume. A prospectively acquired patient cohort between 04/2021 and 11/2023 was retrospectively screened if patients had the following combination of portal venous phase thoracoabdominal CT scans: (a) PCD-CT with 120 mL CM volume (PCD-CT120 mL), (b) PCD-CT with 100 mL CM volume (PCD-CT100 mL), and (c) prior energy-integrating detector CT (EID-CT) with 120 mL CM volume. On PCD-CT, virtual monoenergetic image (VMI) reconstructions at 70 keV were applied for both groups as well as additional VMI at 60 keV for PCD‑CT100 mL. Quantitative analyses including signal-to-noise (SNR) and contrast-to-noise ratios (CNR) and qualitative analyses were performed using a mixed linear effects model. The final study cohort comprised 49 patients (mean age 67 [31–86] years, 12 female). Comparison to EID-CT was available in 33 patients. In standard 70 keV VMI reconstructions, PCD-CT100 mL was non-inferior to PCD-CT120 mL as well as to EID-CT120 mL for CNR in abdominal organs (all p > 0.050). The mixed linear effects model revealed significant differences between contrast volume groups for both contrast enhancement and image quality ratings. PCD-CT100 mL/70 keV demonstrated the smallest deviation from optimal contrast enhancement (−0.306, p < 0.001). In portal venous phase thoracoabdominal PCD-CT, a nearly 17

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.

Anemia is a common comorbidity in stroke patients, traditionally detected via blood tests. This study evaluates the feasibility of using virtual non-contrast (VNC) imaging from photon counting detector-CT (PCD-CT) angiography to detect anemia and identifies the optimal anatomical site for assessment. In this retrospective study of 80 patients undergoing PCD-CT angiography of supra-aortic vessels, VNC series were analyzed at various anatomical sites, including the jugular veins, aorta, and cerebral sinuses. Correlations between serum hemoglobin (Hb) levels and VNC Hounsfield Unit (HU) values were assessed using Pearson’s coefficients. Linear regression and ROC analysis evaluated diagnostic performance.ResultsThe jugular veins showed the strongest correlation between VNC HU values and Hb levels (R2 = 0.49, p < 0.001), with weaker correlations in arterial vessels like the aorta (R2 = 0.11, p < 0.001). ROC analysis of jugular vein VNC values yielded an AUC of 0.79 for anemia detection. Correlation strength declined with longer intervals between imaging and blood tests, suggesting temporal Hb variability. VNC imaging in CT angiography is a feasible method for detecting anemia, with the jugular veins providing the most reliable site for assessment. VNC imaging could be a valuable alternative when blood tests are delayed or unavailable.

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.

Accurate segmentation of structures in CT is essential for clinical tasks such as tumour staging, radiotherapy planning, fracture assessment, and monitoring of disease progression. Current deep learning-based automated "segmentators" face challenges due to variability in scanner parameters, anatomical regions, and training data, which impact performance consistency across diverse datasets. We evaluated various total body segmentators on publicly available lung CT data excluded from their training sets. We found that these segmentators exhibit label mixing within individual ribs and vertebrae, often requiring anatomy-informed post-processing steps to improve accuracy. Combining multiple models and incorporating anatomical information enhances segmentation outcomes compared to using single models, highlighting the complementary strengths of different segmentation approaches and task-dependent a priori knowledge.

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.

PURPOSE:To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA).PATIENTS AND METHODS:A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.RESULTS:A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.CONCLUSIONS:The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

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.

Background/Objectives: To evaluate the differences in treatment and outcomes between traumatic and atraumatic splenic lacerations. Methods: This retrospective study included all patients with a diagnosis of splenic lacerations confirmed by computed tomography that presented from 01/2010 to 03/2023 at one tertiary hospital. The exclusion criteria included missing image data and death in the first 24 h due to extensive trauma. The etiology of the splenic laceration, demographic characteristics, and clinical parameters were recorded and evaluated as prognostic factors in therapy success and mortality. Subgroup analyses were undertaken according to the etiology of the splenic laceration and the primary treatment. The extent of splenic laceration was assessed by using the American Association for the Surgery of Trauma (AAST) score in its latest revision (2018). Results: Of all 291 enrolled patients (mean age 47 ± 21 years, 204 males), 50 presented with atraumatic splenic lacerations due to different underlying causes. The occurrence of moderate and high-grade laceration differed significantly between the atraumatic and traumatic study group (45/50 [90%] vs. 139/241 [58%], p < 0.001). Accordingly, the number of patients being treated conservatively differed greatly (20/50 [40%] vs. 164/241 [56%]), with a worse clinical success rate for atraumatic lacerations (75% vs. 94.5%). Atraumatic splenic injuries showed a higher conversion rate to surgery (2/20 [10%] vs. 2/164 [1%]). Despite the lower clinical success rate of splenic artery embolization (SAE) in atraumatic injuries (87% vs. 97%), the number of patients needing treatment for primary SAE in AAST 3 injuries was 14.1 in the traumatic population and only 4 in the atraumatic population. Conclusions: Atraumatic splenic injuries should not be treated as traumatic splenic injuries. An early upgrade to SAE or surgery should be considered for moderate splenic injuries, and they should be evaluated by an interdisciplinary team on a case-by-case basis. However, due to the underlying multimorbidity of patients with atraumatic splenic injuries, a higher mortality is to be expected.

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.