UNASSIGNED: The effects of obesity on pulmonary gas and blood distribution in patients with acute respiratory failure remain unknown. Dual-energy computed tomography (DECT) is a X-ray-based method used to study regional distribution of gas and blood within the lung. We hypothesized that 1) regional gas/blood mismatch can be quantified by DECT; 2) obesity influences the global and regional distribution of pulmonary gas and blood; 3) regardless of ventilation modality (invasive vs. non-invasive ventilation), patients\' body mass index (BMI) has an impact on pulmonary gas/blood mismatch.
UNASSIGNED: This single-centre prospective observational study enrolled 118 hypoxic COVID-19 patients (92 male) in need of respiratory support and intensive care who underwent DECT. The cohort was divided into three groups according to BMI: 1. BMI<25 kg/m2 (non-obese), 2. BMI = 25-40 kg/m2 (overweight to obese), and 3. BMI>40 kg/m2 (morbidly obese). Gravitational analysis of Hounsfield unit distribution of gas and blood was derived from DECT and used to calculate regional gas/blood mismatch. A sensitivity analysis was performed to investigate the influence of the chosen ventilatory modality and BMI on gas/blood mismatch and adjust for other possible confounders (i.e., age and sex).
UNASSIGNED: 1) Regional pulmonary distribution of gas and blood and their mismatch were quantified using DECT imaging. 2) The BMI>40 kg/m2 group had less hyperinflation in the non-dependent regions and more lung collapse in the dependent regions compared to the other BMI groups. In morbidly obese patients, gas and blood were more evenly distributed; therefore, the mismatch was lower than in other patients (30% vs. 36%, p < 0.05). 3) An increase in BMI of 5 kg/m2 was associated with a decrease in mismatch of 3.3% (CI: 3.67% to -2.93%, p < 0.05). Neither the ventilatory modality nor age and sex affected the gas/blood mismatch (p > 0.05).
UNASSIGNED: 1) In a hypoxic COVID-19 population needing intensive care, pulmonary gas/blood mismatch can be quantified at a global and regional level using DECT. 2) Obesity influences the global and regional distribution of gas and blood within the lung, and BMI>40 kg/m2 improves pulmonary gas/blood mismatch. 3) This is true regardless of the ventilatory mode and other possible confounders, i.e., age and sex.
UNASSIGNED: Clinicaltrials.gov, identifier NCT04316884, NCT04474249.

OBJECTIVE: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction.
METHODS: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score.
RESULTS: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively.
CONCLUSIONS: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively.
CONCLUSIONS: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.

OBJECTIVE: We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making.
METHODS: This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models.
RESULTS: Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model.
CONCLUSIONS: The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients.
CONCLUSIONS: The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS.
CONCLUSIONS: Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

OBJECTIVE: Patients with gout are at elevated risk of multiple vascular and metabolic comorbidities. Whether they are also at risk of sarcopenia, which is known to affect patients with other rheumatic diseases, has not been previously assessed. We examined whether patients with gout have decreased lumbar muscle quality and quantity, indicating an association between gout and sarcopenia.
METHODS: Fifty gout subjects and 25 controls, ages 45-80, underwent computed tomography imaging of the lumbosacral spine. We measured muscle quantity (skeletal muscle area [SMA] and index [SMI]) and quality (skeletal muscle radiation attenuation [SMRA] and intermuscular adipose tissue [IMAT] area and index [IMATI]) of the psoas and erector spinae muscles at the L3 level.
RESULTS: Seventy subjects (45 gout and 25 controls) were included in the analysis. Gout subjects had higher BMI, more kidney disease and hypertension, lower exercise frequency, and higher mean serum urate and creatinine vs. controls. Lumbar SMRA was significantly lower in gout subjects vs. controls, indicating reduced muscle quality. Lumbar IMAT area was significantly higher in gout subjects vs. controls, as was lumbar IMATI, indicating increased muscle adiposity. These differences persisted after adjusting for potential confounders. In contrast, there was no significant difference between gout and control groups in lumbar SMA or lumbar SMI, suggesting that muscle quantity may not be routinely affected by the diagnosis of gout.
CONCLUSIONS: Gout patients exhibit decreased lumbar muscle quality compared with controls, consistent with an association between gout and sarcopenia.

Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p < .001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p > .005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited.

To explore the feasibility of measuring glomerular filtration rate (GFR) using iodine maps in dual-energy spectral computed tomography urography (DEsCTU) and correlate them with the estimated GFR (eGFR) based on the equation of creatinine-cystatin C.
One hundred and twenty-eight patients referred for DEsCTU were retrospectively enrolled. The DEsCTU protocol included non-contrast, nephrographic, and excretory phase imaging. The CT-derived GFR was calculated using the above 3-phase iodine maps (CT-GFRiodine) and 120 kVp-like images (CT-GFR120kvp) separately. CT-GFRiodine and CT-GFR120kvp were compared with eGFR using paired t-test, correlation analysis, and Bland-Altman plots. The receiver operating characteristic curves were used to test the renal function diagnostic performance with CT-GFR120kvp and CT-GFRiodine.
The difference between eGFR (89.91 ± 18.45 ml·min-1·1.73 m-2) as reference standard and CT-GFRiodine (90.06 ± 20.89 ml·min-1·1.73 m-2) was not statistically significant, showing excellent correlation (r = 0.88, P < 0.001) and agreement (± 19.75 ml·min-1·1.73 m-2, P = 0.866). The correlation between eGFR and CT-GFR120kvp (66.13 ± 19.18 ml·min-1·1.73 m-2) was poor (r = 0.36, P < 0.001), and the agreement was poor (± 40.65 ml·min-1·1.73 m-2, P < 0.001). There were 62 patients with normal renal function and 66 patients with decreased renal function based on eGFR. The CT-GFRiodine had the largest area under the curve (AUC) for distinguishing between normal and decreased renal function (AUC = 0.951).
The GFR can be calculated accurately using iodine maps in DEsCTU. DEsCTU could be a non-invasive and reliable one-stop-shop imaging technique for evaluating both the urinary tract morphology and renal function.

No study has rigorously compared the performances of iodine quantification on recent CT systems employing different emission-based technologies, depending on the manufacturers and models.
A specific bespoke phantom was used for this study, with 12 known concentrations of iodinated contrast agent: 0.4, 0.5, 1.0, 2.0, 3.0, 4.0, 5.0, 10.0, 15.0, 20.0, 30.0 and 50.0 mg/mL. Three different dual-energy scanners were tested: one system using dual-source acquisition (CT#1) and two systems using Fast kilovolt-peak switching technology ± artificial intelligence (AI) reconstruction methods (CT#2 and #3) from two different manufacturers. For each system, helical scans were performed following recommended clinical protocols. Four acquisitions were performed per iodine concentration (mg/mL), and measurements were made on iodine-maps using ROIs. Mean measured values were compared to the known concentrations, and the absolute quantification error (AQE) and the relative percentage error (RPE) were used to compare the performances of each CT.
The accuracy of the obtained measurements varied depending on the studied model but not on the acquisition mode (dual-source vs kVp switch ± AI). The quantification was more precise at high concentrations. RPE values were below 10 % with CT#2 (kVp switch) and below 25 % with CT#1 (dual-source), but were significantly higher with CT#3 (kVp switch + AI), exceeding 50 % at low concentrations (<3 mg/mL).
With the help of a phantom, we identified variability in the results accuracy depending on the CT model, with sometimes significant deviation. Considering the performances of the different DECT technologies in iodine mapping, dual-source (CT#1) and kVp switch (CT#2) technologies appear more accurate than kVp switch technology combined with deep-learning-based reconstruction (CT#3) especially at low concentrations (<3 mg/mL).
As the primary and daily user of medical imaging devices, the radiographer role is to be attentive to the performance of imaging systems, particularly when performing quantitative acquisitions like iodine-quantification. In CT quantitative imaging (iodine map), it\'s essential for radiographers to consider their CT systems as measuring tools, and to be aware of their accuracies and limits.

OBJECTIVE: To assess hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) through clinical data analyses, including evaluation of liver fibrosis using the extracellular volume fraction (ECV) obtained from dual-energy computed tomography (DECT).
METHODS: Ninety-two patients (52 men and 40 women; mean age, 69.9 years) with hepatitis C virus infection after SVR underwent DECT of the liver (3-minute equilibrium-phase images) between January 2020 and March 2022. The ECV was calculated by measuring iodine density; fibrous markers, including ECV, fibrosis-4 index, aspartate aminotransferase to platelet ratio index, and platelet count, were statistically analyzed (p < 0.05). The risk factors associated with HCC were analyzed using univariate and multivariate logistic regression analyses.
RESULTS: The ECV (26.1 ± 4.6 %) in patients with HCC (n,21) was significantly larger than the ECV (20.7 ± 3.3 %) in patients without HCC (n = 71) (p < 0.001). The cutoff value for the ECV was 24.3 %. The area under the operating characteristic curve of the ECV was 0.857, which was higher than that of the serum fibrosis markers. Older age, SVR achieved with interferon, alpha-fetoprotein level (>5 ng/mL), advanced fibrosis before treatment (>F3), and ECV were associated with HCC according to the univariate analysis. Multivariate analyses showed that ECV was the only factor independently associated with HCC (odds ratio 0.619, 95 % confidence interval 0.482-0.795, p < 0.001).
CONCLUSIONS: Liver fibrosis estimated using ECV can be a predictive marker in patients with HCC after SVR.

UNASSIGNED: Pulmonary hypertension (PH) is a rare but fatal complication of sickle cell disease (SCD) that is possibly reversible if treated early. Dual-energy computed tomography (DECT) is a valuable tool for diagnosing PH. We attempted to determine if DECT can detect early signs of PH in children with SCD.
UNASSIGNED: This prospective observational pilot study was conducted at the Geneva University Hospitals and was approved by the local human ethics committee (CCER 2019-01975). A written informed consent was obtained from the patients and/or their legal guardian. Eight children (consisting of five girls and three boys) with homozygous SCD were included in the study. They underwent full cardiological workup using transthoracic echocardiography (TTE) and cardiopulmonary exercise test (CPET), as well as DECT.
UNASSIGNED: The median age of the children was 11 years old (range 8-12). All patients exhibited a normal biventricular systo-diastolic function using the TTE. The median tricuspid regurgitant jet velocity value was 2.24 m/s (range 1.96-2.98). Four children were found to have signs of vasculopathy detected on DECT. Of them, two had abnormal screening test results. They both had an increased VE/VCO2 slope during CPET and an increased TVR of >2.5 m/s on TTE.
UNASSIGNED: DECT is capable of identifying early signs of pulmonary vascular disease in children with SCD. Further studies are needed to understand the correlation between DECT abnormalities and hemodynamic pulmonary circulation better.

No study has investigated scan parameters in head and neck dual layer dual-energy computed tomography (DL-DECT). This study aimed to select the appropriate scan parameters in head and neck imaging by evaluating the scan parameter effects on the accuracies of CT numbers and conduct iodine quantification in DL-DECT.
A multi-energy phantom was scanned using a dual layer CT (DLCT) scanner. Reference materials of iodine, blood, calcium, and adipose were used. A helical scan was performed by using reference and several protocols. Iodine density and virtual monochromatic images (VMIs) at the energy of 50, 70, and 100 keV were reconstructed. The iodine concentrations and CT numbers in each protocol were measured. Moreover, the absolute percentage errors (APEs) of iodine quantifications and CT numbers (reference vs. each protocol) were compared. Equivalence was observed when APEs between reference and each protocol was within 5%. Statistical analysis was performed using appropriate software.
The APEs between the high-tube-voltage and reference protocol were 23.7, 14.0, 8.8, and 8.1% for iodine reference materials with concentrations equal to 2, 5, 10, and 15 mg/ml, respectively. At 50 keV, APEs between the high-tube-voltage and reference protocols were greater than 5% except for calcium and adipose. At 100 keV, APEs between the high-tube-voltage and reference protocols were greater than 5% except for blood and calcium.
The high-tube-voltage protocol improved the accuracies of the measurement for iodine quantification and CT numbers. Additionally, the scanning parameters except for tube voltage had no effect on accuracies of iodine quantitation and CT numbers in the DLCT scanner.
The use of the high-tube-voltage protocol will be recommended for more accurate material decomposition in head and neck DL-DECT.