Background:Dual-energy computed tomography (DECT) and iterative metal artifact reduction (iMAR) algorithms are valuable tools for reducing metal artifacts. Different parameters of these technologies and their combination can achieve different performance. This study compared various polychromatic and monochromatic images obtained via DECT with and without using iMAR algorithm to reduce artifacts in patients with dental implants. Methods:This study included 30 patients with dental implants who underwent DECT for head and neck imaging. The computed tomography (CT) image sets comprised DECT polychromatic image sets [dual-energy (DE) polychromatic] that linearly blended 100 kV and tin-filtered 140 kV images using composition ratios of -1, -0.6, -0.3, 0, and 0.6, and virtual monochromatic images (DE monochromatic) at 90, 110, 130, 150, and 170 keV. These image sets were obtained with and without using iMAR, resulting in a total of 20 image sets. For subjective analysis, metal artifacts and image quality were assessed using a 5-point Likert scale. For objective analysis, CT attenuation, standard deviation (SD), contrast-to-noise ratio (CNR) and artifact index (AI) were evaluated. In addition, subgroup analysis was performed based on implant size. Results:In the subjective evaluation, iMAR + DE polychromatic (-0.3) images exhibited the lowest metal artifact scores [median (interquartile range): 2 (2-3)]. iMAR + DE monochromatic (110 keV) images demonstrated optimal image quality scores [median (interquartile range): 2 (2-3)]. In the objective evaluation, none of the images demonstrated a significant difference in the CNR, except polychromatic images with a composition of -1 and 0.6. iMAR + DE polychromatic (0) exhibited the lowest AI [median (interquartile range): 8.7 (5.9-14.5)]. There was no significant difference between the two groups with different implant sizes for the techniques combined with iMAR (all P>0.05). Conclusion:iMAR + DE polychromatic (-0.3 and 0) and iMAR + DE monochromatic (110 keV) images exhibited better image quality and substantial metal artifact reduction (MAR) compared with the other image sets. The performance of the techniques combined with iMAR was not affected by the size of the implant.

ObjectiveTo utilize radiomics analysis on dual-energy CT images of the pancreas to establish a quantitative imaging biomarker for type 2 diabetes mellitus.Materials and methodsIn this retrospective study, 78 participants (45 with type 2 diabetes mellitus, 33 without) underwent a dual energy CT exam. Pancreas regions were segmented automatically using a deep learning algorithm. From these regions, radiomics features were extracted. Additionally, 24 clinical features were collected for each patient. Both radiomics and clinical features were then selected using the least absolute shrinkage and selection operator (LASSO) technique and then build classifies with random forest (RF), support vector machines (SVM) and Logistic. Three models were built: one using radiomics features, one using clinical features, and a combined model.ResultsSeven radiomic features were selected from the segmented pancreas regions, while eight clinical features were chosen from a pool of 24 using the LASSO method. These features were used to build a combined model, and its performance was evaluated using five-fold cross-validation. The best classifier type is Logistic and the reported area under the curve (AUC) values on the test dataset were 0.887 (0.73–1), 0.881 (0.715–1), and 0.922 (0.804–1) for the respective models.ConclusionRadiomics analysis of the pancreas on dual-energy CT images offers potential as a quantitative imaging biomarker in the detection of type 2 diabetes mellitus.

Objective: To explore the characteristics of high-resolution computed tomography (HRCT) in diabetes complicated with coronavirus disease 2019 (COVID-19)-associated pneumonia. Materials and Methods: This study included 584 patients (359 males and 225 females), aged between 60~99 years old (mean, (76±9) years), with positive chest computed tomography (CT) findings and diagnosed with COVID-19 in our hospital from December 14, 2022, to January 10, 2023. Of these, 225 patients were diabetic and 359 were non-diabetic. The features of the chest HRCT from patients with diabetes mellitus complicated with COVID-19 and those without diabetes mellitus complicated with COVID-19 were compared. Moreover, 363 patients in the acute stage of COVID-19 (defined as the time interval between onset and CT examination <7 days) were selected for subgroup analysis, and the HRCT characteristics of COVID-19 between the diabetes group and the non-diabetic group in the acute stage. Results: The location, distribution, morphology, and concomitant signs of pulmonary lesions between the two groups of patients with COVID-19 did not differ significantly. Conversely, statistically significant differences in density (fine mesh, uneven density) and lesion margin (fuzzy lesion margin) were detected. In particular, the grid, uneven, and fuzzy signs on lung imaging were significantly higher in the non-diabetic group than that in the diabetic group. Additionally, 54 patients (24%) in the diabetic group and 127 patients (35.38%) in the non-diabetic group demonstrated fine mesh shadows. There were 181 patients (80.44%) in the diabetic group and 313 patients (87.19%) in the non-diabetic group with uneven density. Furthermore, 205 patients (91.11%) in the diabetic group and 344 patients (95.82%) in the non-diabetic group had blurred edges. There was significantly less pulmonary grid shadowing in the acute subgroup with diabetes (35, 24.65%) than in the acute subgroup without diabetes (82, 37.10%). Conclusion: The features of chest HRCT in patients with diabetes mellitus and COVID-19 are mainly exudation, uniform density, and a clear edge, while the interstitial changes are not obvious compared with patients in the non-diabetic group.

Purpose: To investigate the clinical value of measuring pancreatic fat fraction using dual-energy computed to-mography (DECT) in association with type 2 diabetes mellitus (T2DM). Materials and methods: This retrospective study included patients who underwent abdominal DECT between September 2021 and July 2022. The fat fractions in the head, body, and tail of the pancreas were calculated using fat maps generated from unenhanced DECT images, and CT values were measured at the same locations. The intraclass correlation coefficient (ICC) was used to analyze the reproducibility of measurements from two ob-servers. Diagnostic performance was assessed using receiver operating characteristic curves. Results: Seventy-eight patients, including 45 T2DM patients and 33 controls, were enrolled. The fat fractions of the pancreas were significantly higher in the T2DM group than in the control group (pancreatic head: 8.4 +/- 6.3 % vs 5.1 +/- 3.9 %; pancreatic body: 4.8 +/- 4.0 % vs 2.7 +/- 3.9 %; and pancreatic tail: 5.3 +/- 3.2 % vs 2.7 +/- 2.9 %, all p < 0.05). And the CT values of the pancreas were significantly lower in the T2DM group than in the control group (pancreatic head: 41.1 +/- 8.5 HU vs 45.7 +/- 4.6 HU; pancreatic body: 44.4 +/- 5.0 HU vs 47.4 +/- 3.7 HU; and pancreatic tail: 44.5 +/- 5.0 HU vs 47.6 +/- 3.2 HU, all p < 0.05). The fat fraction of the pancreatic tail was the best indicator for distinguishing T2DM patients from the controls (area under the curve: 0.716 (95 % CI: 0.601, 0.832), sensitivity: 64.4 % (95 % CI: 48.7 %, 77.7 %), and specificity: 78.8 % (95 % CI: 60.6 %, 90.4 %)). Conclusion: The DECT fat fractions of the pancreas could be a valuable additional parameter in the detection of T2DM.