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OBJECTIVE: The aim of this study was to investigate the feasibility of muscle CT radiomics in identifying gout. MATERIALS AND METHODS: A total of 30 gout patients and 20 non-gout cases with CT examinations of ankles were analyzed by using the methods of CT radiomics. CT radiomics features of the soleus muscle were extracted using the software of a 3D slicer, and then gout cases and non-gout cases were compared. The radiomics features that were significantly different between the two groups were then processed with machine learning methods. Receiver operating characteristic curve analysis was used to evaluate the diagnostic performance. RESULTS: Five CT radiomics features were significantly different between gout cases and non-gout cases (P < 0.05). In the logic regression, the AUC, sensitivity, specificity, and accuracy were 0.738, 77% (46/60), 70% (28/40), and 74% (74/100), respectively. In the Random forest, Xgboost, and support vector machine analysis, the accuracy was 0.901, 0.833, and 0.875, respectively. CONCLUSION: From this study, it can be concluded that muscle CT radiomics is feasible in identifying gout.

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AIM: To assess the correlation of quantitative data of pulmonary Perfused Blood Volume (PBV) on Dual-Energy CT (DECT) datasets in patients with moderate - severe Pulmonary Emphysema (PE) with Lung Perfusion Scintigraphy (LPS) as the reference standard. The secondary endpoints are the correlation between the CT densitometric analysis and the visual assessment of parenchymal destruction with PBV. MATERIALS AND METHODS: Patients with moderate - severe PE candidate to Lung Volumetric Reduction (LVR), with available a pre-procedural LS and a contrast-enhanced DECT were retrospectively included. DECT studies were performed with a 3rd generation Dual-Source CT and the PBV was obtained with a 3-material decomposition algorithm. The CT densitometric analysis was performed with a dedicated commercial software (Pulmo3D). The Goddard Score was used for visual assessment. The perfusion LS were performed after the administration of albumin macroaggregates labeled with 99mTechnetium. The image revision was performed by two radiologists or nuclear medicine physicians blinded, respectively, to LS and DECT data. The statistical analysis was performed with nonparametric tests. RESULTS: Thirty-one patients (18 males, median age 69 y.o., interquartile range 62-71 y.o.) with moderate - severe PE (Median Goddard Score 14/20 and 31% of emphysematous parenchyma at quantitative CT) candidate to LVR were retrospectively included. The median enhancement on PBV was 17 HU. Significant correlation coefficients were demonstrated between lung PBV and LS, poor in apical regions (Rho = 0.1-0.2) and fair (Rho = 0.3-0.5) in middle and lower regions. No significant correlations were recorded between the CT densitometric analysis, the visual score, and the PBV. CONCLUSIONS: Lung perfusion with PBV on DECT is feasible in patients with moderate - severe PE candidate to LVR, and has a poor to fair agreement with LPS.

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RATIONALE AND OBJECTIVES: To assess the consistency between ultrasound and dual-energy computed tomography (DECT) for the diagnosis of gout in the knee joint. MATERIALS AND METHODS: The ultrasound and DECT images of 176 knee joints from 167 patients diagnosed with gout at the Gout Specialty Clinic of Qingdao University Affiliated Hospital from February 2022 to December 2023 were retrospectively analyzed. The knee joint was segmented into five anatomical regions: intra-articular, anterior, posterior, medial, and lateral. The location of monosodium urate (MSU) crystal deposition was recorded. Tophi were classified as hypoechogenic, isoechogenic, hyperechogenic, or strongly echogenic. The Kappa test was used to assess the consistency between the two examination methods in different regions of the knee joint. The McNemar chi-square test was utilized to conduct a differential analysis between the DECT and ultrasound results. The chi-square test was used to assess differences in the rate of tophi detection with different echogenicities by DECT. Pearson's correlation coefficient was used to assess the correlation between MSU crystal deposition volume and clinically relevant indicators. RESULTS: Double contour (61.4%) was the most common intra-articular ultrasound sign. In the extra-articular region, MSU crystals were commonly deposited in and around the popliteal groove region (ultrasound: 52.3%; DECT: 60.0%). Corresponding MSU deposits on DECT were found in 7 of 54 joints with aggregates detected on ultrasound, and in 15 of 108 joints with DC. Tophi with hyperechogenicity or strong echogenicity were more likely to be detected on DECT than those with hypoechoic or isoechoic features (84.3% and 90.9% vs. 55.1% and 27.8%, respectively). For the assessment of MSU deposits, ultrasound showed an overall higher positive rate than DECT (81.1% vs. 72.2%), with poor consistency between the two examinations (κ = 0.177). In distinct anatomical regions, ultrasound and DECT showed high consistency in the medial (κ = 0.651) and lateral (κ = 0.705) views, with no significant difference. The intra-articular (κ = 0.316) and anterior (κ = 0.346) regions exhibited only fair consistency, with statistically significant diagnostic differences. When exclusively assessing cases with tophi, ultrasound and DECT demonstrated similar consistency in the medial, lateral and anterior views (κ = 0.633, 0.712, and 0.400, respectively), with statistically significant differences. In the intra-articular region, the consistency was reduced (κ = 0.237), and the differences were statistically significant. CONCLUSION: Ultrasound and DECT are effective methods to detect MSU deposition in gout of the knee. However, the consistency between the two techniques varies in different anatomical locations. Clinical assessment should be tailored based on the specific anatomical position. DECT is advantageous for the evaluation of intra-articular MSU deposits, while ultrasound is more sensitive for the early detection of scattered MSU deposits.

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OBJECTIVE: We aimed to evaluate the diagnostic performance of dual-energy computed tomography (DECT) in detecting bone marrow edema (BME) in patients with lower limb joint injuries. METHODS: A thorough literature search was conducted across the PubMed, Embase, and Web of Science databases to identify relevant studies up to April 2024. Studies examining the diagnostic performance of DECT in detecting BME in lower limb joint injuries patients were included. Sensitivity and specificity were evaluated using the inverse variance method and transformed via the Freeman-Tukey double arcsine transformation. Furthermore, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to evaluate the methodological quality of the included studies. RESULTS: This meta-analysis included 17 articles involving 625 patients. The pooled sensitivity, specificity, and AUC for DECT in detecting BME in lower limb joint injuries patients were 0.82 (95 % CI: 0.76-0.87), 0.95 (95 % CI: 0.92-0.97), and 0.95 (95 % CI: 0.93-0.97), respectively. The pooled sensitivity of DECT for detecting BME in knee, hip, and ankle joint injuries was 0.80, 0.84, and 0.80, with no significant difference among these joints (P = 0.55). The pooled specificity for knee, hip, and ankle injuries was 0.95, 0.97, and 0.89. Specificity differed significantly among the joints (P < 0.01), with the highest specificity in hip injuries. CONCLUSIONS: Our meta-analysis indicates that DECT demonstrates high diagnostic performance in detecting BME in patients with lower limb joint injuries, with the highest specificity observed in hip joint injuries. To validate these findings, further larger prospective studies are necessary.

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PURPOSE: Preliminary dual-energy CT studies have shown that low-energy virtual monoenergetic (VMI) + reconstructions can provide superior image quality compared to standard 120 kV CTA series. The purpose of this study is to evaluate the impact of low-energy VMI reconstructions on quantitative and qualitative image quality, vascular contrast, and diagnostic assessability of the carotid artery in patients undergoing photon-counting CTA examinations. MATERIALS AND METHODS: A total of 122 patients (67 male) who had undergone dual-source photon-counting CTA scans of the carotid artery were retrospectively analyzed in this study. Standard 120 kV CT images and low-keV VMI series from 40 to 100 keV with an interval of 15 keV were reconstructed. Quantitative analyses included the evaluation of vascular CT numbers, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). CT number measurements were performed in the common, external, and internal carotid arteries. Qualitative analyses were performed by three board-certified radiologists independently using five-point scales to evaluate image quality, vascular contrast, and diagnostic assessability of the carotid artery. RESULTS: Mean attenuation, CNR and SNR values were highest in 40 keV VMI reconstructions (HU, 1362.32 ± 457.81; CNR, 33.19 ± 12.86; SNR, 34.37 ± 12.89) followed by 55-keV VMI reconstructions (HU, 736.94 ± 150.09; CNR, 24.49 ± 7.11; SNR, 26.25 ± 7.34); all three mean values at these keV levels were significantly higher compared with the remaining VMI series and standard 120 kV CT series (HU, 154.43 ± 23.69; CNR, 16.34 ± 5.47; SNR, 24.44 ± 7.14) (p < 0.0001). The qualitative analysis showed the highest rating scores for 55 keV VMI reconstructions followed by 40 keV and 70 keV VMI series with a significant difference compared to standard 120 kV CT images series regarding image quality, vascular contrast, and diagnostic assessability of the carotid artery (all comparisons, p < 0.01). CONCLUSIONS: Low-keV VMI reconstructions at a level of 40-55 keV significantly improve image quality, vascular contrast, and the diagnostic assessability of the carotid artery compared with standard CT series in photon-counting CTA.

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OBJECTIVE: To evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa). MATERIALS AND METHODS: A retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA). RESULTS: The normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively. CONCLUSION: The proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa.

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Low back pain is the most common musculoskeletal complaint accounting for over 30 million visits to primary care physicians annually. Serious pathology is found in less than 1% of these visits. Therefore it is often a challenge to distinguish worrisome findings requiring further workup and treatment from common complaints of pain. Gout is an inflammatory arthritis that most commonly affects the appendicular skeleton. It is characterized by the saturation of uric acid and deposition of monosodium urate crystals in joints and tissues. Spinal involvement is rare and is not typically considered on the differential diagnosis for a patient presenting with acute low back pain. We present such a case of a 35-year-old male who presented with intractable back pain, highlighting the need to recognize signs and symptoms that raise suspicion for spinal gout.

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Purpose: To determine the feasibility of meniscal imaging using virtual monochromatic images obtained through dualenergy computed tomography (DECT) technique, and to determine which keV levels optimise contrast resolution. Material and methods: All DECT exams were performed on a Discovery CT750 HD system from GE Healthcare. Virtual monochromatic images were reconstructed at 40 keV, 73 keV, 106 keV, and 139 keV. Contrast resolution of the medial and lateral menisci using a 5-point Likert scale at each keV level was determined through a consensus agreement by 2 fellowship-trained musculoskeletal radiologists. Friedman's and Wilcoxon signed rank tests were used to compare visualisation scores across different keV levels. Results: Seventeen knee exams from 10 patients met criteria for inclusion in the study. All patients included in the study cohort were male. The median age of patients was 46 years (interquartile range, 35-53 years). Virtual monochromatic images at 40 keV demonstrated highest contrast resolution of the menisci, with a statistically significant difference between contrast resolution scores at 40 keV and 76 keV, Friedman test: p < 0.0001. Conclusions: Meniscal imaging is feasible using DECT virtual monochromatic images at low keV levels. Improved contrast resolution at these specified KeV values could pave the way for further research in this field to determine its role in the future as an alternative option for assessment of the menisci in patients with contraindications to MRI or in the setting of a periarticular ferromagnetic foreign body obscuring the field of view.

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The article addresses the diagnostic value of the combined use of computed tomography (CT) perfusion and dual-energy CT (DECT) in patients with neuroendocrine tumors. It emphasizes the heterogeneity and complexity of these neoplasms, primarily affecting the gastrointestinal tract, bronchopulmonary system, and pancreas. While conventional CT is widely employed in their diagnosis, the combination of CT perfusion and dual-energy CT offers greater precision, particularly in detecting synchronous tumors and characterizing their vascularization. A clinical case of a patient with chronic abdominal symptoms, whose diagnosis was facilitated using both combined techniques, is presented. The discussion explores how CT perfusion assesses tumor vascularization and how dual-energy CT improves soft tissue differentiation, resulting in increased diagnostic accuracy. It is highlighted that this approach not only enhances detection rates but also positively impacts clinical management and healthcare costs. Therefore, the importance of considering these advanced tools in the diagnosis of neuroendocrine tumors to improve diagnostic precision and efficiency in patient care is underscored.

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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.

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Background: The aim was to assess the diagnostic accuracy of DECT in diagnosing Achilles tendon tears, using MRI as the reference for diagnosis. Methods: This feasibility study conducted prospectively at a single center included consecutive patients suffering from ankle pain who underwent DECT and MRI between April 2023 and October 2023. A total of three radiologists, blinded to the patient's clinical data, assessed the images. Achille Tendon injuries were diagnosed in case of thickened and inflamed tendons or in case of a partial or complete tear. Diagnostic accuracy values of DECT were calculated using a multi-reader approach. Inter-observer agreement was calculated using k statistics. Results: The final study population included 22 patients (mean age 48.5 years). At MRI, Achille's tendon lesion was present in 12 cases (54.5%) with 2 cases of complete rupture, 8 cases of partial tear (5 with tendon retraction), and 2 cases of tendon thickening. The mean thickness of injured tendons was 10 mm. At DECT, R1 was allowed to correctly classify 20/22 cases (90.9%), R2 19/22 cases (86.4%), and R3 18/22 cases (81.8%). At DECT, the mean thickness of the positively scored tendon was 10 mm for R1, 10.2 mm for R2, and 9.8 mm for R3. A very good agreement was achieved with regard to the evaluation of tears (k = 0.94), thickness (k = 0.96), and inflammatory changes (k = 0.82). Overall agreement was very good (k = 0.88). Conclusions: DECT showed a good diagnostic performance in identifying Achille's tendon tears, with respect to MRI.

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We examined the association between texture features using three-dimensional (3D) io-dine density histogram on delayed phase of dual-energy CT (DECT) and expression of programmed death-ligand 1 (PD-L1) using immunostaining methods in non-small cell lung cancer. Consecutive 37 patients were scanned by DECT. Unenhanced and enhanced (3 min delay) images were obtained. 3D texture analysis was performed for each nodule to obtain 7 features (max, min, median, mean, standard deviation, skewness, and kurtosis) from iodine density mapping and extracellular volume (ECV). A pathologist evaluated a tumor proportion score (TPS, %) using PD-L1 immunostaining: PD-L1 high (TPS ≥ 50%) and low or negative expression (TPS < 50%). Associations between PD-L1 expression and each 8 parameter were evaluated using logistic regression analysis. The multivariate logistic regression analysis revealed that skewness and ECV were independent indicators associated with high PD-L1 expression (skewness: odds ratio [OR] 7.1 [95% CI 1.1, 45.6], p = 0.039; ECV: OR 6.6 [95% CI 1.1, 38.4], p = 0.037). In the receiver-operating characteristic analysis, the area under the curve of the combination of skewness and ECV was 0.83 (95% CI 0.67, 0.93) with sensitivity of 64% and specificity of 96%. Skewness from 3D iodine density histogram and ECV on dual energy CT were significant factors for predicting PD-L1 expression.

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BACKGROUND: The challenge of early and rapid diagnosis of periprosthetic joint infection (PJI) remains important. This study aimed to assess the efficacy of dual-energy computed tomography (CT) (DECT) iodine maps for diagnosing PJI in total hip arthroplasty. METHODS: We prospectively enrolled 68 patients who had postoperative joint pain after hip arthroplasty. All patients underwent preoperative DECT iodine imaging to quantify iodine concentration (IC) in periprosthetic tissues during arterial and venous phases. The diagnostic efficacy of DECT iodine maps was evaluated by constructing receiver operating characteristic (ROC) curves according to the Musculoskeletal Infection Society criteria. RESULTS: Compared with erythrocyte sedimentation rate (Area Under the Curve (AUC) = 0.837), polymorphonuclear cell percentage (AUC = 0.703), and C-reactive protein (AUC = 0.837), periprosthetic tissue venous-phase iodine concentration (IC) (AUC = 0.970) and arterial-phase IC (AUC = 0.964) exhibited outstanding discriminative capability between PJI and aseptic failure. The PJI cut-off point was venous IC = 1.225 mg/ml, with a sensitivity of 92.31% and specificity of 90.48%; for arterial IC = 1.065 mg/ml, the sensitivity was 96.15%, and a specificity was 90.70%. CONCLUSION: This study demonstrates the great potential of DECT iodine maps for the diagnosis of PJI around hip arthroplasty, which helps to differentiate between periprosthetic infection and aseptic failure after hip arthroplasty.

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PURPOSE: Our study aimed to compare the accuracy of the effective atomic number (Zeff) of five dual-energy CT (DECT) from three vendors and different generations under different scanning parameters. METHODS: Zeff accuracy of five DECT scanners with twelve tube voltage configurations was evaluated by using the TomoTherapy cheese phantom. The potential dose dependence of the Zeff was investigated using three radiation dose (5, 15, and 25 mGy), and the robustness of Zeff was simulated for different organs of the body by placing the inserts at different positional depths. Bias and mean absolute percentage error (MAPE) were used to characterize the accuracy of Zeff. Data underwent analysis using one-way ANOVA, followed by the Turky and LSD post hoc tests, simple linear regression, and linear mixed models. RESULTS: All tube voltage configurations had a bias of less than 1. Dual layer detector DECT (dl-DECT) -140 kV has the lowest MAPE (1.79 %±1.93 %). The third generation dual source DECT (ds-DECT) and the second generation rapid switch DECT (rs-DECT) have higher MAPE than their predecessor DECT. The results of the linear mixed model showed that tube voltage configuration (F=16.92, p < 0.001) and insert type (F=53.26, p < 0.001) significantly affect the MAPE. In contrast, radiation dose only has a significant effect on the MAPE of rs-DECT. The inserts position does not affect the final MAPE. CONCLUSION: When scanning different inserts, Zeff accuracy varies by vendor and DECT generation. Of all the scanners, dl-DECT had the highest Zeff accuracy. Upgrading DECT generation doesn't lead to higher accuracy, or even lower.

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OBJECTIVE: To investigate the effect of radiomics models obtained from dual-energy CT (DECT) material decomposition images and virtual monoenergetic images (VMIs) in predicting the pathological grading of bladder urothelial carcinoma (BUC). MATERIALS AND METHODS: A retrospective analysis of preoperative DECT examination was conducted on 112 patients diagnosed with BUC. This cohort included 76 cases of high-grade urothelial carcinoma and 36 cases of low-grade urothelial carcinoma. DECT can provide material decomposition images of venous phase Iodine maps and Water maps based on the differences in attenuation of substances, as well as VMIs at 40 to 140 keV (interval 10 keV). A total of 13 image sets were obtained, and radiomics features were extracted and analyzed from each set to achieve preoperative prediction of BUC. The best features related to BUC were identified by recursive feature elimination (RFE), the Minimum Redundancy Maximum Relevance (mRMR), and the Least Absolute Shrinkage and Selection Operator (LASSO) in order. A five-fold cross-validation method was used to divide the samples into training and testing sets, and models for pathological prediction of BUC grading were constructed by a random forest (RF) classifier. Receiver operating curves (ROC) were plotted to evaluate the performance of 13 models obtained from each image set. RESULTS: Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100 keV VMIs included the Dependence Variance of the GLDM feature set. There were no statistically significant differences in the area under the curve (AUC) between the training set and the testing set for all 13 models. In the testing set, the AUCs of the models established through 40 keV to 140 keV (interval of 10 keV) image sets were 0.895, 0.874, 0.855, 0.889, 0.841, 0.868, 0.852, 0.847, 0.889, 0.887 and 0.863 respectively. The AUCs for the models established using the Iodine maps and Water maps image sets were 0.873 and 0.852, respectively. CONCLUSION: Despite the differences in the selected radiomic features from DECT multi-parameter images, the performance of radiomics models in predicting the pathological grading of BUC was not affected by the variations in the types of images used for model training.

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Objective.Proton therapy allows for highly conformal dose deposition, but is sensitive to range uncertainties. Several approaches currently under development measure composition-dependent secondary radiation to monitor the delivered proton rangein-vivo. To fully utilize these methods, an estimate of the elemental composition of the patient's tissue is often needed.Approach.A published dual-energy computed tomography (DECT)-based composition-extraction algorithm was validated against reference compositions obtained with two independent methods. For this purpose, a set of phantoms containing either fresh porcine tissue or tissue-mimicking samples with known, realistic compositions were imaged with a CT scanner at two different energies. Then, the prompt gamma-ray (PG) signal during proton irradiation was measured with a PG detector prototype. The PG workflow used pre-calculated Monte Carlo simulations to obtain an optimized estimate of the sample's carbon and oxygen contents. The compositions were also assessed with chemical combustion analysis (CCA), and the stopping-power ratio (SPR) was measured with a multi-layer ionization chamber. The DECT images were used to calculate SPR-, density- and elemental composition maps, and to assign voxel-wise compositions from a selection of human tissues. For a more comprehensive set of reference compositions, the original selection was extended by 135 additional tissues, corresponding to spongiosa, high-density bones and low-density tissues.Results.The root-mean-square error for the soft tissue carbon and oxygen content was 8.5 wt% and 9.5 wt% relative to the CCA result and 2.1 wt% and 10.3 wt% relative to the PG result. The phosphorous and calcium content were predicted within 0.4 wt% and 1.1 wt% of the CCA results, respectively. The largest discrepancies were encountered in samples whose composition deviated the most from tabulated compositions or that were more inhomogeneous.Significance.Overall, DECT-based composition estimations of relevant elements were in equal or better agreement with the ground truth than the established SECT-approach and could contribute toin-vivodose verification measurements.

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PURPOSE: To evaluate the quality of lung perfusion imaging obtained with photon-counting-detector CT (PCD-CT) in comparison with dual-source, dual-energy CT (DECT). METHODS: Seventy-one consecutive patients scanned with PCD-CT were compared to a paired population scanned with dual-energy on a 3rd-generation DS-CT scanner using (a) for DS-CT (Group 1): collimation: 64 × 0.6 × 2 mm; pitch: 0.55; (b) for PCD-CT (Group 2): collimation: 144 × 0.4 mm; pitch: 1.5; single-source acquisition. The injection protocol was similar in both groups with the reconstruction of perfusion images by subtraction of high- and low-energy virtual monoenergetic images. RESULTS: Compared to Group 1, Group 2 examinations showed: (a) a shorter duration of data acquisition (0.93 ± 0.1 s vs 3.98 ± 0.35 s; p < 0.0001); (b) a significantly lower dose-length-product (172.6 ± 55.14 vs 339.4 ± 75.64 mGy·cm; p < 0.0001); and (c) a higher level of objective noise (p < 0.0001) on mediastinal images. On perfusion images: (a) the mean level of attenuation did not differ (p = 0.05) with less subjective image noise in Group 2 (p = 0.049); (b) the distribution of scores of fissure visualization differed between the 2 groups (p < 0.0001) with a higher proportion of fissures sharply delineated in Group 2 (n = 60; 84.5% vs n = 26; 26.6%); (c) the rating of cardiac motion artifacts differed between the 2 groups (p < 0.0001) with a predominance of examinations rated with mild artifacts in Group 2 (n = 69; 97.2%) while the most Group 1 examinations showed moderate artifacts (n = 52; 73.2%). CONCLUSION: PCD-CT acquisitions provided similar morphologic image quality and superior perfusion imaging at lower radiation doses. CLINICAL RELEVANCE STATEMENT: The improvement in the overall quality of perfusion images at lower radiation doses opens the door for wider applications of lung perfusion imaging in clinical practice. KEY POINTS: The speed of data acquisition with PCD-CT accounts for mild motion artifacts. Sharply delineated fissures are depicted on PCD-CT perfusion images. High-quality perfusion imaging was obtained with a 52% dose reduction.

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Dual-energy computed tomography (DECT) has emerged as a transformative tool in the past decade. Initially employed in gout within the field of rheumatology to distinguish and quantify monosodium urate crystals through its dual-material discrimination capability, DECT has since broadened its clinical applications. It now encompasses various rheumatic diseases, employing advanced techniques such as bone marrow edema assessment, iodine mapping, and collagen-specific imaging. This review article aims to examine the unique characteristics of DECT, discuss its strengths and limitations, illustrate its applications for accurately evaluating various rheumatic diseases in clinical practice, and propose future directions for DECT in rheumatology.

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Objective.The study aimed to generate synthetic contrast-enhanced Dual-energy CT (CE-DECT) images from non-contrast single-energy CT (SECT) scans, addressing the limitations posed by the scarcity of DECT scanners and the health risks associated with iodinated contrast agents, particularly for high-risk patients.Approach.A conditional denoising diffusion probabilistic model (C-DDPM) was utilized to create synthetic images. Imaging data were collected from 130 head-and-neck (HN) cancer patients who had undergone both non-contrast SECT and CE-DECT scans.Main Results.The performance of the C-DDPM was evaluated using Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Peak Signal-to-Noise Ratio (PSNR). The results showed MAE values of 27.37±3.35 Hounsfield Units (HU) for high-energy CT (H-CT) and 24.57±3.35HU for low-energy CT (L-CT), SSIM values of 0.74±0.22 for H-CT and 0.78±0.22 for L-CT, and PSNR values of 18.51±4.55 decibels (dB) for H-CT and 18.91±4.55 dB for L-CT.Significance.The study demonstrates the efficacy of the deep learning model in producing high-quality synthetic CE-DECT images, which significantly benefits radiation therapy planning. This approach provides a valuable alternative imaging solution for facilities lacking DECT scanners and for patients who are unsuitable for iodine contrast imaging, thereby enhancing the reach and effectiveness of advanced imaging in cancer treatment planning.

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Computed tomography (CT) is a common modality employed for musculoskeletal imaging. Conventional CT techniques are useful for the assessment of trauma in detection, characterization and surgical planning of complex fractures. CT arthrography can depict internal derangement lesions and impact medical decision making of orthopedic providers. In oncology, CT can have a role in the characterization of bone tumors and may elucidate soft tissue mineralization patterns. Several advances in CT technology have led to a variety of acquisition techniques with distinct clinical applications. These include four-dimensional CT, which allows examination of joints during motion; cone-beam CT, which allows examination during physiological weight-bearing conditions; dual-energy CT, which allows material decomposition useful in musculoskeletal deposition disorders (e.g., gout) and bone marrow edema detection; and photon-counting CT, which provides increased spatial resolution, decreased radiation, and material decomposition compared to standard multi-detector CT systems due to its ability to directly translate X-ray photon energies into electrical signals. Advanced acquisition techniques provide higher spatial resolution scans capable of enhanced bony microarchitecture and bone mineral density assessment. Together, these CT acquisition techniques will continue to play a substantial role in the practices of orthopedics, rheumatology, metabolic bone, oncology, and interventional radiology.

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