Dual-energy Computed Tomography Datasets Research Articles

Multi-reader multiparametric DECT study evaluating different strengths of iterative and deep learning-based image reconstruction techniques.

To perform a multi-reader comparison of multiparametric dual-energy computed tomography (DECT) images reconstructed with deep-learning image reconstruction (DLIR) and standard-of-care adaptive statistical iterative reconstruction-V (ASIR-V). This retrospective study included 100 patients undergoing portal venous phase abdominal CT on a rapid kVp switching DECT scanner. Six reconstructed DECT sets (ASIR-V and DLIR, each at three strengths) were generated. Each DECT set included 65 keV monoenergetic, iodine, and virtual unenhanced (VUE) images. Using a Likert scale, three radiologists performed qualitative assessments for image noise, contrast, small structure visibility, sharpness, artifact, and image preference. Quantitative assessment was performed by measuring attenuation, image noise, and contrast-to-noise ratios (CNR). For the qualitative analysis, Gwet's AC2 estimates were used to assess agreement. DECT images reconstructed with DLIR yielded better qualitative scores than ASIR-V images except for artifacts, where both groups were comparable. DLIR-H images were rated higher than other reconstructions on all parameters (p-value < 0.05). On quantitative analysis, there was no significant difference in the attenuation values between ASIR-V and DLIR groups. DLIR images had higher CNR values for the liver and portal vein, and lower image noise, compared to ASIR-V images (p-value < 0.05). The subgroup analysis of patients with large body habitus (weight ≥ 90 kg) showed similar results to the study population. Inter-reader agreement was good-to-very good overall. Multiparametric post-processed DECT datasets reconstructed with DLIR were preferred over ASIR-V images with DLIR-H yielding the highest image quality scores. Deep-learning image reconstruction in dual-energy CT demonstrated significant benefits in qualitative and quantitative image metrics compared to adaptive statistical iterative reconstruction-V. Dual-energy CT (DECT) images reconstructed using deep-learning image reconstruction (DLIR)showed superior qualitative scores compared to adaptive statistical iterative reconstruction-V (ASIR-V) reconstructed images, except for artifacts where both reconstructions were rated comparable. While there was no significant difference in attenuation values between ASIR-V and DLIR groups, DLIR images showed higher contrast-to-noise ratios (CNR) for liver and portal vein, and lower image noise (p value < 0.05). Subgroup analysis of patients with large body habitus (weight≥90 kg) yielded similar findings to the overall study population.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

Dual-energy computed tomography for improved contrast on a polyphase graphitic ore

Proper ore characterisation is essential for understanding ore deposits and developing efficient mineral processing flow sheets. Conventional mineralogical and chemical techniques are usually used to study ores, but they can be destructive and, in some cases, provide only 2D information. Computed tomography (CT) is an emerging technology in the raw materials sector enabling the non-destructive 3D analysis of the ore mineralogy and microstructure. However, single-energy CT (SECT) has some limitations concerning the accurate imaging and differentiation of polyphase geomaterials comprising a broad range of attenuation properties. By contrast, dual-energy CT (DECT) uses two different X-ray energies to acquire data, which can be used to distinguish between materials with similar attenuation properties. This study explored the application of DECT for the analysis of a polyphase graphitic ore. A sequential fusion approach was utilized to combine data obtained from different X-ray energy scans at high spatial resolution, and varying weighting factors were applied to determine the optimal contribution of each energy level and spectrum. Both, SECT and DECT datasets were quantitatively evaluated based on the contrast-to-noise-ratio (CNR) and Q factor. The findings demonstrate that DECT significantly improves image contrast compared to SECT while further increases image sharpness. As a result, DECT may enable more accurate segmentation and, therefore, more accurate quantitative 3D analysis of graphite ores.

Dose-Response Relationship Between Radiation Therapy and Loss of Lung Perfusion Comparing Positron Emission Tomography and Dual-Energy Computed Tomography in Non-Small Cell Lung Cancer

Background: Radiotherapy treatment for non-small cell lung cancer (NSCLC) may result in radiation damage to the perfused lung. The loss in perfusion may be measured from positron tomography emission (PET) perfusion imaging, however, this modality may not be widely available. Dual-energy computed tomography (DECT) with contrast may be an alternative to PET/CT. The purpose of this work is to investigate the equivalence of dose-response curves (DRC) determined from PET and DECT in NSCLC. Methods: PET and DECT datasets from the prospective clinical trial BLINDED FOR REVIEW (BLINDED FOR REVIEW) were included in this pre-planned trial analysis. Patients underwent 68Ga-macroaggregated albumin (68Ga-MAA) PET/CT examination and DECT with contrast on the same day at baseline/3-months/12-months post-treatment. The perfused lung was defined from a threshold based on the maximum standardized uptake value (%SUVmax)/iodine concentration (%IoMax) in PET/DECT. The equivalence between PET and DECT DRC was established by comparing (1) the average of the normalized overlap of the two DRCs (aNO), ranging from 0 (no overlap) to 1 (perfect overlap), and (2) the slope of a linear model applied to DRCs. Results: Out of the 19 patients enrolled in the clinical trial, 14/10 patients had a post-treatment imaging session at median = 4.5 months/13.5 months, respectively. With 30%SUVmax/35%IoMax, aNO was maximized and the difference between PET and DECT slope of the linear model was minimized at each time point (slope = 0.76%/Gy / 0.75%/Gy at 3 months and 0.86%/Gy / 0.87%/Gy at 12 months determined from PET / DECT). Conclusions: Dose-response relationship determined from DECT was comparable to PET at 3- and 12-months post-treatment in NSCLC patients.

Image Quality Evaluation in Dual-Energy CT of the Chest, Abdomen, and Pelvis in Obese Patients With Deep Learning Image Reconstruction.

The aim of this study was to evaluate image quality in vascular and oncologic dual-energy computed tomography (CT) imaging studies performed with a deep learning (DL)-based image reconstruction algorithm in patients with body mass index of ≥30. Vascular and multiphase oncologic staging dual-energy CT examinations were evaluated. Two image reconstruction algorithms were applied to the dual-energy CT data sets: standard of care Adaptive Statistical Iterative Reconstruction (ASiR-V) and TrueFidelity DL image reconstruction at 2 levels (medium and high). Subjective quality criteria were independently evaluated by 4 abdominal radiologists, and interreader agreement was assessed. Signal-to-noise ratio (SNR) and contrast-to-noise ratio were compared between image reconstruction methods. Forty-eight patients were included in this study, and the mean patient body mass index was 39.5 (SD, 7.36). TrueFidelity-High (DL-High) and TrueFidelity-Medium (DL-Med) image reconstructions showed statistically significant higher Likert scores compared with ASiR-V across all subjective image quality criteria ( P < 0.001 for DL-High vs ASiR-V; P < 0.05 for DL-Med vs ASiR-V), and SNRs for aorta and liver were significantly higher for DL-High versus ASiR-V ( P < 0.001). Contrast-to-noise ratio for aorta and SNR for aorta and liver were significantly higher for DL-Med versus ASiR-V ( P < 0.05). TrueFidelity DL image reconstruction provides improved image quality compared with ASiR-V in dual-energy CTs obtained in obese patients.

Neuroforamen stenosis remains a challenge in conventional computed tomography and new dual-energy techniques

Lumbar foraminal stenosis may be caused by osseous and soft tissue structures. Thus, both computed tomography (CT) and magnetic resonance imaging (MRI) play a role in the diagnostic algorithm. Recently, dual-energy CT (DECT) has been introduced for the detection of spinal disorders. Our study’s aim was to investigate the diagnostic accuracy of collagen-sensitive maps derived from DECT in detecting lumbar foraminal stenosis compared with standard CT and MRI. We retrospectively reviewed CT, DECT, and MRI datasets in patients with vertebral fractures between January 2015 and February 2017. Images were scored for presence and type of lumbar neuroforaminal stenosis. Contingency tables were calculated to determine diagnostic accuracy and interrater agreement was evaluated. 612 neuroforamina in 51 patients were included. Intraclass correlation coefficients for interrater reliability in detecting foraminal stenoses were 0.778 (95%-CI 0.643–0.851) for DECT, 0.769 (95%-CI 0.650–0.839) for CT, and 0.820 (95%-CI 0.673–0.888) for MRI. Both DECT and conventional CT showed good diagnostic accuracy in detecting lumbar foraminal stenosis but low sensitivities in detecting discoid stenosis. Thus, even though previous studies suggest that DECT has high diagnostic accuracy in assessing lumbar disc pathologies, we show that DECT does not provide additional information for detecting discoid stenosis compared with conventional CT.

Vertebral disk morphology of the lumbar spine: a retrospective analysis of collagen-sensitive mapping using dual-energy computed tomography

ObjectivesTo investigate the diagnostic accuracy of collagen-sensitive maps derived from dual-energy computed tomography (DECT) for the detection of lumbar disk pathologies in a feasibility setting.Materials and methodsWe retrospectively reviewed magnetic resonance imaging (MRI), computed tomography (CT), and DECT datasets acquired in patients who underwent periradicular therapy of the lumbar spine from June to December 2019. Three readers scored DECT collagen maps, conventional CT, and MRI for presence, type, and extent of disk pathology. Contingency table analyses were performed to determine diagnostic accuracy using MRI as standard of reference. Interrater agreement within and between imaging modalities was evaluated by computing intraclass correlation coefficients (ICCs) and Cohen’s kappa. Correlation between sum scores of anteroposterior disk displacement was determined by calculation of a paired t test.ResultsIn 21 disks in 13 patients, DECT had a sensitivity of 0.87 (0.60–0.98) and specificity of 1.00 (0.54–1.00) for the detection of disk pathology. Intermodality agreement for anteroposterior disk displacement was excellent for DECT (ICC 0.963 [0.909–0.985]) and superior to CT (ICC 0.876 [0.691–0.95]). For anteroposterior disk displacement, DECT also showed greater within-modality interrater agreement (ICC 0.820 [0.666–0.916]) compared with CT (ICC 0.624 [0.39–0.808]).ConclusionOur data suggest that collagen-sensitive imaging has an added benefit, allowing more accurate evaluation of the extent of disk displacement with higher interrater reliability. Thus, DECT could provide useful diagnostic information in patients undergoing CT for other indications or with contraindications to MRI.

Computed Tomography Thermography for Ablation Zone Prediction in Microwave Ablation and Cryoablation: Advantages and Challenges in an Ex Vivo Porcine Liver Model.

The aim of this study was to investigate the diagnostic accuracy of computed tomography (CT) for the prediction of ablation zones from microwave ablation (MWA) and cryoablation (CA) in an ex vivo porcine liver model. Sequential (30 seconds) CT scans were acquired during and after MWA and CA in an ex vivo porcine liver model. We generated 120-kVp equivalent reconstructions of generic dual-energy CT data sets, and comprehensive region-of-interest measurements were statistically correlated with invasive temperature monitoring using Pearson correlation coefficient. Binary logistic regression was performed for prediction of successful ablation. With the use of pooled data from 6 lesions in 2 separate experiments, correlation analysis of attenuation in Hounsfield units (HU) and temperature yielded r = -0.79 [confidence interval (CI), -0.85 to -0.71] for MWA and r = 0.62 (CI, 0.55 to 0.67) for CA.For MWA, there was a linear association between attenuation and temperature up to 75°C; thus, linear regression yielded a slope of -2.00 HU/°C (95% CI, -1.58 to -2.41). For CA, a linear association between attenuation and temperature was observed in the cooling phase with a slope of 2.11 HU/°C (95% CI, 1.79 to 2.58). In MWA treatment, binary logistic regression separated less than 70°C and greater than 70°C with 89.2% accuracy. Within the ice ball, temperatures above and below -20°C were distinguished with 65.3% accuracy. Our experiments reveal several difficulties in predicting ablation zone temperature from CT attenuation. Microwave ablation leads to gas production in the tissue, which degrades the accuracy of noninvasive temperature measurement, especially at higher temperatures. In CA, CT thermometry is limited by ice ball formation, which leads to homogeneous attenuation, nearly independent of temperature. Further research is needed to define the role of CT thermography in ablation zone monitoring in liver malignancies.

Dual-energy CT collagen density mapping of wrist ligaments reveals tissue remodeling in CPPD patients: first results from a clinical cohort

ObjectivesTo evaluate differences in collagen density as detected by dual-energy computed tomography (DECT) of wrist ligaments between patients with calcium pyrophosphate-dihydrate deposition disease (CPPD) and a control group in order to gain insight into changes of the extracellular matrix in response to crystal deposition.Materials and methodsThis retrospective study included 28 patients (18 with CPPD, 10 controls) who underwent DECT of the wrist. Collagen density maps were reconstructed from the DECT datasets and used to measure densities in regions of interest (ROIs) placed in the scapholunate (SL) ligament (dorsal, palmar, proximal), lunotriquetral (LT) ligament, and extensor carpi radialis (ECR) tendon, (n = 260 measurements). The presence of calcifications on standard CT images in these regions was assessed by a blinded reader. Densities were compared with nonparametric tests, and linear regression analysis was performed to investigate the impact of age, sex, and CT- detected calcium deposition on collagen density.ResultsCollagen density in the SL ligament was significantly higher in CPPD patients than in controls (overall mean: 265.4 ± 32.1 HU vs. 196.3 ± 33.8 HU; p < 0.001). In the ECR tendon, collagen densities did not differ significantly (p = 0.672): 161.3 ± 20.1 HU in CPPD vs. 163.6 ± 12.0 HU in controls. Regression analysis showed that diagnosis, but not age or calcification, had a significant impact on collagen density.ConclusionCollagen density of the SL ligament is significantly higher in CPPD patients than in control patients. Further research is needed to understand these changes in the extracellular matrix of ligaments in CPPD.

Dual-energy CT in the differentiation of crystal depositions of the wrist: does it have added value?

To evaluate the ability of dual-energy computed tomography (DECT) to improve diagnostic discrimination between gout and other crystal arthropathies such as calcium pyrophosphate deposition disease (CPPD) of the wrist in a clinical patient population. This retrospective case-control study included 29 patients with either gout (case group; n = 9) or CPPD (control group; n = 20) who underwent DECT of the wrist for clinically suspected crystal arthropathy. Color-coded urate and enhanced calcium as well as virtual 120 kVe blended images were reconstructed from the DECT datasets. Two independent and blinded readers evaluated each reconstructed dataset for the presence of depositions in 17 predefined regions. Additionally, a global diagnosis was made first for 120 kVe images only, based solely on morphologic criteria, and subsequently for all reconstructed images. Sensitivity for the global diagnosis of gout was 1.0 (95% CI 0.63-1) for both DECT and 120 kVe images with specificities of 0.70 (95% CI 0.46-0.87) for DECT and 0.80 (95% CI 0.56-0.93) for 120 kVe images. Color-coded DECT images did not detect more depositions than monochrome standard CT images. Discrimination of crystal arthropathies of the wrist is limited using DECT and primarily relying on color-coded images. Evaluation of morphologic criteria on standard CT images is essential for accurate diagnosis.

Metal About the Hip and Artifact Reduction Techniques: From Basic Concepts to Advanced Imaging.

Promising outcomes of hip replacement interventions in this era of aging populations have led to higher demands for hip arthroplasty procedures. These require effective methods and techniques for the detection of postoperative outcomes and complications. Based on the presence or absence of radiographic findings, magnetic resonance imaging (MRI) and computed tomography (CT) may be required to detect and further characterize different causes of failing implants. Yet metal-related artifacts degrade image quality and pose significant challenges for adequate image quality. To mitigate such artifacts in MRI, a set of techniques, collectively known as metal artifact reduction sequence (MARS) MRI, were developed that optimize the framework of the conventional pulse sequences and exploit novel multispectral and multispatial imaging methods such as Slice Encoding for Metal Artifact Correction (SEMAC) and Multi-Acquisition Variable-Resonance Image Combination (MAVRIC). Metal-induced artifacts on CT can be effectively reduced with virtual monochromatic reconstruction of dual-energy CT data sets, metal artifact reduction reconstruction algorithms, and postprocessing image visualization techniques.

Assessment of cardiovascular risk profile based on measurement of tophus volume in patients with gout.

Hyperuricemia and gout are associated with increased risk of cardiovascular disease and metabolic syndrome. The aim of this study was to evaluate the correlation of total tophus volumes, measured using dual-energy computed tomography, with cardiovascular risk and the presence of metabolic syndrome. Dual-energy computed tomography datasets from 91 patients with a diagnosis of gout were analyzed retrospectively. Patients who received urate lowering therapy were excluded to avoid the effect on tophus volume. The total volumes of tophaceous deposition were quantified using automated volume assessment software. The 10-year cardiovascular risk using the Framingham Risk Score and metabolic syndrome based on the Third Adult Treatment Panel criteria were estimated. Fifty-five and 36 patients with positive and negative dual-energy computed tomography results, respectively, were assessed. Patients with positive dual-energy computed tomography results showed significantly higher systolic blood pressure, diastolic blood pressure, fasting glucose, and higher prevalence of chronic kidney disease, compared with those with negative dual-energy computed tomography results. The total tophus volumes were significantly correlated with the Framingham Risk Score, and the number of metabolic syndrome components (r = 0.22 and p = 0.036 and r = 0.373 and p < 0.001, respectively). The total tophus volume was one of the independent prognostic factors for the Framingham Risk Score in a multivariate analysis. This study showed the correlation of total tophus volumes with cardiovascular risk and metabolic syndrome-related comorbidities. A high urate burden could affect unfavorable cardiovascular profiles.

Impact of noise-optimized virtual monoenergetic dual-energy computed tomography on image quality in patients with renal cell carcinoma

ObjectiveThe aim of this study was to evaluate the impact of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique on image quality and lesion delineation in patients with renal cell carcinoma (RCC) undergoing abdominal dual-energy computed tomography (DECT). Materials and methodsFifty-two patients (33 men; 61.5±13.6years) with RCC underwent contrast-enhanced DECT during the corticomedullary and nephrogenic phase of renal enhancement. DECT datasets were reconstructed with standard linearly-blended (M_0.6), as well as traditional virtual monoenergetic (VMI) and VMI+ algorithms in 10-keV increments from 40 to 100 keV. Contrast-to-noise (CNR) and tumor-to-cortex ratios for corticomedullary- and nephrogenic-phase images were objectively measured by a radiologist with 3 years of experience. Subjective image quality and RCC delineation were evaluated by three independent radiologists. ResultsGreatest CNR values were found for 40-keV VMI+ series in both corticomedullary- (8.9±4.9) and nephrogenic-phase (7.1±4.6) images and were significantly higher compared to all other reconstructions (P<0.001). Furthermore, tumor-to-cortex ratios were highest for 40-keV nephrogenic-phase VMI+ (2.1±3.5; P≤0.016), followed by 50-keV and 60-keV VMI+ (2.0±3.2 and 1.8±2.8, respectively). Qualitative image quality scored highest for 50-keV VMI+ series in corticomedullary-phase reconstructions and 60-keV in nephrogenic-phase reconstructions (P≤0.031). Highest scores for lesion delineation were assigned for 40-keV VMI+ reconstructions (P≤0.074). ConclusionLow-keV VMI+ reconstructions lead to improved image quality and lesion delineation of corticomedullary- and nephrogenic-phase DECT datasets in patients with RCC.

Noise-optimized virtual monoenergetic dual-energy computed tomography: optimization of kiloelectron volt settings in patients with gastrointestinal stromal tumors.

The aim of this study was to evaluate the impact of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique on quantitative and qualitative image analysis in patients with gastrointestinal stromal tumors (GISTs) at dual-energy computed tomography (DECT) of the abdomen. Forty-five DECT datasets of 21 patients (14 men; 63.7±9.2years) with GISTs were reconstructed with the standard linearly blended (M_0.6) and VMI+ and traditional virtual monoenergetic (VMI) algorithm in 10-keV increments from 40 to 100keV. Attenuation measurements were performed in GIST lesions and abdominal metastases to calculate objective signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Five-point scales were used to evaluate overall image quality, lesion delineation, image sharpness, and image noise. Quantitative image parameters peaked at 40-keV VMI+ series (SNR 27.8±13.0; CNR 26.3±12.7), significantly superior to linearly blended (SNR 16.8±7.3; CNR 13.6±6.9) and all VMI series (all P<0.001). Qualitative image parameters were highest for 60-keV VMI+ reconstructions regarding overall image quality and image sharpness (median 5, respectively; P≤0.023). Qualitative assessment of lesion delineation peaked in 40 and 50-keV VMI+ series (median 5, respectively). Image noise was superior in 90 and 100-keV VMI and VMI+ reconstructions (all medians 5). Low-keV VMI+ reconstructions significantly increase SNR and CNR of GISTs and improve quantitative and qualitative image quality of abdominal DECT datasets compared to traditional VMI and standard linearly blended image series.

Quantitative image variables reflect the intratumoral pathologic heterogeneity of lung adenocarcinoma.

We aimed to compare quantitative radiomic parameters from dual-energy computed tomography (DECT) of lung adenocarcinoma and pathologic complexity.A total 89 tumors with clinical stage I/II lung adenocarcinoma were prospectively included. Fifty one radiomic features were assessed both from iodine images and non-contrast images of DECT datasets. Comprehensive histologic subtyping was evaluated with all surgically resected tumors. The degree of pathologic heterogeneity was assessed using pathologic index and the number of mixture histologic subtypes in a tumor. Radiomic parameters were correlated with pathologic index. Tumors were classified as three groups according to the number of mixture histologic subtypes and radiomic parameters were compared between the three groups.Tumor density and 50th through 97.5th percentile Hounsfield units (HU) of histogram on non-contrast images showed strong correlation with the pathologic heterogeneity. Radiomic parameters including 75th and 97.5th percentile HU of histogram, entropy, and inertia on 1-, 2- and 3 voxel distance on non-contrast images showed incremental changes while homogeneity showed detrimental change according to the number of mixture histologic subtypes (all Ps < 0.05).Radiomic variables from DECT of lung adenocarcinoma reflect pathologic intratumoral heterogeneity, which may help in the prediction of intratumoral heterogeneity of the whole tumor.

Determinants of Detection of Stones and Calcifications in the Hepatobiliary System on Virtual Nonenhanced Dual-energy CT.

Objective To retrospectively determine the features of stones and calcifications in hepatobiliary system on virtual nonenhanced (VNE) dual-energy computed tomography (CT), and to evaluate the possibility of VNE images in diagnosis for those lesions.Methods A total of 128 gall stones and calcifications of the liver found in 110 patients were examined with triple phase abdominal CT scan from July 2007 to December 2011, in which true nonenhanced (TNE) phase and arterial phase were performed with single-energy CT (120 kVp) and portal venous phase was performed with dual-energy CT (100 kVp and 140 kVp). VNE images were generated from the portal venous phase dual-energy CT data sets by using commercially VNC software. The mean CT values for the stone, liver, bile and paraspinal muscle, mean lesion density and size in area dimension, contrast-to-noise ratio (CNR) of lesion to the liver or bile, and image noise were assessed and compared between VNE and TNE images. The effective dose and size-specific dose estimate (SSDE) were also calculated.Results The mean CT values of the lesions measured on VNE images declined significantly compared with those measured on TNE images (164.51±102.13 vs. 290.72±197.80 HU, P<0.001), so did the lesion-to-liver CNR (10.80±11.82 vs.18.81±17.06, P<0.001) and the lesion-to-bile CNR (17.24±14.41 vs. 21.32±17.31, P<0.001). There was no significant difference in size of lesions area between VNE and TNE images (0.69±0.88 vs. 0.72±0.85 cm2, P=0.062). Compared to the 128 lesions found in TNE images, VNE images showed the same density in 30 (23.4%) lesions, lighter density in 88 (68.8%) lesions, while failed to show 10 (7.8%) lesions, and showed the same size in 61 (47.7%) lesions and smaller size in 57 (44.5%) lesions. The CT cutoff values of lesion and size were 229.21 HU and 0.15 cm2, respectively. The total effective dose for triple phase scan protocol with TNE images was 19.51±7.03 mSv, and the SSDE was 39.84±11.10 mGy. The effective dose for dual phase scan protocol with VNE images instead of TNE images was 13.29±4.89 mSv, and the SSDE was 27.83±9.99 mGy. Compared with TNE images, the effective dose and SSDE of VNE images were down by 32.05%±3.69 % and 30.63%±2.34 %, respectively.Conclusions Although the CT values and CNR of the lesions decreased in VNE images, the lesions of which attenuation greater than 229.21 HU and size larger than 0.15 cm2 could be detected with good reliability and obvious dose reduction. There was good consistency in the size of stones and calcifications in hepatobiliary system between VNE images and TNE images, which ensured the possibility of the clinical application of VNE images.

Tin-filter Enhanced Dual-Energy-CT: Image Quality and Accuracy of CT Numbers in Virtual Noncontrast Imaging

To measure and compare the objective image quality of true noncontrast (TNC) images with virtual noncontrast (VNC) images acquired by tin-filter-enhanced, dual-source, dual-energy computed tomography (DECT) of upper abdomen. Sixty-three patients received unenhanced abdominal CT and enhanced abdominal DECT (100/140 kV with tin filter) in portal-venous phase. VNC images were calculated from the DECT datasets using commercially available software. The mean attenuation of relevant tissues and image quality were compared between the TNC and VNC images. Image quality was rated objectively by measuring image noise and the sharpness of object edges using custom-designed software. Measurements were compared using Student two-tailed t-test. Correlation coefficients for tissue attenuation measurements between TNC and VNC were calculated and the relative deviations were illustrated using Bland-Altman plots. Mean attenuation differences between TNC and VNC (HUTNC - HUVNC) image sets were as follows: right liver lobe -4.94 Hounsfield units (HU), left liver lobe -3.29 HU, vena cava -2.19 HU, spleen -7.46 HU, pancreas 1.29 HU, fat -11.14 HU, aorta 1.29 HU, bone marrow 36.83 HU (all P < .05); right kidney 0.46 HU, left kidney 0.56 HU, vena portae -0.48 HU and muscle -0.62 HU (nonsignificant). Good correlations between VNC and TNC series were observed for liver, vena portae, kidneys, pancreas, muscle and bone marrow (Pearson's correlation coefficient ≥0.75). Mean image noise was significantly higher in TNC images (P < .0001). Measurements of edge sharpness revealed no significant differences between VNC and TNC images (P = .19). The Hounsfield units in VNC images closely resemble TNC images in the majority of the organs of the upper abdomen (kidneys, liver, pancreas). In spleen and fat, Hounsfield numbers in VNC images are tend to be higher than in TNC images. VNC images show a low image noise and satisfactory edge sharpness. Other criteria of image quality and the depiction of certain lesions need to be evaluated additionally.

Dual-Energy CT with Single- and Dual-Source Scanners: Current Applications in Evaluating the Genitourinary Tract

Several promising clinical applications for dual-energy computed tomography (CT) in genitourinary imaging have been reported. Dual-energy CT not only provides excellent morphologic detail but also can supply material-specific and quantitative information that may be particularly useful in genitourinary imaging. Dual-energy CT has unique capabilities for characterizing renal lesions by quantifying iodine content and helping identify the mineral contents of renal stones, information that is important for patient care. Virtual unenhanced images reconstructed from dual-energy CT datasets can be useful for detecting calculi within the iodine-filled urinary collecting system, potentially reducing the need for an unenhanced scanning phase at CT urography. Although the underlying principles of dual-energy CT are the same regardless of scanner type, single-source dual-energy scanners with fast kilovoltage switching differ from dual-source dual-energy scanners both in image data acquisition and in processing methods; an understanding of these differences may help optimize dual-energy CT genitourinary protocols. Dual-energy CT performed with a dual-source scanner or with a single-source scanner with fast kilovoltage switching also has some important limitations. Further advances in scanning protocols and refinement of processing techniques to reduce image noise may lead to more widespread use of dual-energy CT.