Dual-energy CT Research Articles

Improved Pulmonary Artery Evaluation Using High-Pitch Photon-Counting CT Compared to High-Pitch Conventional or Routine-Pitch Conventional Dual-Energy CT.

Pulmonary CT angiography (CTA) to detect pulmonary emboli can be performed using conventional dual-source CT with single-energy acquisition at high-pitch (high-pitch conventional CT), which minimizes motion artifacts, or routine-pitch, dual-energy acquisitions (routine-pitch conventional DECT), which maximize iodine signal. We compared iodine signal, radiation dose, and motion artifacts of pulmonary CTA between these conventional CT modalities and dual-source photon-counting detector CT with high-pitch, multienergy acquisitions (high-pitch photon-counting CT). Consecutive clinically indicated pulmonary CTA exams were collected. CT number/noise was measured from the main to right lower lobe segmental pulmonary arteries using 120 kV threshold low, 120 kV, and mixed kV (0.6 linear blend) images. Three radiologists reviewed anonymized, randomized exams, rating them using a 4- or 5-point Likert scale (1 = worst, and 4/5 = best) for contrast enhancement in pulmonary arteries, motion artifacts in aortic root to subsegmental pulmonary arteries, lung image quality; pulmonary blood volume (PBV) map image quality (for multienergy or dual-energy exams), and contribution to reader confidence. One hundred fifty patients underwent high-pitch photon-counting CT (n = 50), high-pitch conventional CT (n = 50), and routine-pitch conventional DECT (n = 50). High-pitch photon-counting CT had lower radiation dose (CTDIvol: 8.1 ± 2.5 vs 9.6 ± 6.8 and 16.2 ± 8.5 mGy, respectively; P < 0.001), and routine-pitch conventional DECT had significantly less contrast (P < 0.009). CT number and CNR measurements were significantly greater at high-pitch photon-counting CT (P < 0.001). Across readers, high-pitch photon-counting CT demonstrated significantly higher subjective contrast enhancement in the pulmonary arteries compared to the other modalities (4.7 ± 0.6 vs 4.4 ± 0.7 vs 4.3 ± 0.7; P = 0.011) and lung image quality (3.4 ± 0.5 vs 3.1 ± 0.5 vs 3.1 ± 0.5; P = 0.013). High-pitch photon-counting CT and high-pitch conventional CT had fewer motion artifacts at all levels compared to DECT (P < 0.001). High-pitch photon-counting CT PBV maps had superior image quality (P < 0.001) and contribution to reader confidence (P < 0.001) compared to routine-pitch conventional DECT. High-pitch photon-counting pulmonary CTA demonstrated higher contrast in pulmonary arteries at lower radiation doses with improved lung image quality and fewer motion artifacts compared to high-pitch conventional CT and routine-pitch conventional dual-energy CT.

Impact of multi-parameter images obtained from dual-energy CT on radiomics to predict pathological grading of bladder urothelial carcinoma.

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). 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 140keV (interval 10keV). 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. Despite the notable differences in the best radiomics features chosen from each image set, all the features selected from 40 to 100keV 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 40keV to 140keV (interval of 10keV) 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. 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.

Dual-energy computed tomography with new virtual monoenergetic image reconstruction enhances prostate lesion image quality and improves the diagnostic efficacy for prostate cancer

BackgroundProstate cancer is one of the most common malignant tumors in middle-aged and elderly men and carries significant prognostic implications, and recent studies suggest that dual-energy computed tomography (DECT) utilizing new virtual monoenergetic images can enhance cancer detection rates. This study aimed to assess the impact of virtual monoenergetic images reconstructed from DECT arterial phase scans on the image quality of prostate lesions and their diagnostic performance for prostate cancer.MethodsWe conducted a retrospective analysis of 83 patients with prostate cancer or prostatic hyperplasia who underwent DECT scans at Meizhou People’s Hospital between July 2019 and December 2023. The variables analyzed included age, tumor diameter and serum prostate-specific antigen (PSA) levels, among others. We also compared CT values, signal-to-noise ratio (SNR), subjective image quality ratings, and contrast-to-noise ratio (CNR) between virtual monoenergetic images (40–100 keV) and conventional linear blending images. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic efficacy of virtual monoenergetic images (40 keV and 50 keV) compared to conventional images.ResultsVirtual monoenergetic images at 40 keV showed significantly higher CT values (168.19 ± 57.14) compared to conventional linear blending images (66.66 ± 15.5) for prostate cancer (P < 0.001). The 50 keV images also demonstrated elevated CT values (121.73 ± 39.21) compared to conventional images (P < 0.001). CNR values for the 40 keV (3.81 ± 2.13) and 50 keV (2.95 ± 1.50) groups were significantly higher than the conventional blending group (P < 0.001). Subjective evaluations indicated markedly better image quality scores for 40 keV (median score of 5) and 50 keV (median score of 5) images compared to conventional images (P < 0.05). ROC curve analysis revealed superior diagnostic accuracy for 40 keV (AUC: 0.910) and 50 keV (AUC: 0.910) images based on CT values compared to conventional images (AUC: 0.849).ConclusionsVirtual monoenergetic images reconstructed at 40 keV and 50 keV from DECT arterial phase scans substantially enhance the image quality of prostate lesions and improve diagnostic efficacy for prostate cancer.

Validation of dual-energy CT-based composition analysis using fresh animal tissues and composition-optimized tissue equivalent samples

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 range in-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 to in-vivo dose verification measurements.

Differentiation of intrathoracic lymph node histopathology by volumetric dual energy CT radiomic analysis

PurposeTo determine the performance of volumetric dual energy low kV and iodine radiomic features for the differentiation of intrathoracic lymph node histopathology, and influence of contrast protocol. Materials and methodsIntrathoracic lymph nodes with histopathologic correlation (neoplastic, granulomatous sarcoid, benign) within 90 days of DECT chest imaging were volumetrically segmented. 1691 volumetric radiomic features were extracted from iodine maps and low-kV images, totaling 3382 features. Univariate analysis was performed using 2-sample t-test and filtered for false discoveries. Multivariable analysis was used to compute AUCs for lymph node classification tasks. Results129 lymph nodes from 72 individuals (mean age 61 ± 15 years) were included, 52 neoplastic, 51 benign, and 26 granulomatous-sarcoid. Among all contrast enhanced DECT protocol exams (routine, PE and CTA), univariable analysis demonstrated no significant differences in iodine and low kV features between neoplastic and non-neoplastic lymph nodes; in the subset of neoplastic versus benign lymph nodes with routine DECT protocol, 199 features differed (p = .01- < 0.05).Multivariable analysis using both iodine and low kV features yielded AUCs >0.8 for differentiating neoplastic from non-neoplastic lymph nodes (AUC 0.86), including subsets of neoplastic from granulomatous (AUC 0.86) and neoplastic from benign (AUC 0.9) lymph nodes, among all contrast protocols. ConclusionsVolumetric DECT radiomic features demonstrate strong collective performance in differentiation of neoplastic from non-neoplastic intrathoracic lymph nodes, and are influenced by contrast protocol.

Three-dimensional iodine mapping quantified by dual-energy CT for predicting programmed death-ligand 1 expression in invasive pulmonary adenocarcinoma

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.

Iodine Density Threshold to Distinguish Between Enhancing and Nonenhancing Renal Lesions With Dual-Layer Dual-Energy CT.

This study aimed to determine the optimal threshold iodine density to distinguish enhancing and nonenhancing renal masses with dual-layer dual-energy CT (dlDECT). In this retrospective, HIPAA-compliant, institutional review board-approved study, 383 consecutive renal mass dlDECT studies from September 5, 2018, through December 15, 2022, were reviewed for enhancing solid renal masses with ≥∆20 HU. Studies with simple cysts in the same interval served as controls. Lesion ROI HU measurements on unenhanced and nephrographic phases and ROI iodine density measurements of each lesion and the abdominal aorta for normalization were recorded. The mean lesion HU values and absolute and normalized iodine densities were compared with enhancing and nonenhancing renal lesions using a two-sample t test. The diagnostic accuracy of iodine thresholds was assessed by calculating sensitivity and specificity, with receiver operating characteristic curve and AUC analysis. There were 38 enhancing and 39 nonenhancing renal lesions. The mean (standard deviation [SD]) ∆HU was 73.5 (38.7) and 3.9 (5.1) HU for enhancing and nonenhancing lesions, respectively. The mean absolute iodine density was significantly different for enhancing and nonenhancing lesions (3.2 [1.7] mg/mL and 0.20 [0.22] mg/mL, respectively; P < 0.0001). Normalized mean iodine density was significantly different for enhancing and nonenhancing lesions (0.62 [0.33] mg/mL and 0.04 [0.04] mg/mL, respectively; P < 0.0001). The optimal absolute iodine density threshold of 0.70 mg/mL (AUC, 0.999) and normalized iodine density threshold of 0.11 mg/mL (AUC, 0.999) were 100% sensitive and 97.4% specific for discriminating enhancing and nonenhancing renal lesions. This study provides absolute and normalized iodine density thresholds to distinguish enhancing and nonenhancing renal lesions with high sensitivity and specificity using dlDECT.

Dual-energy computed tomography iodine quantification combined with laboratory data for predicting microvascular invasion in hepatocellular carcinoma: a two-centre study.

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. 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. 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. The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively. 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.

Dual-energy computed tomography for predicting cervical lymph node metastasis in laryngeal squamous cell carcinoma

Rationale and objectivesWe constructed a dual-energy computed tomography (DECT)-based model to assess cervical lymph node metastasis (LNM) in patients with laryngeal squamous cell carcinoma (LSCC). Materials and methodsWe retrospectively analysed 164 patients with LSCC who underwent preoperative DECT from May 2019 to May 2023. The patients were randomly divided into training (n = 115) and validation (n = 49) cohorts. Quantitative DECT parameters of the primary tumours and their clinical characteristics were collected. A logistic regression model was used to determine independent predictors of LNM, and a nomogram was constructed along with a corresponding online model. Model performance was assessed using the area under the curve (AUC) and the calibration curve, and the clinical value was evaluated using decision curve analysis (DCA). ResultsIn total, 64/164 (39.0 %) patients with LSCC had cervical LNM. Independent predictors of LNM included normalized iodine concentration in the arterial phase (odds ratio [OR]: 8.332, 95 % confidence interval [CI]: 2.813–24.678, P < 0.001), normalized effective atomic number in the arterial phase (OR: 5.518, 95 % CI: 1.095–27.818, P = 0.002), clinical T3-4 stage (OR: 5.684, 95 % CI: 1.701–18.989, P = 0.005), and poor histological grade (OR: 5.011, 95 % CI: 1.003–25.026, P = 0.049). These predictors were incorporated into the DECT-based nomogram and the corresponding online model, showing good calibration and favourable performance (training AUC: 0.910, validation AUC: 0.918). The DCA indicated a significant clinical benefit of the nomogram for estimating LNM. ConclusionsDECT parameters may be useful independent predictors of LNM in patients with LSCC, and a DECT-based nomogram may be helpful in clinical decision-making.

The potential value of dual-energy CT radiomics in evaluating CD8+, CD163+ and αSMA+ cells in the tumor microenvironment of clear cell renal cell carcinoma.

This study aims to develop radiomics models and a nomogram based on machine learning techniques, preoperative dual-energy computed tomography (DECT) images, clinical and pathological characteristics, to explore the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC). We retrospectively recruited of 87 patients diagnosed with ccRCC through pathological confirmation from Center I (training set, n = 69; validation set, n = 18), and collected their DECT images and clinical information. Feature selection was conducted using variance threshold, SelectKBest, and the least absolute shrinkage and selection operator (LASSO). Radiomics models were then established using 14 classifiers to predict TME cells. Subsequently, we selected the most predictive radiomics features to calculate the radiomics score (Radscore). A combined model was constructed through multivariate logistic regression analysis combining the Radscore and relevant clinical characteristics, and presented in the form of a nomogram. Additionally, 17 patients were recruited from Center II as an external validation cohort for the nomogram. The performance of the models was assessed using methods such as the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA). The validation set AUC values for the radiomics models assessing CD8+, CD163+, and αSMA+ cells were 0.875, 0.889, and 0.864, respectively. Additionally, the external validation cohort AUC value for the nomogram reaches 0.849 and shows good calibration. Radiomics models could allow for non-invasive assessment of TME cells from DECT images in ccRCC patients, promising to enhance our understanding and management of the tumor.

Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans

Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the utilization of virtual non-contrast CT (VNC) for the development of DL-SynCCT. Training and internal validations were performed with 2202 pairs of retrospectively collected contrast-enhanced CT (CECT) images with the corresponding VNC images acquired from dual-energy CT. Clinical validation was performed using an external validation set including 398 patients designated for true nonenhanced CT (NECT), from multiple vendors at three institutes. Detection of lesions was performed by three radiologists with only NECT in the first session and an additionally provided DL-SynCCT in the second session. The mean peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM) of the DL-SynCCT compared to CECT were 43.25 ± 0.41 and 0.92 ± 0.01, respectively. With DL-SynCCT, the pooled sensitivity for lesion detection (72.0% to 76.4%, P < 0.001) and level of diagnostic confidence (3.0 to 3.6, P < 0.001) significantly increased. In conclusion, DL-SynCCT generated by weakly supervised learning showed significant benefit in terms of sensitivity in detecting abnormal findings when added to NECT in patients designated for nonenhanced CT scans.

Dosimetric impact of stopping power for human bone porosity with dual-energy computed tomography in scanned carbon-ion therapy treatment planning

Few reports have documented how the accuracy of stopping power ratio (SPR) prediction for porous bone tissue affects the dose distribution of scanned carbon-ion beam therapy. The estimated SPR based on single-energy computed tomography (SECT) and dual-energy CT (DECT) were compared for the femur of a Rando phantom which simulates the porosity of human bone, NEOBONE which is the hydroxyapatite synthetic bone substitute, and soft tissue samples. Dose differences between SECT and DECT were evaluated for a scanned carbon-ion therapy treatment plan for the Rando phantom. The difference in the water equivalent length was measured to extract the SPR of the examined samples. The differences for SPR estimated from the DECT-SPR conversion were small with − 1.8% and − 3.3% for the Rando phantom femur and NEOBONE, respectively, whereas the differences for SECT-SPR were between 7.6 and 70.7%, illustrating a 1.5-mm shift of the range and a dose difference of 13.3% at maximum point in the evaluation of the dose distribution. This study demonstrated that the DECT-SPR conversion method better estimated the SPR of the porosity of bone tissues than SECT-SPR followed by the accurate range of the carbon-ion beams on carbon-ion dose calculations.

Comparison of preoperative diagnostic performance between dual-energy CT, conventional CT, and MRI in endometrial cancer.

To compare the diagnostic performance of virtual monoenergetic imaging (VMI), computed tomography (CT), and magnetic resonance imaging (MRI) in patients with endometrial cancer (EC). This retrospective study analysed 45 EC patients (mean age: 62 years, range: 44-84 years) undergoing contrast-enhanced CT with dual-energy CT (DECT) and MRI between September 2021 and October 2022. Dual-energy CT generated conventional CT (C-CT) and 40 keV VMI. Quantitative analysis compared contrast-to-noise ratio (CNR) of tumour to myometrium between C-CT and VMI. Qualitative assessment by 5 radiologists compared C-CT, VMI, and MRI for myometrial invasion (MI), cervical invasion, and lymph node metastasis. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated and compared for each diagnostic parameter. Virtual monoenergetic imaging showed significantly higher CNR than C-CT (p < 0.001) and a higher sensitivity for MI than C-CT (p = 0.027) and MRI (p = 0.011) but lower specificity than MRI (p = 0.018). C-CT had a higher sensitivity and AUC for cervical invasion than MRI (p = 0.018 and 0.004, respectively). The study found no significant superiority of MRI over CT across all diagnostic parameters. VMI demonstrated heightened sensitivity for MI, and C-CT showed greater sensitivity and AUC for cervical invasion than MRI. This suggests that combining VMI with C-CT holds promise as a comprehensive preoperative staging tool for EC when MRI cannot be performed.

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.

Robustness of radiomics among photon-counting detector CT and dual-energy CT systems: a texture phantom study.

To evaluate the robustness of radiomics features among photon-counting detector CT (PCD-CT) and dual-energy CT (DECT) systems. A texture phantom consisting of twenty-eight materials was scanned with one PCD-CT and four DECT systems (dual-source, rapid kV-switching, dual-layer, and sequential scanning) at three dose levels twice. Thirty sets of virtual monochromatic images at 70 keV were reconstructed. Regions of interest were delineated for each material with a rigid registration. Ninety-three radiomics were extracted per PyRadiomics. The test-retest repeatability between repeated scans was assessed by Bland-Altman analysis. The intra-system reproducibility between dose levels, and inter-system reproducibility within the same dose level, were evaluated by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-system variability among five scanners was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). The test-retest repeatability analysis presented that 97.1% of features were repeatable between scan-rescans. The mean ± standard deviation ICC and CCC were 0.945 ± 0.079 and 0.945 ± 0.079 for intra-system reproducibility, respectively, and 86.0% and 85.7% of features were with ICC > 0.90 and CCC > 0.90, respectively, between different dose levels. The mean ± standard deviation ICC and CCC were 0.157 ± 0.174 and 0.157 ± 0.174 for inter-system reproducibility, respectively, and none of the features were with ICC > 0.90 or CCC > 0.90 within the same dose level. The inter-system variability suggested that 6.5% and 12.8% of features were with CV < 10% and QCD < 10%, respectively, among five CT systems. The radiomics features were non-reproducible with significant variability in values among different CT techniques. Radiomics features are non-reproducible with significant variability in values among photon-counting detector CT and dual-energy CT systems, necessitating careful attention to improve the cross-system generalizability of radiomic features before implementation of radiomics analysis in clinical routine. CT radiomics stability should be guaranteed before the implementation in the clinical routine. Radiomics robustness was on a low level among photon-counting detectors and dual-energy CT techniques. Limited inter-system robustness of radiomic features may impact the generalizability of models.

Feasibility of dose calculation for treatment plans using electron density maps from a novel dual-layer detector spectral CT simulator

BackgroundConventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans.Methods30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software.ResultsIn terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%.ConclusionCompared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

Cardiopulmonary exercise testing excludes significant disease in patients recovering from COVID-19

ObjectivePost-COVID-19 syndrome presents a health and economic challenge affecting ~10% of patients recovering from COVID-19. Accurate assessment of patients with post-COVID-19 syndrome is complicated by health anxiety and coincident symptomatic...

Comparison of automated kidney stone size measurement and volumetry in photon counting CT compared to 3rd generation dual energy CT and physically measurements - an ex vivo study.

This ex vivo study aimed to compare a newly developed dual-source photon-counting CT (PCCT) with a 3rd generation dual-source dual-energy CT (DECT) for the detection and measurement (stone lengths and volumetrics) of urinary stones. 143 urinary stones with a known geometry were physically measured and defined as reference values. Next, urinary stones were placed in an anthropomorphic abdomen-model and were scanned with DECT and PCCT. Images were read by two experienced examiners and automatically evaluated using a specific software. DECT and PCCT showed a high sensitivity for manual stone detection of 97.9% and 94.4%, and for automatic detection of 93.0% and 87.4%, respectively. Compared to that uric acid and xanthine stones were recognized slightly worse by DECT and PCCT with manual stone detection (93.3% and 82.2%), and with automatic detection (77.8% and 60.0%). All other stone entities were completely recognized. By comparing the maximum diameter of the reference value and DECT, Pearson-correlation was 0.96 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurement, and for PCCT it was 0.94 (p < 0.001) for manual and 0.97 (p < 0.001) for automatic measurements. DECT and PCCT can also reliably determine volume manually and automatically with a Pearson-correlation of 0.99 (p < 0.001), respectively. Both CTs showed comparable results in stone detection, length measurement and volumetry compared to the reference values. Automatic measurement tends to underestimate the maximum diameter. DECT proved to be slightly superior in the recognition of xanthine and uric acid stones.

Analysis of experience in the clinical use of dual-energy computed tomography in patients with undifferentiated arthritis

Dual-energy computed tomography (DECT) is a radiation diagnostic method that is used in rheumatology to verify microcrystalline arthritis, but in Russia this method is little known and the experience of its use is rather scarce.Objective of the research. To analyze the experience of using DECT in patients with undifferentiated arthritis and lesions of the axial skeleton in clinical practice.Material and Methods. The retrospective study included 20 patients (14 men and 6 women) observed at the V. A. Nasonova Research Institute of Rheumatology, who underwent DECT for diagnostic purposes. 13 patients with undifferentiated arthritis underwent DECT of peripheral joints to diagnose gout; 7 patients with an established diagnosis of gout (based on the 2015 ACR/EULAR classification criteria for gout) – to clarify the genesis of lesions to the axial joints and spine. The study was performed on a Siemens SOMATOM Definition Flash computed tomograph.Results. Using DECT in patients with undifferentiated arthritis, deposits of monosodium urate crystals (MSUc) were detected in 7 out of 13 (54 %), which made it possible to verify the diagnosis of gout. In the second sample, 6 out of 7 patients (85 %) showed signs of MSUc deposition on DECT, which explained the genesis of the existing complaints. Among these 6 patients, in 1 person, DECT revealed the presence of MSUc and calcium pyrophosphate crystals in the shoulder joint, which made it possible to make two diagnoses at once – gout and calcium pyrophosphate deposition disease.Conclusion. DECT in patients with undifferentiated arthritis makes it possible to verify the diagnosis of gout in more than half of the cases (54 %). In 85 % of patients with gout and pain in the back and axial joints of unknown origin, urate deposits are detected according to DECT. This method may be a useful tool for identifying microcrystalline arthritis of the axial joints, but further research is needed to implement the method in routine practice.

Deep learning image reconstruction for low-kiloelectron volt virtual monoenergetic images in abdominal dual-energy CT: medium strength provides higher lesion conspicuity.

The best settings of deep learning image reconstruction (DLIR) algorithm for abdominal low-kiloelectron volt (keV) virtual monoenergetic imaging (VMI) have not been determined. To determine the optimal settings of the DLIR algorithm for abdominal low-keV VMI. The portal-venous phase computed tomography (CT) scans of 109 participants with 152 lesions were reconstructed into four image series: VMI at 50 keV using adaptive statistical iterative reconstruction (Asir-V) at 50% blending (AV-50); and VMI at 40 keV using AV-50 and DLIR at medium (DLIR-M) and high strength (DLIR-H). The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of nine anatomical sites were calculated. Noise power spectrum (NPS) using homogenous region of liver, and edge rise slope (ERS) at five edges were measured. Five radiologists rated image quality and diagnostic acceptability, and evaluated the lesion conspicuity. The SNR and CNR values, and noise and noise peak in NPS measurements, were significantly lower in DLIR images than AV-50 images in all anatomical sites (all P < 0.001). The ERS values were significantly higher in 40-keV images than 50-keV images at all edges (all P < 0.001). The differences of the peak and average spatial frequency among the four reconstruction algorithms were significant but relatively small. The 40-keV images were rated higher with DLIR-M than DLIR-H for diagnostic acceptance (P < 0.001) and lesion conspicuity (P = 0.010). DLIR provides lower noise, higher sharpness, and more natural texture to allow 40 keV to be a new standard for routine VMI reconstruction for the abdomen and DLIR-M gains higher diagnostic acceptance and lesion conspicuity rating than DLIR-H.