Dual-energy CT Research Articles

Extracellular volume fraction derived from dual-energy CT: a potential predictor for acute pancreatitis after pancreatoduodenectomy.

To investigate the value of extracellular volume (ECV) fraction and fat fraction (FF) derived from dual- energy CT (DECT) for predicting postpancreatectomy acute pancreatitis (PPAP) after pancreatoduodenectomy (PD). This retrospective study included patients who underwent DECT and PD between April 2022 and September 2022. PPAP was determined according to the International Study Group for Pancreatic Surgery (ISGPS) definition. Iodine concentration (IC) and FF of the pancreatic parenchyma were measured on preoperative DECT. The ECV fraction was calculated from iodine map images of the equilibrium phase. The independent predictors for PPAP were assessed by univariate and multivariable logistic regression analysisand receiver operating characteristic (ROC) curve analysis. Sixty-nine patients were retrospectively enrolled (median age, 60 years; interquartile range, 55-70 years; 47 men). Of these, nine patients (13.0%) developed PPAP. These patients had lower portal venous phase IC, equilibrium phase IC, FF, and ECV fraction, and higher pancreatic parenchymal-to-portal venous phase IC ratio and pancreatic parenchymal-to-equilibrium phase IC ratio, compared with patients without PPAP. After multivariable analysis, ECV fraction was independently associated with PPAP (odd ratio [OR], 0.87; 95% confidence interval [CI]: 0.79, 0.96; p < 0.001), with an area under the curve (AUC) of 0.839 (sensitivity 100.0%, specificity 58.3%). A lower ECV fraction is independently associated with the occurrence of PPAP after PD. ECV fraction may serve as a potential predictor for PPAP after PD. DECT-derived ECV fraction of pancreatic parenchyma is a promising biomarker for surgeons to preoperatively identify patients with higher risk for postpancreatectomy acute pancreatitis after PD and offer selective perioperative management. PPAP is a complication of pancreatic surgery, early identification of higher-risk patients allows for risk mitigation. Lower DECT-derived ECV fraction was independently associated with the occurrence of PPAP after PD. DECT aids in preoperative PAPP risk stratification, allowing for appropriate treatment to minimize complications.

Electron density dual-energy CT can improve the detection of lumbar disc herniation with higher image quality than standard and virtual non-calcium images.

To compare the diagnostic performance and image quality of dual-energy computed tomography (DECT) with electron density (ED) image reconstruction with those of DECT with standard CT (SC) and virtual non-calcium (VNCa) image reconstructions, for diagnosing lumbar disc herniation (L-HIVD). A total of 59 patients (354 intervertebral discs from T12/L1 to L5/S1; mean age, 60 years; 30 women and 29 men) who underwent DECT with spectral reconstruction and 3-T MRI within 2 weeks were enrolled between March 2021 and February 2022. Four radiologists independently assessed three image sets of randomized ED, SC, and VNCa images to detect L-HIVD at 8-week intervals. The coefficient of variance (CV) and the Weber contrast of the ROIs in the normal and diseased disc to cerebrospinal fluid space (NCR-normal/-diseased, respectively) were calculated to compare the image qualities of the noiseless ED and other series. Overall, 129 L-HIVDs were noted on MRI. In the detection of L-HIVD, ED showed a higher AUC and sensitivity than SC and VNCa; 0.871 vs 0.807 vs 833 (p = 0.002) and 81% vs 70% vs 74% (p = 0.006 for SC), respectively. CV was much lower in all measurements of ED than those for SC and VNCa (p < 0.001). Furthermore, NCR-normal and NCR-diseased were the highest in ED (ED vs SC in NCR-normal and NCR-diseased, p = 0.001 and p = 0.004, respectively; ED vs VNCa in NCR-diseased, p = 0.044). Compared to SC and VNCa images, DECT with ED reconstruction can enhance the AUC and sensitivity of L-HIVD detection with a lower CV and higher NCR. To our knowledge, this is the first study to quantify the image quality of noiseless ED images. ED imaging may be helpful for detecting L-HIVD in patients who cannot undergo MRI. ED images have diagnostic potential, but relevant quantitative analyses of image quality are limited. ED images detect disc herniation, with a better coefficient of variance and normalized contrast ratio values. ED images could detect L-HIVD when MRI is not an option.

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

ObjectivesThe simplified endoscopic score of Crohn’s disease (SES-CD) is the gold standard for quantitatively evaluating Crohn’s disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity.MethodsWe evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves.ResultsThere were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81–0.87 (DeLong test p value was 0.071–0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779–0.959).ConclusionsThe ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients.Critical relevance statementThe machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn’s disease activity noninvasively and quantitatively.Key PointsDual-energy CT parameters are related to Crohn’s disease activity.Three machine learning models effectively evaluated Crohn’s disease activity.Combined models based on conventional and dual-energy CT have the best performance.Graphical

Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers

ObjectiveTo evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes - serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. Material and MethodsThis prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). ResultsIn total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared with the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P＜0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P ＜0.05). ConclusionDECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.

Dosimetric characteristics of 3D-printed and epoxy-based materials for particle therapy phantoms

Objective3D printing has seen use in many fields of imaging and radiation oncology, but applications in (anthropomorphic) phantoms, especially for particle therapy, are still lacking. The aim of this work was to characterize various available 3D printing methods and epoxy-based materials with the specific goal of identifying suitable tissue surrogates for dosimetry applications in particle therapy.Methods3D-printed and epoxy-based mixtures of varying ratios combining epoxy resin, bone meal, and polyethylene powder were scanned in a single-energy computed tomography (CT), a dual-energy CT, and a µCT scanner. Their CT-predicted attenuation was compared to measurements in a 148.2 MeV proton and 284.7 MeV/u carbon ion beam. The sample homogeneity was evaluated in the respective CT images and in the carbon beam, additionally via widening of the Bragg peak. To assess long-term stability attenuation, size and weight measurements were repeated after 6–12 months.ResultsFour 3D-printed materials, acrylonitrile butadiene styrene polylactic acid, fused deposition modeling printed nylon, and selective laser sintering printed nylon, and various ratios of epoxy-based mixtures were found to be suitable tissue surrogates. The materials’ predicted stopping power ratio matched the measured stopping power ratio within 3% for all investigated CT machines and protocols, except for µCT scans employing cone beam CT technology. The heterogeneity of the suitable surrogate samples was adequate, with a maximum Bragg peak width increase of 11.5 ± 2.5%. The repeat measurements showed no signs of degradation after 6–12 months.ConclusionWe identified surrogates for soft tissue and low- to medium-density bone among the investigated materials. This allows low-cost, adaptable phantoms to be built for quality assurance and end-to-end tests for particle therapy.

Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.

Dual-energy CT in acute cholecystitis- features predicting culture-positive bile and outcome

PurposeLow mono-energetic CT has been shown to improve visualization of acute abdominal inflammatory processes. We aimed to determine its utility in patients with acute cholecystitis and potential added value in clinical decision making. MethodsSixty-seven consecutive patients with radiological signs of cholecystitis on contrast-enhanced dual-layer CT imaging were retrospectively identified over a four-year period (2/17–8/21). A ranked Likert scale was created for imaging findings present in acute cholecystitis, including gallbladder mucosal integrity and enhancement and pericholecystic liver parenchymal enhancement. These rankings were correlated with laboratory data, followed by sensitivity, specificity, and odds-ratios calculations. ResultsMucosal integrity and pericholecystic liver enhancement were better seen on low-energetic images by unanimous consensus. Presence of pericholecystic liver enhancement and poorer mucosal wall integrity correlated with positive bile cultures (sensitivity: 93.8 % and 96.9 %, specificity: 37.5 and 50.0 %; odds-ratio: 9.0[1.1–68.1 95 %CI] and 31.0 [2.7–350.7 95 %CI], p = 0.017 and p ≤ 0.001) in patients undergoing cholecystostomy (n = 40/67). Moreover, binary regression modeling showed that the strongest predictor variable for bile culture positivity was the score for pericholecystic liver enhancement (Exp(B) = 0.6, P = 0.022). By contrast, other laboratory markers and other imaging findings (such as GB wall thickness) showed lower sensitivities (76–82 %), specificities (16–21 %) and odds ratios (0.2–4.4) for the prediction of infected bile. ConclusionsPericholecystic liver enhancement and gallbladder wall integrity are better visualized on low-DECT images. These findings also potentially predict bile culture positivity in patients with cholecystitis, which may influence clinical management including the need for intervention.

Evaluation of Radiation Dose Effect on Lung Function Using Iodine Maps Derived From Dual-Energy Computed Tomography

PurposeThere is interest in using dual-energy computed tomography (DECT) to evaluate organ function before and after radiotherapy. The purpose of this study (trial identifier: XXXX) is to assess longitudinal changes in lung perfusion using iodine maps derived from DECT in lung cancer patients treated with conventional or stereotactic radiotherapy (RT). MethodsFor 48 prospectively enrolled lung cancer patients, a contrast-enhanced DECT using a dual-source CT simulator was acquired pre-treatment and at 6 and 12 months post-treatment. Pulmonary functions tests (PFT) were obtained at baseline and at 6 and 12 months post-treatment. Iodine maps were extracted from the DECT images using a previously described 2-material decomposition framework. Longitudinal iodine maps were normalized using a reference region defined as all voxels with perfusion in the top 10% outside of the 5 Gy isodose volume. Normalized functional responses (NFR) were calculated for three dose ranges: <5 Gy, 5-20 Gy and >20 Gy. Mixed model analysis was used to assess the correlation between dose metrics and NFR. Pearson correlation was used to assess if NFR are correlated with PFT changes. ResultsOut of the 48 patients, 21 (44%) were treated with stereotactic body radiation therapy (SBRT) and 27 (56%) were treated with conventionally fractionated IMRT. 31 out of these 48 patients were ultimately included in data analysis. It was found that NFR is linearly correlated with dose (p < 0.001) for both groups. The number of months elapsed post-RT is also found to correlate with NFR (p = 0.029), although this correlation was not observed for the SBRT subgroup. The NFR is not found to correlate with PFT changes. ConclusionDECT derived iodine maps are a promising method for detailed anatomic evaluation of radiation effect on lung function, including potentially subclinical changes.

Variation in Hounsfield unit calculated using dual-energy computed tomography: comparison of dual-layer, dual-source, and fast kilovoltage switching technique.

The purpose of the study is to investigate the variation in Hounsfield unit (HU) values calculated using dual-energy computed tomography (DECT) scanners. A tissue characterization phantom inserting 16 reference materials were scanned three times using DECT scanners [dual-layer CT (DLCT), dual-source CT (DSCT), and fast kilovoltage switching CT (FKSCT)] changing scanning conditions. The single-energy CT images (120 or 140 kVp), and virtual monochromatic images at 70keV (VMI70) and 140keV (VMI140) were reconstructed, and the HU values of each reference material were measured. The difference in HU values was larger when the phantom was scanned using the half dose with wrapping with rubber (strong beam-hardening effect) compared with the full dose without the rubber (reference condition), and the difference was larger as the electron density increased. For SECT, the difference in HU values against the reference condition measured by the DSCT (3.2 ± 5.0 HU) was significantly smaller (p < 0.05) than that using DLCT with 120 kVp (22.4 ± 23.8 HU), DLCT with 140 kVp (11.4 ± 12.8 HU), and FKSCT (13.4 ± 14.3 HU). The respective difference in HU values in the VMI70 and VMI140 measured using the DSCT (10.8 ± 17.1 and 3.5 ± 4.1 HU) and FKSCT (11.5 ± 21.8 and 5.5 ± 10.4 HU) were significantly smaller than those measured using the DLCT120 (23.1 ± 27.5 and 12.4 ± 9.4 HU) and DLCT140 (22.3 ± 28.6 and 13.1 ± 11.4 HU). The HU values and the susceptibility to beam-hardening effects varied widely depending on the DECT scanners.

Value of third-generation of VNCa dual-energy CT for differentiating diffuse marrow infiltration of multiple myeloma from red bone marrow.

This study aims to investigate the ability of bone marrow imaging using third-generation dual-energy computed tomography (CT) virtual noncalcium (VNCa) to differentiate between multiple myeloma (MM) with diffuse bone marrow infiltration and red bone marrow (RBM). Bone marrow aspiration or follow-up results were used as reference. We retrospectively reviewed 188 regions of interests (ROIs) from 21 patients with confirmed MM and diffuse bone marrow infiltrations who underwent VNCa bone marrow imaging between May 2019 and September 2022. At the same time, we obtained 98 ROIs from 11 subjects with RBM for comparative study, and 189 ROIs from 20 subjects with normal yellow bone marrow for the control group. The ROIs were delineated by 2 radiologists independently, the interobservers reproducibility was evaluated by interclass correlation coefficients. The correlation with MRI grade results was analyzed by Spearman correlation coefficient. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal threshold for differentiating between these groups and to assess diagnostic performance. There were statistically significant differences in VNCa CT values of bone marrow among the MM, RBM, and control groups (all P < .001), with values decreasing sequentially. A strong positive rank correlation was observed between normal bone marrow, subgroup MM with moderately and severe bone marrow infiltration divided by MRI and their corresponding CT values (ρ = 0.897, 95%CI: 0.822 to 0.942, P < .001). When the CT value of VNCa bone marrow was 7.15 HU, the area under the curve (AUC) value for differentiating RBM and MM was 0.723, with a sensitivity of 50.5% and a specificity of 89.8%. When distinguishing severe bone marrow infiltration of MM from RBM, the AUC value was 0.80 with a sensitivity 70.9% and a specificity 78.9%. The AUC values for MM, RBM, and the combined group compared to the control group were all >0.99, with all diagnostic sensitivity and specificity exceeding 95%. VNCa bone marrow imaging using third-generation dual-energy CT accurately differentiates MM lesions from normal bone marrow or RBM. It demonstrates superior diagnostic performance in distinguishing RBM from MM with diffuse bone marrow infiltration.

Pancreatic Iodine Density and Fat Fraction on Dual-Energy Computed Tomography in Acute Pancreatitis.

The aim of our study was to investigate iodine density (ID) and fat fraction (FF) on dual-energy computed tomography (DECT) in patients with acute pancreatitis (AP). This retrospective study included 72 patients with clinically confirmed AP and 62 control subjects with DECT of the abdomen. Two radiologists assessed necrosis and measured attenuation values, ID, and FF in three pancreatic segments. We used receiver operating characteristic (ROC) analysis to determine the optimal threshold for ID for the differentiation between AP groups. The ID was significantly higher in interstitial edematous AP compared to necrotizing AP and the control group (both p < 0.05). The ROC curve analysis revealed the thresholds of ID for detecting pancreatic necrosis ≤ 2.2, ≤2.3, and ≤2.4 mg/mL (AUC between 0.880 and 0.893, p > 0.05) for the head, body, and tail, respectively. The FF was significantly higher for pancreatitis groups when compared with the control group in the head and body segments (both p < 0.001). In the tail, the difference was significant in necrotizing AP (p = 0.028). The ID values were independent of attenuation values correlated with the FF values in pancreatic tissue. Iodine density values allow for differentiation between morphologic types of AP.

1583: Phantom-based optimization of virtual monoenergetic image reconstruction in dual-energy CT

1583: Phantom-based optimization of virtual monoenergetic image reconstruction in dual-energy CT

Predicting Osteoporosis and Osteopenia by Fusing Deep Transfer Learning Features and Classical Radiomics Features Based on Single-Source Dual-energy CT Imaging

Rationale and ObjectivesTo develop and validate a predictive model for osteoporosis and osteopenia prediction by fusing deep transfer learning (DTL) features and classical radiomics features based on single-source dual-energy computed tomography (CT) virtual monochromatic imaging. MethodsA total of 606 lumbar vertebrae with dual-energy CT imaging and quantitative CT (QCT) evaluation were included in the retrospective study and randomly divided into the training (n=424) and validation (n=182) cohorts. Radiomics features and DTL features were extracted from 70-keV monochromatic CT images, followed by feature selection and model construction, radiomics and DTL features models were established. Then, we integrated the selected two types of features into a features fusion model. We developed a two-level classifier for the hierarchical pairwise classification of each vertebra. All the vertebrae were first classified into osteoporosis and non-osteoporosis groups, then non-osteoporosis group was classified into osteopenia and normal groups. QCT was used as reference. The predictive performance and clinical usefulness of three models were evaluated and compared. ResultsThe area under the curve (AUC) of the features fusion, radiomics and DTL models for the classification between osteoporosis and non-osteoporosis were 0.981, 0.999, 0.997 in the training cohort and 0.979, 0.943, 0.848 in the validation cohort. Furthermore, the AUCs of the abovementioned models for the differentiation between osteopenia and normal were 0.994, 0.971, 0.996 in the training cohort and 0.990, 0.968, 0.908 in the validation cohort. The overall accuracy of the abovementioned models for two-level classifications was 0.979, 0.955, 0.908 in the training cohort and 0.918, 0.885, 0.841 in the validation cohort. Decision curve analysis (DCA) showed that all models had high clinical value. ConclusionThe feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

853: Evaluation ability of dual-Energy CT for the extent of osteolytic spinal metastases infiltration

853: Evaluation ability of dual-Energy CT for the extent of osteolytic spinal metastases infiltration

1551: Tumor hypoxia assessment using dual-energy CT derived iodine concentration in head and neck cancer

1551: Tumor hypoxia assessment using dual-energy CT derived iodine concentration in head and neck cancer

Urinary lithiasis: Can dual-energy CT rival morphological and infrared analysis in determining composition?

Urinary lithiasis: Can dual-energy CT rival morphological and infrared analysis in determining composition?

Image Domain Multi-Material Decomposition Noise Suppression Through Basis Transformation and Selective Filtering.

Spectral CT can provide material characterization ability to offer more precise material information for diagnosis purposes. However, the material decomposition process generally leads to amplification of noise which significantly limits the utility of the material basis images. To mitigate such problem, an image domain noise suppression method was proposed in this work. The method performs basis transformation of the material basis images based on a singular value decomposition. The noise variances of the original spectral CT images were incorporated in the matrix to be decomposed to ensure that the transformed basis images are statistically uncorrelated. Due to the difference in noise amplitudes in the transformed basis images, a selective filtering method was proposed with the low-noise transformed basis image as guidance. The method was evaluated using both numerical simulation and real clinical dual-energy CT data. Results demonstrated that compared with existing methods, the proposed method performs better in preserving the spatial resolution and the soft tissue contrast while suppressing the image noise. The proposed method is also computationally efficient and can realize real-time noise suppression for clinical spectral CT images.

Characterization of tumors of jaw: Additive value of contrast enhancement and dual-energy computed tomography.

Currently, the differentiation of jaw tumors is mainly based on the lesion's morphology rather than the enhancement characteristics, which are important in the differentiation of neoplasms across the body. There is a paucity of literature on the enhancement characteristics of jaw tumors. This is mainly because, even though computed tomography (CT) is used to evaluate these lesions, they are often imaged without intravenous contrast. This study hypothesised that the enhancement characteristics of the solid component of jaw tumors can aid in the differentiation of these lesions in addition to their morphology by dual-energy CT, therefore improving the ability to differentiate between various pathologies. To evaluate the role of contrast enhancement and dual-energy quantitative parameters in CT in the differentiation of jaw tumors. Fifty-seven patients with jaw tumors underwent contrast-enhanced dual-energy CT. Morphological analysis of the tumor, including the enhancing solid component, was done, followed by quantitative analysis of iodine concentration (IC), water concentration (WC), HU, and normalized IC. The study population was divided into four subgroups based on histopathological analysis-central giant cell granuloma (CGCG), ameloblastoma, odontogenic keratocyst (OKC), and other jaw tumors. A one-way ANOVA test for parametric variables and the Kruskal-Wallis test for non-parametric variables were used. If significant differences were found, a series of independent t-tests or Mann-Whitney U tests were used. Ameloblastoma was the most common pathology (n = 20), followed by CGCG (n = 11) and OKC. CGCG showed a higher mean concentration of all quantitative parameters than ameloblastomas (P < 0.05). An IC threshold of 31.35 × 100 μg/cm3 had the maximum sensitivity (81.8%) and specificity (65%). Between ameloblastomas and OKC, the former showed a higher mean concentration of all quantitative parameters (P < 0.001), however when comparing unilocular ameloblastomas with OKCs, the latter showed significantly higher WC. Also, ameloblastoma had a higher IC and lower WC compared to "other jaw tumors" group. Enhancement characteristics of solid components combined with dual-energy parameters offer a more precise way to differentiate between jaw tumors.

Vertebral and Femoral Bone Mineral Density (BMD) Assessment with Dual-Energy CT versus DXA Scan in Postmenopausal Females.

This study aimed to demonstrate the potential role of dual-energy CT in assessing bone mineral density (BMD) using hydroxyapatite-fat material pairing in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using a HAP-fat material pair. Statistical analysis was performed using the Bland-Altman method to assess the agreement between DXA and DECT HAP-fat measurements. Mean BMD, vertebral, and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP-fat values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) was found for measurements at the femoral neck. In conclusion, we demonstrate the enhanced importance of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

Evaluation of the Pulmonary Arteries on CTPA With Dual Energy CT: Objective Analysis and Subjective Preferences in a Multireader Study.

To perform qualitative and quantitative evaluation of low-monoenergetic images (50KeV) compared with conventional images (120kVp) in pulmonary embolism (PE) studies and to determine the extent and clinical relevance of these differences as well as radiologists' preferences. One hundred fifty CT examinations for PE detection conducted on a single-source dual-energy CT were retrospectively evaluated. Attenuation, contrast-to-noise-ratio, and signal-to-noise-ratio were obtained in a total of 8 individual pulmonary arteries on each exam-including both central (450/1200=37.5%) and peripheral (750/1200=62.5%) locations. Results were compared between the conventional and low-monoenergetic images. For quality assessment, 41 images containing PE were presented side-by-side as pairs of slices in both conventional and monoenergetic modes and evaluated for ease in embolus detection by 9 radiologists: cardiothoracic specialists (3), noncardiothoracic specialists (3), and residents (3). Paired samples t tests, a-parametric Wilcoxon test, McNemar test, and kappa statistics were performed. Monoenergetic images had an overall statistically significant increased average ratio of 2.09 to 2.26 ( P <0.05) for each measured vessel attenuation, with an increase in signal-to-noise ratio (23.82±9.29 vs. 11.39±3.2) and contrast-to-noise ratio (17.17±6.7 vs 7.27±2.52) ( P <0.05). Moreover, 10/150 (6%) of central pulmonary artery measurements considered suboptimal on conventional mode were considered diagnostic on the monoenergetic images (181±14.6 vs. 387.7±72.4 HU respectively, P <0.05). In the subjective evaluation, noncardiothoracic radiologists showed a preference towards low-monoenergetic images, whereas cardiothoracic radiologists did not (74.4% vs. 57.7%, respectively, P <0.05). The SNR and CNR increase on monoenergetic images may have clinical significance particularly in the setting of sub-optimal PE studies. Noncardiothoracic radiologists and residents prefer low monoenergetic images.