Dual-energy (spectral) CT: applications in abdominal imaging

Epstein-Barr virus positive gastric cancer: the pathological basis of CT findings and radiomics models prediction

PURPOSE

To analyze the clinicopathologic information and CT imaging features of Epstein-Barr virus (EBV)-positive gastric cancer (GC) and establish CT-based radiomics models to predict the EBV status of GC.

METHODS

This retrospective study included 144 GC cases, including 48 EBV-positive cases. Pathological and immunohistochemical information was collected. CT enlarged LN and morphological characteristics were also assessed. Radiomics models were constructed to predict the EBV status, including decision tree (DT), logistic regression (LR), random forest (RF), and support vector machine (SVM).

RESULTS

T stage, Lauren classification, histological differentiation, nerve invasion, VEGFR2, E-cadherin, PD-L1, and Ki67 differed significantly between the EBV-positive and -negative groups (p = 0.015, 0.030, 0.006, 0.022, 0.028, 0.030, < 0.001, and < 0.001, respectively). CT enlarged LN and large ulceration differed significantly between the two groups (p = 0.019 and 0.043, respectively). The number of patients in the training and validation cohorts was 100 (with 33 EBV-positive cases) and 44 (with 15 EBV-positive cases). In the training cohort, the radiomics models using DT, LR, RF, and SVM yielded areas under the curve (AUCs) of 0.905, 0.771, 0.836, and 0.886, respectively. In the validation cohort, the diagnostic efficacy of radiomics models using the four classifiers were 0.737, 0.722, 0.751, and 0.713, respectively.

CONCLUSION

A significantly higher proportion of CT enlarged LN and a significantly lower proportion of large ulceration were found in EBV-positive GC. The prediction efficiency of radiomics models with different classifiers to predict EBV status in GC was good.

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

RATIONALE AND OBJECTIVES

To 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.

METHODS

A 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.

RESULTS

The 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 previously mentioned 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 previously mentioned 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 showed that all models had high clinical value.

CONCLUSION

The feature fusion model can be used for osteoporosis and osteopenia prediction with improved predictive ability over a radiomics model or a DTL model alone.

Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Virtual non-contrast images of detector-based spectral computed tomography in dogs: a promising alternative to true non-contrast images in veterinary medicine

Introduction

In veterinary medicine, abdominal computer tomographic (CT) examinations regularly require a minimum of two scans, with a native scan (true unenhanced, TUE) as a reference for the subsequent contrast-enhanced CT scan (CECT). Spectral detector CT (SDCT) offers the possibility to calculate virtual non-contrast (VNC) images from the post-contrast scan, but this has not yet been investigated in veterinary medicine. The purpose of this study was to assess the reliability of VNC images for abdominal organs in 44 dogs without abdominal pathologies by evaluating their quantitative and qualitative parameters compared to TUE images. We hypothesized that the subtraction of iodine is sufficient in the VNC series compared to the TUE series and that the image quality of the SDCT series is superior to conventional CT images.

Methods

Corresponding attenuation values in the VNC and TUE series regarding the regions of interest (ROI) in different parenchymal organs and major vessels of the abdominal cavity were assessed by means of a two one-sided t-test (TOST) and Bland-Altman plots. Additionally, the signal-to-noise ratio (SNR) was calculated for each ROI in the different series. In the second step, two board-certified veterinary radiologists made a qualitative assessment of VNC images vs. TUE images in consensus by rating the iodine subtraction, image noise, and image quality of VNC images based on a specific 5-point Likert scale.

Results

The difference in corresponding Hounsfield units (HUs) between TUE and VNC images was less than 10 HU in 78.67% of all ROIs. Regarding the limit of less than 10 HU, in the performed TOST, significant p-values of < 0.05 were reached for the liver, spleen, pancreas, and musculature, implying equivalence of both modalities. The quality of spectral base image (SBI) data was rated equivalent to calculated conventional images in the subjective assessment by reaching an average Likert scale score of 3.2 points.

Discussion

VNC images calculated from SDCT data prove a valid alternative to conventional TUE images in the abdominal organs of canine patients without abdominal pathology. VNC offers the possibility to reduce time under general anesthesia and minimize radiation exposure. Future studies are needed to prove the application of this method in clinically diseased patients.

Dual Energy CT in Oncology: Benefits for Both Patients and Radiologists From an Emerging Quantitative and Functional Diagnostic Technique

Dual-energy CT (DECT) imaging makes it possible to identify the characteristics of materials that cannot be recognized with conventional single-energy CT (SECT). In the postprocessing study phase, virtual monochromatic images and virtual-non-contrast (VNC) images, also permits reduction of dose exposure by eliminating the precontrast acquisition scan. Moreover, in virtual monochromatic images, the iodine contrast increases when the energy level decreases resulting in better visualization of hypervascular lesions and in a better tissue contrast between hypovascular lesions and the surrounding parenchyma; thus, allowing for reduction of required iodinate contrast material, especially important in patients with renal impairment. All these advantages are particularly important in oncology, providing the possibility of overcoming many SECT imaging limits and making CT examinations safer and more feasible in critical patients. This review explores the basis of DECT imaging and its utility in routine oncologic clinical practice, with particular attention to the benefits of this technique for both the patients and the radiologists.

A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images

OBJECTIVE

Advancements in computed tomography (CT) reconstruction have enabled image quality improvements and dose reductions. Previous advancements have included iterative and model-based reconstruction. The latest image reconstruction advancement uses deep learning, which has been evaluated for polychromatic imaging only. This article characterizes a commercially available deep learning imaging reconstruction applied to dual-energy CT.

METHODS

Monochromatic, iodine basis, and water basis images were reconstructed with filtered back projection (FBP), iterative (ASiR-V), and deep learning (DLIR) methods in a phantom experiment. Slice thickness, contrast-to-noise ratio, modulation transfer function, and noise power spectrum metrics were used to characterize ASiR-V and DLIR relative to FBP over a range of dose levels, phantom sizes, and iodine concentrations.

RESULTS

Slice thicknesses for ASiR-V and DLIR demonstrated no statistically significant difference relative to FBP for all measurement conditions. Contrast-to-noise ratio performance for DLIR-high and ASiR-V 40% at 2 mg I/mL on 40-keV images were 162% and 30% higher than FBP, respectively. Task-based modulation transfer function measurements demonstrated no clinically significant change between FBP and ASiR-V and DLIR on monochromatic or iodine basis images.

CONCLUSIONS

Deep learning image reconstruction enabled better image quality at lower monochromatic energies and on iodine basis images where image contrast is maximized relative to polychromatic or high-energy monochromatic images. Deep learning image reconstruction did not demonstrate thicker slices, decreased spatial resolution, or poor noise texture (ie, "plastic") relative to FBP.

Development and external validation of a non-invasive imaging biomarker to estimate the microsatellite instability status of gastric cancer and its prognostic value: The combination of clinical and quantitative CT-imaging features

PURPOSE

Molecular testing for microsatellite instability (MSI) status plays a vital role in the clinical management of gastric cancer (GC). Nevertheless, challenges of routinely applied technology for MSI determination exist. This study aimed to develop and validate a non-invasive imaging biomarker for MSI assessment in GC and explore its prognostic value.

METHODS

We retrospectively recruited 396 GC patients with pretreatment CT images from a single center and a public database and divided them into an original cohort (n = 356) and an external validation cohort (n = 40). The SMOTE algorithm was used to generate a balanced training cohort (n = 192) and the independent radiomics model, clinical model, and radiomics-clinic combined model were constructed for determining MSI status. The models' discrimination, calibration, clinical usefulness, and prognosis significance were evaluated by AUC, calibration, decision curve analyses, and Kaplan-Meier curve analysis, respectively.

RESULTS

The radiomics-clinic combined model derived from clinical and quantitative CT-based "Radscore" exhibited the best discriminatory abilities of MSI status in all cohorts, with AUCs of 0.836 (95% CI, 0.780-0.893) in the training cohort, 0.834 (95% CI, 0.688-0.981) in the external validation cohort, and 0.750 (95% CI, 0.682-0.819) in the original cohort, respectively. Meanwhile, the combined model demonstrated goodness of fitness, higher clinical net benefits, and significant positive integrated discrimination improvement compared with any independent model. While it showed no significant overall survival- or progression-free survival-based risk stratification ability (p > 0.05).

CONCLUSIONS

The radiomics-clinic combined model could be a potential non-invasive biomarker for MSI status in GC, which help clinical decision-making, nevertheless, provided limited prognostic ability.

What's new in the hot gallbladder: the evolving radiologic diagnosis and management of acute cholecystitis

Acute cholecystitis (AC) is a common condition and its incidence is rising. New technologies have advanced the imaging diagnosis of AC, providing more structural and functional information as well as allowing the radiologist to distinguish AC from mimics and identify complications from both the disease and its management. Dual energy CT aids in detecting gallstones and gallbladder wall enhancement, which helps to diagnose AC and identify its complications. Similarly, contrast-enhanced and non-contrast perfusion ultrasound techniques improve detection of abnormal gallbladder wall enhancement. Advances in MR imaging including hepatobiliary contrast agents aid in characterizing post-cholecystectomy complications such as bile leaks. Newer interventional techniques have also expanded the suite of options for minimally invasive management. Lumen apposing metal stents provide more options for conservative treatment in non-surgical candidates and are compared to a standard percutaneous cholecystostomy. Radiologists should be familiar with these advanced imaging methods and intervention techniques and the value they can bring to the diagnosis and management of AC.

Dual-energy computed tomography of the abdomen: A reliable trouble-shooter

Dual-energy computed tomography (CT) systems have undergone significant evolution and advancements in technology since they came into clinical practice in 2006. The basic principle of dual-energy is comparing the attenuation of different materials when exposed to high and low energy levels. In this article, we provide a brief overview of the basics of dual-energy CT systems, a pictorial review of commonly encountered abdominal conditions, and its role as a trouble-shooter in various diagnostic difficulties.

Gadolinium Enhances Dual-energy Computed Tomography Scan of Pulmonary Artery

OBJECTIVE

To evaluate the feasibility of using gadopentetate dimeglumine (Gd-DTPA) for dual-energy computed tomography pulmonary angiography (CTPA).

METHODS

Sixty-six patients were randomly divided into three groups and underwent CTPA. Group A had a turbo flash scan using an iohexol injection, Group B had a turbo flash scan using Gd-DTPA, and Group C had a dual-energy scan using Gd-DTPA. The original images of Group C were linearly blended with a blending factor of 0.5 or reconstructed at 40, 50, 60, 70, 80, 90, 100, and 110 keV, respectively. The groups were compared in terms of pulmonary artery CT value, image quality, and radiation dose.

RESULTS

The pulmonary artery CT values were significantly higher in Group C_40keV than in Groups B and C, but lower than in Group A. There was no significant difference in the image noise of Groups C_40keV, B, and C. Moreover, Group A had the largest beam hardening artifacts of the superior vena cava (SVC), followed by Groups B and C. Group C_40keV showed better vascular branching than the other three groups, among which Group B was superior to Group A. The subjective score of the image quality of Groups A, B, and C showed no significant difference, but the score was significantly higher in Group C_40keV than in Groups A and B. The radiation dose was significantly lower in Group B than in Groups A and C.

CONCLUSION

Gd-CTPA is recommended to patients who are unsuitable for receiving an iodine-based CTPA. Furthermore, a turbo flash scan could surpass a dual-energy scan without consideration for virtual monoenergetic imaging.

Spectral CT reconstruction via Spectral-Image Tensor and Bidirectional Image-gradient minimization

It is challenging to obtain good image quality in spectral computed tomography (CT) as the photon-number for the photon-counting detectors is limited for each narrow energy bin. This results in a lower signal to noise ratio (SNR) for the projections. To handle this issue, we first formulate the weight bidirectional image gradient with L₀-norm constraint of spectral CT image. Then, as a new regularizer, bidirectional image gradient with L₀-norm constraint is introduced into the tensor decomposition model, generating the Spectral-Image Tensor and Bidirectional Image-gradient Minimization (SITBIM) algorithm. Finally, the split-Bregman method is employed to optimize the proposed SITBIM mathematical model. The experiments on the numerical mouse phantom and real mouse experiments are designed to validate and evaluate the SITBIM method. The results demonstrate that the SITBIM can outperform other state-of-the-art methods (including TVM, TV + LR, SSCMF and NLCTF). INDEX TERMS: -spectral CT, image reconstruction, tensor decomposition, unidirectional image gradient, image similarity.

Development and validation of a radiomic nomogram based on pretherapy dual-energy CT for distinguishing adenocarcinoma from squamous cell carcinoma of the lung

Objective

Based on pretherapy dual-energy computed tomography (DECT) images, we developed and validated a nomogram combined with clinical parameters and radiomic features to predict the pathologic subtypes of non-small cell lung cancer (NSCLC) - adenocarcinoma (ADC) and squamous cell carcinoma (SCC).

Methods

A total of 129 pathologically confirmed NSCLC patients treated at the Second Affiliated Hospital of Nanchang University from October 2017 to October 2021 were retrospectively analyzed. Patients were randomly divided in a ratio of 7:3 (n=90) into training and validation cohorts (n=39). Patients' pretherapy clinical parameters were recorded. Radiomics features of the primary lesion were extracted from two sets of monoenergetic images (40 keV and 100 keV) in arterial phases (AP) and venous phases (VP). Features were selected successively through the intra-class correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO). Multivariate logistic regression analysis was then performed to establish predictive models. The prediction performance between models was evaluated and compared using the receiver operating characteristic (ROC) curve, DeLong test, and Akaike information criterion (AIC). A nomogram was developed based on the model with the best predictive performance to evaluate its calibration and clinical utility.

Results

A total of 87 ADC and 42 SCC patients were enrolled in this study. Among the five constructed models, the integrative model (AUC: Model 4 = 0.92, Model 5 = 0.93) combining clinical parameters and radiomic features had a higher AUC than the individual clinical models or radiomic models (AUC: Model 1 = 0.84, Model 2 = 0.79, Model 3 = 0.84). The combined clinical-venous phase radiomics model had the best predictive performance, goodness of fit, and parsimony; the area under the ROC curve (AUC) of the training and validation cohorts was 0.93 and 0.90, respectively, and the AIC value was 60.16. Then, this model was visualized as a nomogram. The calibration curves demonstrated it's good calibration, and decision curve analysis (DCA) proved its clinical utility.

Conclusion

The combined clinical-radiomics model based on pretherapy DECT showed good performance in distinguishing ADC and SCC of the lung. The nomogram constructed based on the best-performing combined clinical-venous phase radiomics model provides a relatively accurate, convenient and noninvasive method for predicting the pathological subtypes of ADC and SCC in NSCLC.

Virtual monochromatic spectral attenuation curve analysis for evaluation of incidentally detected small renal lesions using rapid kilovoltage-switching dual-energy computed tomography

PURPOSE

To determine whether the spectral attenuation curve on a rapid kilovoltage-switching dual-energy computed tomography (DECT) scan can distinguish enhancing from nonenhancing incidental small (1-4 cm) renal lesions compared with conventional single-energy attenuation changes.

METHODS

This retrospective study enrolled 46 patients with 78 renal lesions (24 enhancing; 54 nonenhancing) who underwent DECT with DE mode performed during the portovenous or nephrographic phase. Final diagnosis of enhancing and nonenhancing masses was confirmed by pathology or imaging following the established criteria. Virtual monochromatic images (VMI) were reconstructed, and the slopes between the VMI dataset at 40-70 keV (Slope HU_40-70), 40-100 keV (Slope HU_40-100), and 40-140 keV (Slope HU_40-140) were measured. Visual assessment of the curve pattern was recorded. Diagnostic accuracies were calculated with a cross-validated Mann-Whitney U test, and correlations of quantitative spectral parameters and intraclass correlation coefficient (ICC) were calculated using Spearman's rho correlation.

RESULTS

All quantitative and qualitative spectral analysis parameters significantly differentiated the enhancing and nonenhancing lesions (P < 0.001). The optimal slope thresholds calculated by cross-validation for Slope HU_40-70, Slope HU_40-100, and Slope HU_40-140 were 3.0, 1.8 and 1.2, respectively for reader 1 and 3.0, 1.9 and 1.15, respectively for reader 2. Using a slope threshold at all datasets yielded a high diagnostic accuracy of 96 for reader 1 and 95 for reader 2. Using a ∆HU threshold of 20 HU yielded an accuracy of 100. Visual analysis of the curve pattern also yielded high accuracy of 94.

CONCLUSIONS

The spectral attenuation curve on rapid kilovoltage-switching DECT gives excellent diagnostic accuracy differentiating between incidental enhancing and nonenhancing renal lesions. This benefit of DECT will be most helpful when the true unenhanced phase is not performed.

Diagnostic performance of dual-energy computed tomography for HCC after transarterial chemoembolization: Utility of virtual unenhanced and low keV virtual monochromatic images

The purpose of this study is to evaluate the usefulness of virtual unenhanced (VUE) and low keV virtual monochromatic images (VMI) for diagnosing viable hepatocellular carcinomas (HCC) after transarterial chemoembolization (TACE). This retrospective study included 53 patients with suspected viable HCC after TACE who underwent multiphasic liver computed tomography including true unenhanced (TUE) phase and conventional (CV) enhanced phases on a dual-energy scanner. VUE images, 40 keV and 55 keV VMIs of enhanced phases were reconstructed using dual-energy computed tomography data. For every patient, six combination image sets (TUE-CV; TUE-55; TUE-40; VUE-CV; VUE-55; VUE-40) were evaluated by two readers and compared with the reference standard.There was no statistically significant difference (P > .05) in sensitivity or specificity among all image combinations. In most combinations, interobserver agreements were almost perfect. The diagnostic odds ratio showed a higher trend in combinations with conventional images. Currently, with regards to diagnostic performance, liver computed tomography including TUE and CV enhanced phases is recommended for tumor surveillance after TACE because VUE and VMIs do not have a distinct advantage compared to conventional images.

Differentiation of Intracerebral Tumor Entities with Quantitative Contrast Attenuation and Iodine Mapping in Dual-Layer Computed Tomography

Purpose: To investigate if quantitative contrast enhancement and iodine mapping of common brain tumor (BT) entities may correctly differentiate between tumor etiologies in standardized stereotactic CT protocols. Material and Methods: A retrospective monocentric study of 139 consecutive standardized dual-layer dual-energy CT (dlDECT) scans conducted prior to the stereotactic needle biopsy of untreated primary brain tumor lesions. Attenuation of contrast-enhancing BT was derived from polyenergetic images as well as spectral iodine density maps (IDM) and their contrast-to-noise-ratios (CNR) were determined using ROI measures in contrast-enhancing BT and healthy contralateral white matter. The measures were correlated to histopathology regarding tumor entity, isocitrate dehydrogenase (IDH) and MGMT mutation status. Results: The cohort included 52 female and 76 male patients, mean age of 59.4 (±17.1) years. Brain lymphomas showed the highest attenuation (IDM CNR 3.28 ± 1,23), significantly higher than glioblastoma (2.37 ± 1.55, p < 0.005) and metastases (1.95 ± 1.14, p < 0.02), while the differences between glioblastomas and metastases were not significant. These strongly enhancing lesions differed from oligodendroglioma and astrocytoma (Grade II and III) that showed IDM CNR in the range of 1.22−1.27 (±0.45−0.82). Conventional attenuation measurements in DLCT data performed equally or slightly superior to iodine density measurements. Conclusion: Quantitative attenuation and iodine density measurements of contrast-enhancing brain tumors are feasible imaging biomarkers for the discrimination of cerebral tumor lesions but not specifically for single tumor entities. CNR based on simple HU measurements performed equally or slightly superior to iodine quantification.

Validation of Virtual Monochromatic Images and Effect of Body Size Obtained Using a Rapid kVp-switching Dual-energy Computed Tomography System: A Phantom Study

The objective of this paper is to validate virtual monochromatic computed tomography (CT) numbers and the effect of the body size of insert materials in phantoms on the findings of a dual-energy CT scanner. The material inserted in the phantom simulates human organs. This study investigated the effect of different body sizes on CT numbers to understand the accuracy of dual-energy CT. The effect of body size on virtual monochromatic CT numbers was investigated using a QRM phantom. The true monochromatic CT numbers of insert materials were calculated from coefficients obtained using NIST XCOM. The true Z eff values were supplied by phantom manufacturers or computed using Mayneord's equation. The virtual monochromatic CT numbers of insert materials in both the phantoms varied with energy. The CT numbers of materials with a Z eff of >7.42 (water Z eff ) and <7.42 decreased and increased with energy, respectively. The CT numbers were affected by phantom size as a function of energy. For water, tissues, and air, the CT numbers in the XL phantom were considerably larger than those in other phantom sizes at 40 keV. Body size affected the CT numbers, particularly for the XL size and at low energies. For all materials, the magnitude of difference between the measured and true CT numbers was related to the Z eff of the materials, potentially because the photoelectric effect is more prominent at low energies for materials with a higher Z eff . The difference in CT numbers appeared to be dependent on position. The true and measured Z eff agreed to within 6% for all the materials except the SR2 brain, for which the discrepancy was 25%.

Quantitative dual-energy CT as a nondestructive tool to identify indicators for fossilized bone in vertebrate paleontology

Dual-energy computed tomography (DECT) is an imaging technique that combines nondestructive morphological cross-sectional imaging of objects and the quantification of their chemical composition. However, its potential to assist investigations in paleontology has not yet been explored. This study investigates quantitative DECT for the nondestructive density- and element-based material decomposition of fossilized bones. Specifically, DECT was developed and validated for imaging-based calcium and fluorine quantification in bones of five fossil vertebrates from different geological time periods and of one extant vertebrate. The analysis shows that DECT material maps can differentiate bone from surrounding sediment and reveals fluorine as an imaging marker for fossilized bone and a reliable indicator of the age of terrestrial fossils. Moreover, the jaw bone mass of Tyrannosaurus rex showed areas of particularly high fluorine concentrations on DECT, while conventional CT imaging features supported the diagnosis of chronic osteomyelitis. These findings highlight the relevance of radiological imaging techniques in the natural sciences by introducing quantitative DECT imaging as a nondestructive approach for material decomposition in fossilized objects, thereby potentially adding to the toolbox of paleontological studies.

Association between quantitative spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as ground-glass nodules

BACKGROUND

Few studies about lung ground-glass nodules (GGNs) have been done using non-enhancement spectral computed tomography (CT) imaging.

PURPOSE

To examine the association between spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as GGNs.

MATERIAL AND METHODS

Spectral CT parameters were analyzed in 106 patients with lung GGNs. The Ki-67 labeling index (Ki-67 LI) was measured, and patients were divided into low expression and high expression groups according to the number of positive-stained cells (low expression ≤10%; high expression >10%). Spectral CT parameters were compared between low and high expression groups. The correlation between spectral CT parameters and Ki-67 LI was estimated by Spearman correlation analysis. Cases were divided into a preinvasive and minimally invasive adenocarcinoma (MIA) group (atypical adenomatous hyperplasia, adenocarcinoma in situ, and MIA) and invasive adenocarcinoma (IA) group. Spectral CT parameters were compared between the two groups. The diagnostic performance was evaluated using receiver operating characteristic analysis.

RESULTS

There were significant differences in water concentration of lesions (WCL) and monochromatic CT values between the low and high expression groups. CT 40 keV had the highest correlation coefficient with Ki-67 LI. WCL and monochromatic CT values were significantly higher in the IA group than in the pre/MIA group. The value of area under the curve of CT 40 keV was 0.946 (95% confidence interval=0.905-0.988) for differentiating the two groups; the cutoff was -280.66 Hu.

CONCLUSION

Spectral CT is an effective non-invasive method for the prediction of proliferation and invasiveness in lung adenocarcinoma manifesting as GGNs.

The value of radiomics based on dual-energy CT for differentiating benign from malignant solitary pulmonary nodules

OBJECTIVE

To investigate the value of monochromatic dual-energy CT (DECT) images based on radiomics in differentiating benign from malignant solitary pulmonary nodules.

MATERIALS AND METHODS

This retrospective study was approved by the institutional review board, and informed consent was waived. Pathologically confirmed lung nodules smaller than 3 cm with integrated arterial phase and venous phase (AP and VP) gemstone spectral imaging were retrospectively identified. After extracting the radiomic features of each case, principal component analysis (PCA) was used for feature selection, and after training with the logistic regression method, three classification models (Model_AP, Model_VP and Model_Combination) were constructed. The performance was assessed by the area under the receiver operating curve (AUC), and the efficacy of the models was validated using an independent cohort.

RESULTS

A total of 153 patients were included and divided into a training cohort (n = 107) and a validation cohort (n = 46). A total of 1130 radiomic features were extracted from each case. The PCA method selected 22, 25 and 35 principal components to construct the three models. The diagnostic accuracy of Model_AP, Model_VP and Model_Combination was 0.8043, 0.6739, and 0.7826 in the validation set, with AUCs of 0.8148 (95% CI 0.682-0.948), 0.7485 (95% CI 0.602-0.895), and 0.8772 (95% CI 0.780-0.974), respectively. The DeLong test showed that there were significant differences in the AUCs between Model_AP and Model_Combination (P = 0.0396) and between Model_VP and Model_Combination (P = 0.0465). However, the difference in AUCs between Model_AP and Model_VP was not significant (P = 0.5061). These results demonstrate that Model_Combination shows a better performance than the other models. Decision curve analysis proved the clinical utility of this model.

CONCLUSIONS

We developed a radiomics model based on monochromatic DECT images to identify solitary pulmonary nodules. This model could serve as an effective tool for discriminating benign from malignant pulmonary nodules in patients. The combination of arterial phase and venous phase imaging could significantly improve the model performance.

Utility of Dual-Energy CT in Abdominal Interventions

Dual-energy computed tomography (DECT) is an emerging CT technique based on data acquisition at two different settings. Various postprocessing techniques generate different sets of images, each with unique advantages. With DECT, it is possible to obtain virtual unenhanced images from monochromatic reconstructions and attenuation maps of different elements, thereby improving the detection and characterization of a variety of lesions. Presently, DECT is widely used to evaluate pulmonary embolism, characterize abdominal masses, determine the composition of urinary calculi, and detect tophi in gout. CT angiography is an essential prerequisite for endovascular intervention. DECT allows a better quality of angiographic images with a lesser dose of contrast. Various postprocessing techniques in DECT also help in a better evaluation of response to locoregional therapy. Virtual noncontrast images and iodine map differentiate residual or recurrent tumors from intrinsically hyperdense materials. Superior metallic artifact reduction allows better evaluation of vascular injuries adjacent to bony fractured fragments or previously deployed embolization coils. In addition to metal artifacts reduction, virtual monochromatic spectral imaging could further mitigate metal artifacts during CT-guided biopsy, providing an improved depiction of lesions and safe and versatile access for long puncture pathways. This article reviews and illustrates the different applications of DECT in various abdominal interventions. Familiarity with the capabilities of DECT may help interventional radiologists to improve their practice and ameliorate patient care.

Virtual Unenhanced Images: Qualitative and Quantitative Comparison Between Different Dual-Energy CT Scanners in a Patient and Phantom Study

MATERIALS AND METHODS

Forty-four patients with clinical contrast-enhanced abdominal examinations on each of the 3 DECT scanner types and a phantom scanned with the same protocols were included in this retrospective study. Qualitative and quantitative assessment was performed on VUE images. Quantitative evaluation included measurement of attenuation and image noise for various tissues and the phantom. Virtual unenhanced image attenuation and noise were compared between scanner types, and intrapatient interscanner reproducibility of virtual unenhanced image attenuation was calculated as the percentage of measurement pairs with an interscanner difference ≤ 10 HU. Image quality, noise, sharpness, and iodine elimination were assessed qualitatively by 2 radiologists.

RESULTS

Significant interscanner differences in VUE attenuation and noise were found in all tissues. dlDECT and rsDECT showed significantly higher VUE attenuation than dsDECT in the aorta, portal vein, and kidneys (P < 0.05). Conversely, VUE attenuation in dsDECT was significantly higher than in dlDECT/rsDECT for subcutaneous and retroperitoneal fat (both P < 0.05). A total of 91.9% (385/419) of measurements were reproducible between rsDECT and dlDECT, 70.9% (297/419) between dsDECT and rsDECT, and 66.8% (280/419) between dsDECT and dlDECT. Virtual unenhanced image attenuation in the contrast media-filled phantom cavity was 12.7 ± 4.7 HU in dlDECT, -5.3 ± 4.2 HU in rsDECT, and -4.0 ± 10.7 HU in dsDECT with significant differences between dlDECT and rsDECT/dsDECT, respectively (P < 0.05), between which attenuation was comparable in the unenhanced extraluminal phantom component (P = 0.11-0.62). Qualitatively, dsDECT yielded best iodine elimination, whereas sharpness, image noise, and overall image quality were rated higher in dlDECT and rsDECT.

CONCLUSIONS

There are significant interscanner differences in the attenuation measurements and qualitative assessment of VUE images, which should be acknowledged when using these images in patients that are being scanned on different DECT scanner types during imaging follow-up.

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

Objective

To build and assess a pre-treatment dual-energy CT-based clinical-radiomics nomogram for the individualized prediction of clinical response to systemic chemotherapy in advanced gastric cancer (AGC).

Methods

A total of 69 pathologically confirmed AGC patients who underwent dual-energy CT before systemic chemotherapy were enrolled from two centers in this retrospective study. Treatment response was determined with follow-up CT according to the RECIST standard. Quantitative radiomics metrics of the primary lesion were extracted from three sets of monochromatic images (40, 70, and 100 keV) at venous phase. Univariate analysis and least absolute shrinkage and selection operator (LASSO) were used to select the most relevant radiomics features. Multivariable logistic regression was performed to establish a clinical model, three monochromatic radiomics models, and a combined multi-energy model. ROC analysis and DeLong test were used to evaluate and compare the predictive performance among models. A clinical-radiomics nomogram was developed; moreover, its discrimination, calibration, and clinical usefulness were assessed.

Result

Among the included patients, 24 responded to the systemic chemotherapy. Clinical stage and the iodine concentration (IC) of the tumor were significant clinical predictors of chemotherapy response (all p < 0.05). The multi-energy radiomics model showed a higher predictive capability (AUC = 0.914) than two monochromatic radiomics models and the clinical model (AUC: 40 keV = 0.747, 70 keV = 0.793, clinical = 0.775); however, the predictive accuracy of the 100-keV model (AUC: 0.881) was not statistically different (p = 0.221). The clinical-radiomics nomogram integrating the multi-energy radiomics signature with IC value and clinical stage showed good calibration and discrimination with an AUC of 0.934. Decision curve analysis proved the clinical usefulness of the nomogram and multi-energy radiomics model.

Conclusion

The pre-treatment DECT-based clinical-radiomics nomogram showed good performance in predicting clinical response to systemic chemotherapy in AGC, which may contribute to clinical decision-making and improving patient survival.

Improved characterization of focal airway lesions using spectral detector dual energy CT

Clinicians should be aware of SDCT as a useful tool in the assessment of focal airway lesions. Spectral detector dual-energy computed tomography (SDCT) is a relatively novel imaging technology which has been utilized to aid in the diagnosis of many cardiothoracic conditions. Specifically, the availability of generated iodine density maps, virtual monoenergetic images, and effective atomic number maps allow for better evaluation of thoracic lesions compared to conventional CT. SDCT has previously been shown to be useful in the differentiation of benign vs malignant pulmonary nodules, pleural lesions, and lymph nodes. We describe 3 cases in which a patient presents with an indeterminate tracheal or bronchial lesion on conventional CT and subsequent SDCT reconstructions provided additional information which helped guide diagnosis or management of the patient. The goal is to help clinicians understand the benefit of SDCT in the detection and workup of airway lesions.

Spectral detector CT applications in advanced liver imaging

OBJECTIVE

Spectral detector CT (SDCT) has many applications in advanced liver imaging. If appropriately utilized, this technology has the potential to improve image quality, provide new diagnostic information, and allow for decreased radiation dose. The purpose of this review is to familiarize radiologists with the uses of SDCT in liver imaging.

CONCLUSION

SDCT has a variety of post-processing techniques, which can be used in advanced liver imaging and can significantly add value in clinical practice.

Nonlinear reconstruction of coded spectral X-ray CT based on material decomposition

Coded spectral X-ray computed tomography (CT) based on K-edge filtered illumination is a cost-effective approach to acquire both 3-dimensional structure of objects and their material composition. This approach allows sets of incomplete rays from sparse views or sparse rays with both spatial and spectral encoding to effectively reduce the inspection duration or radiation dose, which is of significance in biological imaging and medical diagnostics. However, reconstruction of spectral CT images from compressed measurements is a nonlinear and ill-posed problem. This paper proposes a material-decomposition-based approach to directly solve the reconstruction problem, without estimating the energy-binned sinograms. This approach assumes that the linear attenuation coefficient map of objects can be decomposed into a few basis materials that are separable in the spectral and space domains. The nonlinear problem is then converted to the reconstruction of the mass density maps of the basis materials. The dimensionality of the optimization variables is thus effectively reduced to overcome the ill-posedness. An alternating minimization scheme is used to solve the reconstruction with regularizations of weighted nuclear norm and total variation. Compared to the state-of-the-art reconstruction method for coded spectral CT, the proposed method can significantly improve the reconstruction quality. It is also capable of reconstructing the spectral CT images at two additional energy bins from the same set of measurements, thus providing more spectral information of the object.

Comparison between Conventional Unenhanced and Virtual Unenhanced Imaging of Hepatopancreaticobiliary System with Third-Generation Dual-Source Dual-Energy CT—An Observational Study

Objectives The aims of our study were to assess the comparability of conventional unenhanced images (CUIs) of hepatopancreaticobiliary system with virtual unenhanced images (VUIs) derived from arterial and portal venous phases acquired in a third-generation, dual-source, dual-energy CT (DECT), and also to assess the best dataset among these VUIs. We also calculated the radiation effective dose (ED) reduction by eliminating noncontrast acquisition.

Materials and Methods 60 patients were included in our study. Unenhanced images in single energy and contrast-enhanced images in dual-energy mode were acquired. Arterial virtual unenhanced (AVU) and portal virtual unenhanced (PVU) images were generated and compared with CUI, using both objective and subjective methods. The ED was calculated separately for each phase. Statistical significance between difference in mean attenuation values were analyzed using ANOVA and unpaired student t-test.

Results In our study, the difference in mean attenuation of liver, spleen, and pancreas between the three phases—CU, AVU, and PVU—were insignificant with p-value > 0.05. This indicates that the values were comparable. Among the VUI, AVU images were statistically superior in image quality. Elimination of noncontrast CT from triple phase abdominal imaging can achieve an average ED reduction of 39%.

Conclusions We conclude that VUI generated in third-generation, dual-source DECT has diagnostic image quality and can replace the CUI in triple-phase studies, with a mean ED reduction by 39%. The VUI obtained from arterial phase is superior to those obtained from portal venous phase.

Consolidated lung on contrast-enhanced chest CT: the use of spectral-detector computed tomography parameters in differentiating atelectasis and pneumonia

Objectives

To investigate the value of spectral-detector computed tomography (SDCT) parameters for the quantitative differentiation between atelectasis and pneumonia on contrast-enhanced chest CT.

Material and methods

Sixty-three patients, 22 clinically diagnosed with pneumonia and 41 with atelectasis, underwent contrast-enhanced SDCT scans during the venous phase. CT numbers (Hounsfield Units [HU]) were measured on conventional reconstructions (CON_120kVp) and the iodine concentration (C_iodine, [mg/ml]), and effective atomic number (Z_eff) on spectral reconstructions, using region-of-interest (ROI) analysis. Receiver operating characteristics (ROC) and contrast-to-noise ratios (CNRs) were calculated to assess each reconstruction's potential to differentiate between atelectasis and pneumonia.

Results

On contrast-enhanced SDCT, the difference between atelectasis and pneumonia was significant on CON_120kVp, C_iodine, and Z_eff images (p < 0.001). On CON_120kVp images, a threshold of 81 HU achieved a sensitivity of 93 % and a specificity of 95 % for identifying pneumonia, while C_iodine and Z_eff images reached the same sensitivity but lower specificities of 85 % and 83 %. CON_120kVp images showed significantly higher CNRs between normal lung and atelectasis or pneumonia with 30.63 and 27.69 compared to C_iodine images with 3.54 and 1.27 and Z_eff images with 4.22 and 7.63 (p < 0.001). None of the parameters could differentiate atelectasis and pneumonia without contrast media.

Conclusions

Contrast-enhanced SDCT can differentiate atelectasis and pneumonia based on the spectral parameters C_iodine, and Z_eff. However, they had no added value compared to CT number measurement on CON_120kVp images. Furthermore, contrast media is still needed for a differentiation based on quantitative SDCT parameters.

The Role of Dual-Energy Computed Tomography in Locating Gastrointestinal Tract Perforations

Objective With each passing day, dual-energy computed tomography (DECT) is being used more frequently in the evaluation of abdominal pathologies. In this article, we aimed to assess the role of dual-energy CT in locating gastrointestinal perforations, which are among the causes of acute abdomen. Materials and methods All patients who underwent DECT due to acute abdomen in a COVID-19 designated hospital between June 1st, 2020 and December 31st, 2020, who were found to have gastrointestinal tract (GIT) perforation and underwent surgery were included in the study. DECT results and intraoperative findings of the patients were compared. Results Thirteen patients (nine males and four females) who underwent DECT for acute abdomen and were diagnosed with perforation in the gastrointestinal system were included in the study. The mean age of the patients was 57.6 years (range: 11-85 years). Two patients had gastric perforation, three had duodenal perforations, and one patient had a perforation in the gallbladder wall. Two patients were diagnosed with jejunal perforations, one patient with Meckel's diverticulum, and three patients with colorectal perforation. Although free air was detected in the abdomen of one patient, perforation could not be located. In patients with GIT perforation who were operated on following DECT imaging, the perforation location shown on DECT correlated 100% with the perforation locations detected during surgery. Conclusion DECT is significantly effective in planning surgical treatment and determining the foci of perforation in GIT perforations.

TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer

OBJECTIVE. The purpose of this study was to investigate the value of TCGA-TCIA (The Cancer Genome Atlas and The Cancer Imaging Archive)-based CT radiomics for noninvasive prediction of Epstein-Barr virus (EBV) status in gastric cancer (GC). MATERIALS AND METHODS. A total of 133 patients with pathologically confirmed GC (94 in the training cohort and 39 in the validation cohort) who were identified from the TCGA-TCIA public data repository and two hospitals were retrospectively enrolled in the study. Two-dimensional and 3D radiomics features were extracted to construct corresponding radiomics signatures. Then, 2D and 3D nomograms were built by combining radiomics signatures and clinical information on the basis of multivariable analysis. Their performance and clinical practicability were determined, validated, and compared with respect to discrimination, calibration, reclassification, and time spent on tumor segmentation. RESULTS. Both 2D and 3D nomograms were robust and showed good calibration. The AUCs of the 2D and 3D nomograms showed no significant difference in the training cohort (0.919 vs 0.945, respectively; p = .41) or validation cohort (0.939 vs 0.955, respectively; p = .71). The net reclassification index showed that the 3D nomogram revealed no significant improvement in risk reclassification when compared with the 2D nomogram in the training cohort (net reclassification index, 0.68%; p = .14) and the validation cohort (net reclassification index, 6.06%; p = .08). Of note, the time spent on 3D segmentation (median, 907 seconds) was higher than that spent on 2D segmentation (median, 129 seconds). CONCLUSION. The 2D and 3D radiomics nomograms might have the potential to be used as effective tools for prediction of EBV in GC. When time spent on segmentation is considered, the 2D nomogram is more highly recommended for clinical application.

The value of a dual-energy spectral CT quantitative analysis technique in acute pancreatitis

AIM

To explore the value of dual-energy spectral computed tomography (DESCT) in evaluating the clinical severity of acute pancreatitis.

MATERIALS AND METHODS

Seventy patients with acute pancreatitis (AP) confirmed by clinical examination were included in this study. All patients underwent unenhanced/double-phase enhanced CT in spectral imaging mode. Iodine concentration and normalised iodine concentration (NIC) were measured retrospectively with a spectral imaging viewer (GSI Viewer). All data were analysed by analysis of variance. Receiver operating characteristic (ROC) curves were constructed to determine the optimal threshold for predicting the clinical severity of AP.

RESULTS

Seventy patients were included in the study comprising 30 mild, 22 moderate, and 18 severe cases of AP. The CT attenuation value, iodine concentration, and NIC were decreased with increasing clinical severity. Moreover, there were significant differences between the mild group and the severe group (p<0.05), as well as between the moderate group and the severe group (p<0.05). The area under the ROC curve AUC of each value was larger in arterial phase than in portal venous phase. The most sensitive value between the mild and severe groups in AP was the NIC (arterial phase: 0.19 ± 0.06; portal venous phase: 0.45 ± 0.09).

CONCLUSION

DESCT can provide multiple parameters to determine the severity of AP.

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

RATIONALE AND OBJECTIVES

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules.

MATERIALS AND METHODS

We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests.

RESULTS

This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D_max), the diameter of the solid component (D_solid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT_{40 keV-190 keV}) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D_max, and CT_{60 keV} values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D_max, and CT_{60 keV} was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05).

CONCLUSION

The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

[CT spectral curve in differentiating spinal tumor metastasis and infections]

OBJECTIVE

To evaluate the value of CT spectral curve in differentiating spinal tumor metastasis (STM) from spinal infections (SI).

METHODS

In the study, 29 STM and 18 SI patients proved pathologically and clinically were examined by dual energy spectral CT (DESCT). The monochromatic images and CT spectral curves were generated automatically by GSI Viewer software. The attenuation values at different energy levels (40-140 keV, every 10 keV), the attenuation values of the lesions on the conventional polychromatic CT images and the gradients of the curve were calculated and compared between STM and SI.

RESULTS

The median age of STM and SI (58 years vs. 64 years) were not significantly different (U=171, P=0.4). The attenuation values of STM at 40-100 keV were 281.79 (143.67, 446.19) HU, 199.68 (100.04, 321.49) HU, 151.54 (81.47, 243.49) HU, (122.64±27.72) HU, (99.90±23.88) HU, (85.82±21.61) HU, and (75.94±20.27) HU, respectively, which were significantly higher than SI: 185.29 (164.19, 277.03) HU, 138.44 (124.98, 238.56) HU, 105.46 (92.94, 169.53) HU, (93.77±15.55) HU, (79.15±12.84) HU, (68.99±11.75) HU, and (62.22±11.71) HU (all P < 0.05). The attenuation values at 110-140 keV and the attenuation value on the conventional CT images were not significantly different between STM and SI. The gradient of CT spectral curve of STM was 2.43±0.58, which was higher than the value of 1.50±0.40 for SI (P < 0.001). Using 1.72 and 248.80 HU as the threshold value for CT spectral curve slope and the attenuation value at 40 keV, could obtain the area under receiver operating characteristic (ROC) curve of 0.905 and 0.892, sensitivity of 88.0% and 80.0%, and specificity of 76.9% and 92.3%.

CONCLUSION

CT spectral curve provides valuable semi-quantitative information for the differential diagnosis of STM and SI, which can be used as a supplement to traditional CT imaging.

Feasibility of Coronary Artery Calcium Scoring on Dual-Energy Chest Computed Tomography: A Prospective Comparison with Electrocardiogram-Gated Calcium Score Computed Tomography

Rationale and Objectives: This study aimed to evaluate the feasibility of assessment using the coronary artery calcium score (CACS) in dual-energy chest computed tomography (CT). Materials and Methods: We prospectively enrolled 30 patients (19 male, 11 female; mean age, 63.73 ± 9.40 years) who clinically required contrast-enhanced chest CT. The patients underwent electrocardiogram-gated cardiac calcium-scoring CT with a slice thickness of 2.5 mm followed by a sequentially non-gated contrast-enhanced dual-energy chest CT using 140/80 fast kVp switching technology with slice thicknesses of 1.25 mm and 2.5 mm. Virtual unenhanced (VUE) images were then reconstructed from the dual-energy CT using the material suppressed iodine (MSI) technique. Results: The mean heart rates were 63.33 ± 12.01 beats per minute. The mean CACS on the coronary calcium-scoring CT was 361.1 ± 435.5, and CACSs of the VUE images were 76.8 ± 128.6 (2.5 mm slice) and 108.7 ± 165.1 (1.25 mm slice). The correlation coefficients of CACS between the coronary calcium-scoring CT with the VUE 2.5 mm and 1.25 mm images were 0.888 and 0.904, respectively. The inter-observer agreements for the calcium score measurement between the calcium-scoring CT, VUE 2.5 mm, and VUE 1.25 mm were 1.000, 0.999, and 1.000, respectively. Conclusions: In conclusion, assessment of CACS using dual-energy chest CT might be feasible when using MSI virtual unenhanced dual-energy chest CT images with a slice thickness of 1.25 mm.

Rapid-kilovoltage-switching dual-energy computed tomography (CT) for differentiating spinal osteolytic metastases from spinal infections

Background

Rapid-kilovoltage-switching dual-energy computed tomography (RDECT) is a non-invasive, alternative technique for quantitative diagnosis. This study aimed to investigate the value of RDECT for differentiating spinal osteolytic metastases (SOM) from spinal infections (SIs).

Methods

RDECT was performed on 29 patients with SOM and 18 patients with SIs. Both iodine-based and water-based material decomposition images were generated from the spectral CT scan. The iodine/water densities of lesions on iodine/water material-decomposition images and the CT attenuation values on traditional CT images were measured three times at different image levels, and the averages were calculated. The lesion-to-muscle ratio (LMR) and lesion-to-artery ratio (LAR) for iodine density measurements were calculated. All parameters were compared between the two groups using the two-tailed Student's t-test. A P value <0.05 was considered statistically significant. The sensitivity and specificity for differentiating SOM from SIs were determined using receiver operating characteristic curves (ROC).

Results

Iodine density, LMR, and LAR during the arterial phase (AP) and venous phase (VP) were all significantly higher for SOM than for SIs (all P<0.05). The water densities and traditional CT attenuation values during the AP and VP were not significantly different between the two groups. For ROC analysis, LAR during the VP (LAR_VP) showed the best diagnostic performance, with an area under the ROC curve (AUC) value of 0.862. When the LARVP was 0.54, the sensitivity was 82.80% and the specificity was 77.80% for differentiating SOM from SIs.

Conclusions

RDECT can provide additional information that may be useful for differentiating atypical SOM from SIs.

Metal artefact reduction of different alloys with dual energy computed tomography (DECT)

To evaluate the influence of dual-energy CT (DECT) and Virtual monochromatic spectral (VMS) imaging on: (1) the artefact size of geometrically identical orthopaedic implants consisting of three different compositions and (2) the image quality of the surrounding bone, three similar phantoms—each featuring one femoral stem composed of either titanium, chrome-cobalt or stainless steel surrounded by five calcium pellets (200 mg hydroxyapatite/calcium carbonate) to simulate bony tissue and one reference pellet located away from the femoral stem—were built. DECT with two sequential scans (80 kVp and 140 kVp; scan-to-scan technique) was performed, and VMS images were calculated between 40 and 190 keV. The artefact sizes were measured volumetrically by semiautomatic selection of regions of interest (ROIs), considering the VMS energies and the polychromatic spectres. Moreover, density and image noise within the pellets were measured. All three phantoms exhibit artefact size reduction as energy increases from 40 to 190 keV. Titanium exhibited a stronger reduction than chrome-cobalt and stainless steel. The artefacts were dependent on the diameter of the stem. Image quality increases with higher energies on VMS with a better depiction of surrounding structures. Monoenergetic energies 70 keV and 140 keV demonstrate superior image quality to those produced by spectral energies 80 kVp and 140 kVp.

Dual-Energy Computed Tomography of the Liver: Uses in Clinical Practices and Applications

Dual-energy computed tomography (DECT) is an imaging technique based on data acquisition at two different energy settings. Recent advances in CT have allowed data acquisitions and simultaneous analyses of X-rays at two energy levels, and have resulted in novel developments in the field of abdominal imaging. The use of low and high X-ray tube voltages in DECT provide fused images that improve the detection of liver tumors owing to the higher contrast-to-noise ratio (CNR) of the tumor compared with the liver. The use of contrast agents in CT scanning improves image quality by enhancing the CNR and signal-to-noise ratio while reducing beam-hardening artifacts. DECT can improve detection and characterization of hepatic abnormalities, including mass lesions. The technique can also be used for the diagnosis of steatosis and iron overload. This article reviews and illustrates the different applications of DECT in liver imaging.

New advances in CT imaging of pancreas diseases: a narrative review

Computed tomography (CT) plays a pivotal role as a diagnostic tool in many diagnostic and diffuse pancreatic diseases. One of the major limits of CT is related to the radiation exposure of young patients undergoing repeated examinations. Besides the standard CT protocol, the most recent technological advances, such as low-voltage acquisitions with high performance X-ray tubes and iterative reconstructions, allow for significant optimization of the protocol with dose reduction. The variety of CT tools are further expanded by the introduction of dual energy: the production of energy-selective images (i.e., virtual monochromatic images) improves the image contrast and lesion detection while the material-selective images (e.g., iodine maps or virtual unenhanced images) are valuable for lesion detection and dose reduction. The perfusion techniques provide diagnostic and prognostic information lesion and parenchymal vascularization and interstitium. Both dual energy and perfusion CT have the potential for pushing the limits of conventional CT from morphological evaluation to quantitative imaging applied to inflammatory and oncological diseases. Advances in post-processing of CT images, such as pancreatic volumetry, texture analysis and radiomics provide relevant information for pancreatic function but also for the diagnosis, management and prognosis of pancreatic neoplasms. Artificial intelligence is promising for optimization of the workflow in qualitative and quantitative analyses. Finally, basic concepts on the role of imaging on screening of pancreatic diseases will be provided.

Imaging acute pancreatitis

OBJECTIVES

Acute pancreatitis is common; the clinical course of this potentially severe condition varies widely. This paper aims to review the role of different imaging techniques in the management of acute pancreatitis, describe the main imaging findings for this entity, and explain the terms and criteria used to classify them.

CONCLUSIONS

Imaging techniques play a key role in the management of acute pancreatitis, from diagnosis and staging to identifying and treating complications, as well as in determining the underlying causes of the condition. For these reasons, radiologists should know the advantages and limitations of each imaging technique in the evaluation of acute pancreatitis, be familiar with the wide spectrum of imaging findings associated with it, and how to use the specific terminology derived from the Atlanta classification to ensure the standardization and quality of reports.

Low-dose spectral CT reconstruction based on image-gradient L0-norm and adaptive spectral PICCS

The photon-counting detector based spectral computed tomography (CT) is promising for lesion detection, tissue characterization, and material decomposition. However, the lower signal-to-noise ratio within multi-energy projection dataset can result in poorly reconstructed image quality. Recently, as prior information, a high-quality spectral mean image was introduced into the prior image constrained compressed sensing (PICCS) framework to suppress noise, leading to spectral PICCS (SPICCS). In the original SPICCS model, the image gradient L₁-norm is employed, and it can cause blurred edge structures in the reconstructed images. Encouraged by the advantages in edge preservation and finer structure recovering, the image gradient L₀-norm was incorporated into the PICCS model. Furthermore, due to the difference of energy spectrum in different channels, a weighting factor is introduced and adaptively adjusted for different channel-wise images, leading to an L₀-norm based adaptive SPICCS (L₀-ASPICCS) algorithm for low-dose spectral CT reconstruction. The split-Bregman method is employed to minimize the objective function. Extensive numerical simulations and physical phantom experiments are performed to evaluate the proposed method. By comparing with the state-of-the-art algorithms, such as the simultaneous algebraic reconstruction technique, total variation minimization, and SPICCS, the advantages of our proposed method are demonstrated in terms of both qualitative and quantitative evaluation results.

Improved detectability of hypoattenuating focal pancreatic lesions by dual-layer computed tomography using virtual monoenergetic images

Background

Multidetector CT is the mainstay for radiologic evaluation of pancreatic pathology. Still, imaging of focal pancreatic lesions using MDCT is faced by a number of challenges that are related to the limited contrast between the lesion and surrounding parenchyma, such as detecting early-stage pancreatic cancer and subtle features of cystic lesions that point to malignancy. Dual-layer CT is the first dual-energy CT machine based on separation of high- and low-energy photons at the detector level. If improved contrast between the lesions and normal pancreatic parenchyma could be achieved on CT images, we may expect enhanced CT detection of pancreatic lesions. The purpose of this study was to evaluate whether virtual monoenergetic reconstructions generated using contrast-enhanced dual-layer CT could improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

Results

Fifty-four lesions were identified and verified by histopathology or follow-up CT, MRCP, and/or EUS along with clinical data. Across the virtual monoenergetic spectrum, 40 KeV images had the highest contrast-to-noise and signal-to-noise ratios (p < 0.001, p < 0.001) and were significantly higher than conventional images (p < 0.001). Subjective scores for lesion visibility at low kiloelectron volt monoenergetic (40 and 50 KeV) images greatly exceeded conventional images (p < 0.001).

Conclusion

Low kiloelectron volt monoenergetic reconstructions of contrast-enhanced dual-layer CT significantly improve detectability of hypoattenuating focal pancreatic lesions compared to conventional polyenergetic reconstructions.

Virtual Monoenergetic CT Imaging via Deep Learning

Conventional single-spectrum computed tomography (CT) reconstructs a spectrally integrated attenuation image and reveals tissues morphology without any information about the elemental composition of the tissues. Dual-energy CT (DECT) acquires two spectrally distinct datasets and reconstructs energy-selective (virtual monoenergetic [VM]) and material-selective (material decomposition) images. However, DECT increases system complexity and radiation dose compared with single-spectrum CT. In this paper, a deep learning approach is presented to produce VM images from single-spectrum CT images. Specifically, a modified residual neural network (ResNet) model is developed to map single-spectrum CT images to VM images at pre-specified energy levels. This network is trained on clinical DECT data and shows excellent convergence behavior and image accuracy compared with VM images produced by DECT. The trained model produces high-quality approximations of VM images with a relative error of less than 2%. This method enables multi-material decomposition into three tissue classes, with accuracy comparable with DECT.

Virtual unenhanced imaging of the liver derived from 160-mm rapid-switching dual-energy CT (rsDECT): Comparison of the accuracy of attenuation values and solid liver lesion conspicuity with native unenhanced images

OBJECTIVES

To evaluate the reliability of attenuation values of the liver parenchyma and focal liver lesions on virtual unenhanced images from arterial (VUEart) and portal venous phases (VUEport) compared to native unenhanced (NU) attenuation values in patients referred for assessment of malignant liver lesions.

METHODS

Seventy-three patients with confirmed primary or metastatic liver tumors who underwent a multiphase contrast-enhanced rapid-switching kVp dual-energy CT (rsDECT) were included in this IRB-approved retrospective study. Both qualitative and quantitative analyses - including the lesion-to-liver contrast-to-noise ratio (LL-CNR) - were performed and compared between NU and both VUEart and VUEport images.

RESULTS

The mean liver attenuation values were significantly lower in VUEart images (56.7 ± 6.7 HU) than in NU images (59.6 ± 7.5 HU, p = 0.008), and were comparable between VUEart and VUEport images (57.9 ± 6 UH, p = 0.38) and between VUEport and NU images (p = 0.051). The mean liver lesions attenuation values were comparable between NU, VUEart and VUEport images (p = 0.60). Strong and significant correlations values were found both in liver lesions and tumor-free parenchyma (r = 0.82-0.91, p < 0.01). The mean LL-CNR was significantly higher in VUEart and VUEport images than in NU images (1.7 ± 1 and 1.6 ± 1.1 vs 0.9 ± 0.6; p < 0.001), but was comparable between VUEart and VUEport images (p > 0.9). Lesion conspicuity was significantly higher in VUEport images than in NU images (p < 0.001).

CONCLUSION

VUEport images derived from 3rd generation rsDECT could confidently replace NU images in patients undergoing assessment for malignant liver lesions. These images provide comparable attenuation values in both liver lesions and liver parenchyma while reducing the radiation dose and scanning time.

Dual-energy CT enterography in evaluation of Crohn's disease: the role of virtual monochromatic images

PURPOSE

To assess the use of virtual monochromatic images (VMI) for discrimination of affected and non-affected bowel walls in patients with Crohn's disease (CD) as well as to compare mural enhancement between patients with and without CD.

MATERIALS AND METHODS

This retrospective study included 61 patients (47 with CD, 14 without CD). Attenuation value (AV), signal-to noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained at VMI energy levels from 40 to 110 keV in 10 keV increment. Analyses were performed among affected and non-affected bowel walls in CD patients, as well as from bowel walls in patients without CD. Image quality and mural enhancement were evaluated at VMI energy levels at 40, 70, and 110 keV.

RESULTS

At all energy levels of VMI, each quantitative data for AV, SNR, and CNR showed statistically significant difference between diseased and non-diseased bowel walls in CD patients. In the quantitative assessment of patients with and without CD, the optimal AV and SNR were obtained at 40 keV, and the optimal CNR was obtained at 70 keV. For the qualitative assessment, the best image quality and mural enhancement were obtained at 70 keV and 40 keV, respectively.

CONCLUSION

VMI are helpful for the differentiation of affected bowel walls in CD patients, providing high diagnostic accuracy.

Dual-energy CT in diffuse liver disease: is there a role?

Dual-energy CT (DECT) can be defined as the use of two different energy levels to identify and quantify material composition. Since its inception, DECT has benefited from remarkable improvements in hardware and clinical applications. DECT enables accurate identification and quantification of multiple materials, including fat, iron, and iodine. As a consequence, multiple studies have investigated the potential role of DECT in the assessment of diffuse liver diseases. While this role is evolving, this article aims to review the most relevant literature on use of DECT for assessment of diffuse liver diseases. Moreover, the basic concepts on DECT techniques, types of image reconstruction, and DECT-dedicated software will be described, focusing on the areas that are most relevant for the evaluation of diffuse liver diseases. Also, we will review the evidence of added value of DECT in detection and assessment of hepatocellular carcinoma which is a known risk in patients with diffuse liver disease.

Evaluating online filtering algorithms to enhance dynamic multispectral optoacoustic tomography

Multispectral optoacoustic tomography (MSOT) is an emerging imaging modality, which is able to capture data at high spatiotemporal resolution using rapid tuning of the excitation laser wavelength. However, owing to the necessity of imaging one wavelength at a time to the exclusion of others, forming a complete multispectral image requires multiple excitations over time, which may introduce aliasing due to underlying spectral dynamics or noise in the data. In order to mitigate this limitation, we have applied kinematic α and α β filters to multispectral time series, providing an estimate of the underlying multispectral image at every point in time throughout data acquisition. We demonstrate the efficacy of these methods in suppressing the inter-frame noise present in dynamic multispectral image time courses using a multispectral Shepp-Logan phantom and mice bearing distinct renal cell carcinoma tumors. The gains in signal to noise ratio provided by these filters enable higher-fidelity downstream analysis such as spectral unmixing and improved hypothesis testing in quantifying the onset of signal changes during an oxygen gas challenge.

Protocol Optimization for Renal Mass Detection and Characterization

Renal masses increasingly are found incidentally, largely due to the frequent use of medical imaging. Computed tomography (CT) and MR imaging are mainstays for renal mass characterization, presurgical planning of renal tumors, and surveillance after surgery or systemic therapy for advanced renal cell carcinomas. CT protocols should be tailored to different clinical indications, balancing diagnostic accuracy and radiation exposure. MR imaging protocols should take advantage of the improved soft tissue contrast for renal tumor diagnosis and staging. Optimized imaging protocols enable analysis of imaging features that help narrow the differential diagnoses and guide management in patients with renal masses.

Effect of energy difference in the evaluation of calcification size and luminal diameter in calcified coronary artery plaque using spectral CT

PURPOSE

This study evaluated the calcium blooming-reducing effect and the differences of luminal diameter among various-energy virtual monochromatic images (VMIs) using rapid kilovolt-switching dual-energy computed tomography (DECT).

MATERIALS AND METHODS

Forty-five calcified segments in 31 patients were analyzed. For the analysis, 40- to 140-keV VMIs on both non-contrast CT and coronary CT angiography were generated at 10-keV steps, and calcification size and luminal diameter were measured using CT number profile curve and full-width at half-maximum method. We compared calcification size and luminal diameter on each keV VMIs with those on 70-keV VMI.

RESULTS

There was no significant differences among the 40- to 140-keV VMIs regarding calcification size or luminal diameter.

CONCLUSION

The 40- to 140-keV VMIs produced by single-source DECT had no effect on the calcification size or luminal diameter in the coronary artery.