Dual-energy CT Research Articles

Impact of virtual monochromatic images of different low-energy levels in dual-energy CT on radiomics models for predicting muscle invasion in bladder cancer.

The purpose of this study was to investigate the impact of different low-energy virtual monochromatic images (VMIs) in dual-energy CT on the performance of radiomics models for predicting muscle invasive status in bladder cancer (BCa). A total of 127 patients with pathologically proven muscle-invasive BCa (n = 49) and non-muscle-invasive BCa (n = 78) were randomly allocated into the training and test cohorts at a ratio of 7:3. Feature extraction was performed on the venous phase images reconstructed at 40, 50, 60 and 70-keV (single-energy analysis) or in combination (multi-energy analysis). Recursive feature elimination (RFE) and the least absolute shrinkage and selection operator(LASSO) were employed to select the most relevant features associated with BCa. Models were built using a support vector machine(SVM) classifier. Diagnostic performance was assessed through receiver operating characteristic curves, evaluating sensitivity, specificity, accuracy, precision, and the area-under-the curve (AUC) values. In the test cohort, the multi-energy model achieved the best diagnostic performance with AUC, sensitivity, specificity, accuracy, and precision of 0.917, 0.800, 0.833, 0.821, and 0.750, respectively. Conversely, the single-energy model exhibited lower AUC and sensitivity in predicting the muscle invasion status. By combining information from VMIs of various energies, the multi-energy model displays superior performance in preoperatively predicting the muscle invasion status of bladder cancer.

Diagnostic Accuracy of Dual-Energy CT for Bone Stress Injury of the Lower Limb.

Background Because of its ability to help assess the presence of subtle morphologic changes in bone and bone marrow edema, dual-energy CT (DECT) could be an alternative to MRI in the diagnosis of bone stress injury that includes a stress fracture (SF) and stress reaction (SR). Purpose To determine the diagnostic accuracy of DECT in identifying bone stress injury of the lower limb using MRI as the reference standard. Materials and Methods This prospective study, conducted between June 2021 and January 2024, included consecutive patients clinically suspected of having stress injury (SF or SR) of the lower limb (leg, ankle, or foot). Imaging diagnosis was based on the absence or presence of cortical or periosteal changes, bone marrow edema, or a fracture line. The diagnostic performance of four blinded independent readers was determined for the entire cohort and for the subset of participants without fracture lines. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was evaluated with Kendall coefficient of concordance (W). Results A total of 184 patients (mean age, 58 years ± 17 [SD]; 97 male) were enrolled. At MRI, 107 of 184 participants (58%) had positive diagnoses, including 70 with SF and 37 with SR. The mean overall sensitivity and specificity were 91% (390 of 428; 95% CI: 0.85, 0.95) and 93% (287 of 308; 95% CI: 0.87, 0.97), respectively, with an AUC of 0.94 (95% CI: 0.91, 0.97). Among patients without fracture lines (n = 114), the mean overall sensitivity and specificity of DECT were 79% (117 of 148; 95% CI: 0.65, 0.88) and 93% (287 of 308; 95% CI: 0.87, 0.97), respectively, with an AUC of 0.87 (95% CI: 0.81, 0.94). The interobserver agreement was very good for diagnosis of SF and SR combined (Kendall W = 0.90) and SR alone (Kendall W = 0.84). Conclusion DECT helped to accurately diagnose bone stress injury of the lower limb by identifying fracture lines and osseous stress reactions. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Breighner in this issue.

Quantitative evaluation of the effects of dual-energy CT acquisition, reconstruction and postprocessing parameters on virtual Non-Calcium (VNCa) images

PurposeThe appearance and associated interpretation of Virtual Non-Calcium (VNCa) images extracted from dual-energy CT (DECT) acquisitions are influenced by many parameters. This study aimed to investigate the effects of acquisition, reconstruction, and postprocessing parameters on VNCa images. Material and methodsA human cadaver leg was scanned using a dual-source DECT scanner, with variations in tube current, tube voltage, reconstruction kernels, and post-processing settings (resolution, upper threshold, lower threshold, beam-hardening correction). The impact of noise was investigated by scanning the specimen five times using the same standard acquisition, reconstruction and postprocessing parameters. VNCa values were measured in four different regions of interest within different bones. ResultsTube current and reconstruction kernels had no significant effect on VNCa values, with maximal standard deviations of 6.2 and 6.1 HU respectively. However, reducing the kVp difference between both tubes, reduced the spectral separation which resulted in lower VNCa values. For postprocessing parameters, variations in resolution and lower and upper thresholds as well as applying beam-hardening correction showed a large impact on VNCa values. ConclusionThe results of this study improve the understanding of the impact of certain CT parameters on VNCa images. Tube voltage and post-processing settings have a large impact on VNCa values. An inappropriate choice of threshold range, resolution and incorrect use of calcium beam-hardening correction can potentially lead to false positive findings of bone marrow edema. This can furthermore impact the ability to compare results from the literature and between institutions, which emphasizes the importance of optimizing and standardizing acquisition, reconstruction and postprocessing parameters for consistent VNCa imaging.

Assessment of photon-counting Computed Tomography for quantitative imaging in radiation therapy: PCCT for quantitative imaging in radiotherapy.

To test a first generation clinical PCCT scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes. A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear, Norfolk, USA) was scanned on a NAEOTOM Alpha photon-counting CT (PCCT) and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers, Forscheim, Germany). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LAC) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via 'Rho/Z'-algorithm was also utilized. Proton stopping-power ratios (SPR) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations (RMSD) within the material regions were evaluated for LAC, RED/EAN and SPR, respectively. Two example ray-projections were also examined for LAC, SPR and water-equivalent thickness (WET), for illustrations of a more treatment-like scenario. There was a tendency towards narrower distributions for PCCT compared to both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored RMSDs showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile whereas the more standard projection showed similar results between the modalities. This study shows promising results for tissue characterization in a human-like geometry for radiotherapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

A quantitative model using multi-parameters in dual-energy CT to preoperatively predict serosal invasion in locally advanced gastric cancer

ObjectivesTo develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).Materials and methodsA total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.ResultsA quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.ConclusionThe proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.Critical relevance statementThis quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.Key PointsSerosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT.DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages.This quantitative model was associated with patient postoperative disease-free survival.Graphical

The diagnosis value of dual-energy computed tomography (DECT) multi-parameter imaging in lung adenocarcinoma and squamous cell carcinoma.

Lung cancer continues to pose a serious risk to human health. With a high mortality rate, non-small cell lung cancer (NSCLC) is the major type of lung cancer, making up to 85% of all cases of lung cancer. Lung adenocarcinoma (AC), and lung squamous cell carcinoma (SC) are the two primary types of NSCLC. Determining the pathological type of NSCLC is important in establishing the most effective treatment method. Dual-energy computed tomography (DECT) multi-parameter imaging is an imaging technology that provides accurate and reliable disease diagnosis, and its uses are utilized for the combined diagnostic efficacy of AC and SC. The purpose of this study was to investigate the diagnostic value of spectral parameters of DECT in efficacy to AC and SC, and their combined diagnostic efficacy was also analyzed. We conducted a retrospective analysis of clinical and imaging data for 36 patients diagnosed with SC and 35 patients with AC. These patients underwent preoperative DECT chest scans, encompassing both arterial and venous phases, at our hospital from December 2020 to April 2022. The tumor diameter, water concentration (WC), iodine concentration (IC), normalized iodine concentration (NIC), Z effective (Zeff), and slope of the curve (K) in lesions were evaluated during two scanning phases in the two separate pathological types of lung cancers. The differences in parameters between these two types of lung cancers were statistically analyzed. In addition, receiver operating characteristic (ROC) curves were performed for these parameters to distinguish between SC and AC. In a univariate analysis involving 71 lung cancer patients, the results from Zeff, IC, NIC, and K from the AC's arterial and venous phase images were more elevated than those from the SC (P < 0.05). In contrast, the WC results were lower than those from SC (P < 0.05). The area under the ROC curve (AUC) for multi-parameter joint prediction typing was 0.831, with a corresponding sensitivity of 63.9% and specificity of 94.3%. It is possible to distinguish between central SC and AC using the spectrum characteristics of DECT-enhanced scanning (Zeff, IC, NIC, K, WC, and tumor diameter). Diagnostic effectiveness can be greatly improved when multiple variables are included.

Dual-energy computed tomography: pediatric considerations.

Multidetector computed tomography (CT) has revolutionized medicine and is now a fundamental aspect of modern radiology. Hardware and software advancements have significantly improved CT accessibility, image quality, and acquisition times. While considerable attention has been directed towards the potential risks of ionizing radiation from CT scans in children, recent concerns regarding the possible short- and long-term risks related to magnetic resonance imaging (MRI) conducted under general anesthesia have generated fresh interest in novel pediatric CT applications and techniques that allow imaging of awake patients at low radiation doses. Among these novel techniques, dual-energy CT (DECT) stands out for its ability to provide enhanced diagnostic information, reduce radiation doses further, and facilitate faster scans, making it a highly promising tool in pediatric radiology. This manuscript explores the current role of DECT in pediatric imaging, emphasizing its technical foundations, hardware configurations, and various reconstruction techniques. We discuss advanced post-processing techniques, such as material decomposition algorithms and virtual monoenergetic imaging, highlighting their clinical advantages in improving diagnostic accuracy and patient outcomes. Furthermore, the paper reviews the clinical applications of DECT in evaluating pulmonary perfusion, cardiovascular assessments, and oncologic imaging in pediatric patients.

Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

BackgroundTo explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).MethodsBetween July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong’s test.ResultsThis study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.ConclusionsThe combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Dual-energy CT angiography in detecting underlying causes of intracerebral hemorrhage: an observational cohort study.

CT angiography (CTA) is often used to detect underlying causes of acute intracerebral hemorrhage (ICH). Dual-energy CT (DECT) is able to distinguish materials with similar attenuation but different compositions, such as hemorrhage and contrast. We aimed to evaluate the diagnostic yield of DECT angiography (DECTA), compared to conventional CTA in detecting underlying ICH causes. All non-traumatic ICH patients who underwent DECTA (both arterial as well as delayed venous phase) at our center between January 2014 and February 2020 were analyzed. Conventional CTA acquisitions were reconstructed ('merged') from DECTA data. Structural ICH causes were assessed on both reconstructed conventional CTA and DECTA. The final diagnosis was based on all available diagnostic and clinical findings during one-year follow up. Of 206 included ICH patients, 30 (14.6%) had an underlying cause as final diagnosis. Conventional CTA showed a cause in 24 patients (11.7%), DECTA in 32 (15.5%). Both false positive and false negative findings occurred more frequently on conventional CTA. DECTA detected neoplastic ICH in all seven patients with a definite neoplastic ICH diagnosis, whereas conventional CTA only detected four of these cases. Both developmental venous anomalies (DVA) and cerebral venous sinus thrombosis (CVST) were more frequently seen on DECTA. Arteriovenous malformations and aneurysms were detected equally on both imaging modalities. Performing DECTA at clinical presentation of ICH may be of additional diagnostic value in the early detection of underlying causes, especially neoplasms, CVST and DVAs.

Computed Tomography and Magnetic Resonance Imaging in Liver Iron Overload: From Precise Quantification to Prognosis Assessment

Liver iron overload is associated with conditions such as hereditary hemochromatosis, thalassemia major, and chronic liver diseases. The liver-related outcomes, patient outcomes, and treatment recommendations of these patients differ depending on the cause and extent of iron overload. Accurate quantification of the liver iron concentration (LIC) is critical for effective patient management. This review focuses on the application of computed tomography (CT) and magnetic resonance imaging (MRI) for the precise quantification and prognostic assessment of liver iron overload. In recent years, the use of dual-energy CT and the emergence of MRI-based sequences (such as UTE, QSM, Dixon, and CSE technologies) have significantly increased the potential for noninvasive liver iron quantification. However, the establishment of internationally standardized imaging parameters, postprocessing procedures, and reporting protocols is urgently needed for better management of patients with liver iron overload.

Iodine-Based Dual-Energy Computed Tomography After Mechanical Thrombectomy Predicts Secondary Neurologic Decline from Cerebral Edema After Severe Stroke.

Patients with severe stroke are at high risk of secondary neurologic decline (ND) from the development of malignant cerebral edema (MCE). However, early infarcts are hard to diagnose on conventional head computed tomography (CT). We hypothesize that high-energy (190keV) virtual monochromatic imaging (VMI) from dual-energy CT (DECT) imaging enables earlier detection of ND from MCE. Consecutive patients with severe stroke with National Institute of Health Stroke Scale (NIHSS) scores > 15 and DECT within 10h of mechanical thrombectomy from May 2020 to March 2022 were included. We excluded patients with parenchymal hematoma type 2 transformation. Retrospective analysis of clinical and novel variables included the VMI Alberta Stroke Program Early CT Score (ASPECTS), total iodine content, and VMI infarct volume. The primary outcome was secondary ND, defined using a composite outcome variable of clinical worsening (increase in NIHSS score ≥ 4 or decrease in Glasgow Coma Scale score > 2) or malignant radiographical edema (midline shift ≥ 5mm at the level of the septum pellucidum). Fisher's exact test and Wilcoxon's test were used for univariate analysis. Logistic regression was used to develop prediction models for categorical outcomes. Eighty-four patients with severe stroke with a median age of 67.5 (interquartile range [IQR] 57-78) years and an NIHSS score of 22 (IQR 18-25) were included. Twenty-nine patients had ND. The VMI ASPECTS, total iodine content, and VMI infarct volume were associated with ND. The VMI ASPECTS, VMI infarct volume, and total iodine content were predictors of ND after adjusting for age, sex, initial NIHSS score, and tissue plasminogen activator administration, with areas under the receiver operating characteristic curve (AUROC) of 0.691 (95% confidence interval [CI] 0.572-0.810), 0.877 (95% CI 0.800-0.954), and 0.845 (95% CI 0.750-0.940). By including all three predictors, the model achieved an AUROC of 0.903 (95% CI 0.84-0.97) and was cross-validated by the leave one out method, with an AUROC of 0.827. The VMI ASPECTS and VMI infarct volume from DECT are superior to the conventional CT ASPECTS and are novel predictors for secondary ND due to MCE after severe stroke. Clinical trial registration ClinicalTrials.gov identifier: NCT04189471.

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

Dual-Energy CT as a Well-Established CT Modality to Reduce Contrast Media Amount: A Systematic Review from the Computed Tomography Subspecialty Section of the Italian Society of Radiology.

Background: Our study aims to provide an overview of existing evidence regarding the image quality of dual-energy CT (DECT) employing reduced contrast media (CM) volumes, in comparison to single-energy CT (SECT) with standard CM loads. The advantages, indications, and possible applications of DECT were investigated from the perspective of providing better patient care, minimizing CM volume and managing CM shortage. Methods: In this systematic review (PRISMA methodology), PubMed and WOS were searched from January 2010 to January 2023 by two independent reviewers. The scan and CM characteristics, radiation dose, and results of quantitative (contrast to noise ratio, CNR, and signal to noise ratio, SNR) and qualitative assessment of image quality were collected. Sixty non-duplicated records eligible for full-text screening were examined. Results: Finally, 22 articles (1818 patients) were included. The average CM reduction with DECT ranged between 43.4 ± 11%. Despite the wide variability in CT scan protocols, no differences were found in radiation doses between DECT and SECT. Conclusions: DECT scanners allow the employment of lower CM volumes with equal or better image quality evaluated by quantitative and qualitative analyses and similar dose radiation compared to SECT. Using image reconstructions at low monochromatic energy levels, DECT increases iodine conspicuity and attenuation contributing to CM containment measures.

Toward the Application of Dual-Energy Computed Tomography with Virtual Non-Hydroxyapatite Color-Coded Maps to Identify Traumatic Fractures in Daily Emergency Settings

To evaluate the advantages of dual-energy computed tomography (DECT) virtual non-hydroxyapatite color mapping (VNHAP) in combination with standard bone CT (BCT) in the identification of subtle or occult traumatic fractures referred to emergency and acceptance departments (DEAs). Forty patients (22 men; mean age 83 ± 23.7 y) with suspected traumatic fractures referred to our emergency department and examined with a fast kilovoltage-switching single-source spectral CT scan between January and October 2023 were retrospectively reviewed. The BCT and VNHAP images were blindly evaluated by two radiologists with &gt;10 years and &lt;2 years of experience in musculoskeletal imaging. Both techniques were evaluated in terms of sensitivity (SE), specificity (SP), positive and negative predictive values (PPVs and NPVs) and accuracy for fracture detection, as confirmed at a 3-month clinical–instrumental follow-up. Inter-observer agreement and examination times were also analyzed. Fractures were confirmed in 18/40 cases. The highest values of diagnostic performance for VNHAP images were obtained in terms of SP (90.9% and 95%) and PPV (87.5% and 92.8%) and for the less experienced operator. No statistically significant differences were observed between the diagnostic accuracy of the two readers in the evaluation of VNHAP images. Inter-observer agreement was moderate (κ = 0.536) for BCT and substantial (κ = 0.680) for VNHAP. Comparing the two operators, a significantly longer examination time for BCT and no significant difference for VNHAP were registered. Our preliminary experience may encourage the employment of VNHAP maps in combination with BCT images in emergency settings. Their use could be time-saving and valuable in terms of diagnostic performance, especially for less experienced operators.

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality. To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA. We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent). The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images. Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

Optimized versus conventional trigger threshold for pancreatic phase image acquisition using dual-energy CT at 40-keV: a randomized controlled trial.

To evaluate the impact of optimized trigger threshold on 40-keV pancreatic phase images acquired with a dual-energy CT (DECT) protocol. A cohort of 69 consecutive participants (median age, 72 years) undergoing a pancreatic protocol DECT examination between September to December 2021 were prospectively randomized into two protocols: conventional trigger threshold of 100 HU (Group A, n = 34) and optimized trigger threshold of 30 HU (Group B, n = 35). Pancreatic phase image acquisition was performed with fixed delay of 20s from the trigger threshold. Two radiologists assessed the 40-keV pancreatic phase images for scan timing adequacy using a binary scale (adequate or inadequate). The proportions of these classifications were compared in the two groups using the Fisher's test. The median times to achieve the aortic attenuation of 30 HU and 100 HU were 16.3s and 22.3s in Group A, respectively, and was 17.8s for 30 HU in Group B. The median time difference from 30 HU to 100 HU was 4.5s in Group A. The scan timing adequacies of pancreatic phase images were classified as adequate (50.0% and 74.3%) or inadequate (50.0% and 25.7%) in Group A and Group B (P = 0.049). An optimized trigger threshold of 30 HU allows consistent acquisition of adequate pancreatic phase images compared to the conventional trigger threshold of 100 HU for pancreatic protocol DECT at 40-keV which might lead to improved pancreatic lesion conspicuity.

Exploring dual energy CT synthesis in CBCT-based adaptive radiotherapy and proton therapy: application of denoising diffusion probabilistic models

Background.Adaptive radiotherapy (ART) requires precise tissue characterization to optimize treatment plans and enhance the efficacy of radiation delivery while minimizing exposure to organs at risk. Traditional imaging techniques such as cone beam computed tomography (CBCT) used in ART settings often lack the resolution and detail necessary for accurate dosimetry, especially in proton therapy.Purpose.This study aims to enhance ART by introducing an innovative approach that synthesizes dual-energy computed tomography (DECT) images from CBCT scans using a novel 3D conditional denoising diffusion probabilistic model (DDPM) multi-decoder. This method seeks to improve dose calculations in ART planning, enhancing tissue characterization.Methods.We utilized a paired CBCT-DECT dataset from 54 head and neck cancer patients to train and validate our DDPM model. The model employs a multi-decoder Swin-UNET architecture that synthesizes high-resolution DECT images by progressively reducing noise and artifacts in CBCT scans through a controlled diffusion process.Results.The proposed method demonstrated superior performance in synthesizing DECT images (High DECT MAE 39.582 ± 0.855 and Low DECT MAE 48.540± 1.833) with significantly enhanced signal-to-noise ratio and reduced artifacts compared to traditional GAN-based methods. It showed marked improvements in tissue characterization and anatomical structure similarity, critical for precise proton and radiation therapy planning.Conclusions.This research has opened a new avenue in CBCT-CT synthesis for ART/APT by generating DECT images using an enhanced DDPM approach. The demonstrated similarity between the synthesized DECT images and ground truth images suggests that these synthetic volumes can be used for accurate dose calculations, leading to better adaptation in treatment planning.

Dual-energy computed tomography for predicting histological grading and survival in patients with pancreatic ductal adenocarcinoma.

We evaluated the value of dual-energy computed tomography (DECT) parameters derived from pancreatic ductal adenocarcinoma (PDAC) to discriminate between high- and low-grade tumors and predict overall survival (OS) in patients. Data were retrospectively collected from 169 consecutive patients with pathologically confirmed PDAC who underwent third-generation dual-source DECT enhanced dual-phase scanning before surgery between January 2017 and March 2023. Patients with prior treatments, other malignancies, small tumors, or poor-quality scans were excluded. Two radiologists evaluated three clinical and seven radiological features and measured sixteen DECT-derived parameters. Univariate and multivariate analyses were applied to select independent predictors. A prediction model and a corresponding nomogram were developed, and the area under the curve (AUC), calibration, and clinical applicability were assessed. The correlations between factors and OS were evaluated using Kaplan-Meier survival and Cox regression analyses. One hundred sixty-nine patients were randomly divided into training (n = 118) and validation (n = 51) cohorts, among which 43 (36.4%) and 19 (37.3%) had high-grade PDAC confirmed by pathology, respectively. The vascular invasion, normalized iodine concentration in the venous phase, and effective atomic number in the venous phase were independent predictors for histological grading. A nomogram was constructed to predict the risk of high-grade tumors in PDAC, with AUCs of 0.887 and 0.844 in the training and validation cohorts, respectively. The nomogram exhibited good calibration and was more beneficial than a single parameter in both cohorts. Pathological- and nomoscore-predicted high-grade PDACs were associated with poor OS (all p < 0.05). The nomogram, which combines DECT parameters and radiological features, can predict the histological grade and OS in patients with PDAC before surgery. Question Preoperative determination of histological grade in PDAC is crucial for guiding treatment, yet current methods are invasive and limited. Findings A DECT-based nomogram combining vascular invasion, normalized iodine concentration, and effective atomic number accurately predicts histological grade and OS in PDAC patients. Clinical relevance The DECT-based nomogram is a reliable, non-invasive tool for predicting histological grade and OS in PDAC. It provides essential information to guide personalized treatment strategies, potentially improving patient management and outcomes.

Dual-energy CT late arterial phase iodine maps for the diagnosis of acute non-occlusive mesenteric ischemia.

To evaluate the diagnostic accuracy of dual-energy CT (DECT) iodine maps in comparison to conventional CT series for the assessment of non-occlusive mesenteric ischemia (NOMI). We evaluated data from 142 patients (72 men; 50.7%) who underwent DECT between 2018 and 2022, with surgically confirmed diagnosis of NOMI. One board-certified radiologist performed region of interest (ROI) measurements in bowel segments on late arterial (LA) and portal venous (PV) phase DECT iodine maps as well as LA conventional series, in both ischemic and non-ischemic bowel loops, using surgical reports as reference standard, and in a control group of 97 patients. Intra- and inter-reader agreement with a second board-certified radiologist was also evaluated. Receiver operating characteristic (ROC) curve analysis was performed to calculate the optimal threshold for discriminating ischemic from non-ischemic bowel segments. Subjective image rating of LA and PV iodine maps was performed. DECT-based iodine concentration (IC) measurements showed significant differences in LA phase iodine maps between ischemic (median:0.72; IQR 0.52-0.91mg/mL) and non-ischemic bowel loops (5.16; IQR 3.45-6.31mg/ml) (P <.0001). IC quantification on LA phase revealed an AUC of 0.966 for the assessment of acute bowel ischemia, significantly higher compared to both IC quantification based on PV phase (0.951) and attenuation values evaluated on LA conventional CT series (0.828). Excellent intra-observer and strong inter-observer agreements were observed for the quantification of iodine concentration. Conversely, weak inter-observer agreement was noted for conventional HU assessments. The optimal LA phase-based IC threshold for assessing bowel ischemia was 1.34mg/mL, yielding a sensitivity of 100% and specificity of 96.48%. Iodine maps based on LA phase significantly improve the diagnostic accuracy for the assessment of NOMI compared to conventional CT series and PV phase iodine maps.

Dual Energy CT for Deep Learning-Based Segmentation and Volumetric Estimation of Early Ischemic Infarcts.

Ischemic changes are not visible on non-contrast head CT until several hours after infarction, though deep convolutional neural networks have shown promise in the detection of subtle imaging findings. This study aims to assess if dual-energy CT (DECT) acquisition can improve early infarct visibility for machine learning. The retrospective dataset consisted of 330 DECTs acquired up to 48h prior to confirmation of a DWI positive infarct on MRI between 2016 and 2022. Infarct segmentation maps were generated from the MRI and co-registered to the CT to serve as ground truth for segmentation. A self-configuring 3D nnU-Net was trained for segmentation on (1) standard 120kV mixed-images (2) 190keV virtual monochromatic images and (3) 120kV + 190keV images as dual channel inputs. Algorithm performance was assessed with Dice scores with paired t-tests on a test set. Global aggregate Dice scores were 0.616, 0.645, and 0.665 for standard 120kV images, 190keV, and combined channel inputs respectively. Differences in overall Dice scores were statistically significant with highest performance for combined channel inputs (p < 0.01). Small but statistically significant differences were observed for infarcts between 6 and 12h from last-known-well with higher performance for larger infarcts. Volumetric accuracy trended higher with combined inputs but differences were not statistically significant (p = 0.07). Supplementation of standard head CT images with dual-energy data provides earlier and more accurate segmentation of infarcts for machine learning particularly between 6 and 12h after last-known-well.