Dual-energy CT Imaging Research Articles

Unveiling Uric Acid in Tendons: Dual-Energy Computed Tomography Imaging Insight.

Unveiling Uric Acid in Tendons: Dual-Energy Computed Tomography Imaging Insight.

Image-based finite element model stiffness and vBMD by single and dual energy CT reconstruction kernel

Single-energy quantitative computed tomography (SEQCT) provides volumetric bone mineral density (vBMD) measures for bone analysis and input to image-based finite element models (FEMs). Dual-energy CT (DECT) improves vBMD by accounting for voxel-specific material variations utilizing scans at multiple x-ray energies. vBMD is also altered by reconstruction kernel that cannot be accounted for using calibration phantoms. This study compared vBMD and FEM stiffness derived from SEQCT and DECT images reconstructed with two common kernels. SEQCT and DECT images of cadaveric shoulders (n = 10) were collected using standard (STD) and boneplus (BONE) kernels. Hounsfield Units were converted to vBMD using specimen-specific calibrations. DECT STD and BONE images were generated using an established material decomposition method with 40 and 90 keV simulated monochromatic images. A proximal humerus bone section below the anatomic neck was used for vBMD analysis and FEM generation. FEMs were loaded to 1 % apparent strain for stiffness measurements.Between STD and BONE kernel images, average vBMD differed 0.9 mgK2HPO4/cc and 4.1 mg K2HPO4/cc, in SEQCT and DECT images, respectively. Significant differences occurred in DECT images (p = 0.001). BONE reconstructed images produced higher vBMD measures across both SEQCT and DECT images. The difference between STD and BONE in both SEQCT- and DECT-based FEMs persisted, with larger estimated stiffness in BONE models. For six of the models DECT-based had higher stiffness than SEQCT-based models using the same kernel, although these models differed between STD and BONE kernels. Differences in stiffness between STD and BONE derived models were similar across image types (DECT: 17.5 kN/mm; SEQCT: 19.0 kN/mm). Stiffness values were significantly different within SECT kernels and between SEQCT BONE and DECT STD models. This study shows important differences in vBMD and FEM stiffness that occur due to CT-based imaging parameters alone. These results indicate that consistent imaging parameters should be used for vBMD analysis and FEM input to avoid systematic measurement errors.

Enhancing Radiomics Reproducibility: Deep Learning-Based Harmonization of Abdominal Computed Tomography (CT) Images

We assessed the feasibility of using deep learning-based image harmonization to improve the reproducibility of radiomics features in abdominal CT scans. In CT imaging, harmonization adjusts images from different institutions to ensure consistency despite variations in scanners and acquisition protocols. This process is essential because such differences can lead to variability in radiomics features, affecting reproducibility and accuracy. Harmonizing images minimizes these inconsistencies, supporting more reliable and clinically applicable results across diverse settings. A pre-trained harmonization algorithm was applied to 63 dual-energy abdominal CT images, which were reconstructed into four different types, and 10 regions of interest (ROIs) were analyzed. From the original 455 radiomics features per ROI, 387 were used after excluding redundant features. Reproducibility was measured using the intraclass correlation coefficient (ICC), with a threshold of ICC ≥ 0.85 indicating acceptable reproducibility. The region-based analysis revealed significant improvements in reproducibility post-harmonization, especially in vessel features, which increased from 14% to 69%. Other regions, including the spleen, kidney, muscle, and liver parenchyma, also saw notable improvements, although air reproducibility slightly decreased from 95% to 94%, impacting only a few features. In patient-based analysis, reproducible features increased from 18% to 65%, with an average of 179 additional reproducible features per patient after harmonization. These results demonstrate that deep learning-based harmonization can significantly enhance the reproducibility of radiomics features in abdominal CT, offering promising potential for advancing radiomics development and its clinical applications.

Application and Optimization of a Fast Non-Local Means Noise Reduction Algorithm in Pediatric Abdominal Virtual Monoenergetic Images

In this study, we applied and optimized a fast non-local means (FNLM) algorithm to reduce noise in pediatric abdominal virtual monoenergetic images (VMIs). To analyze various contrast agent concentrations, we produced contrast agent concentration samples (20, 40, 60, 80, and 100%) and inserted them into a phantom model of a one-year-old pediatric patient. Single-energy computed tomography (SECT) and dual-energy computed tomography (DECT) images were acquired from the phantom, and 40 kilo-electron-volt (keV) VMI was acquired based on the DECT images. For the 40 keV VMI, the smoothing factor of the FNLM algorithm was applied from 0.01 to 1.00 in increments of 0.01. We derived the optimized value of the FNLM algorithm based on quantitative evaluation and performed a comparative assessment with SECT, DECT, and a total variation (TV) algorithm. As a result of the analysis, we found that the average contrast to noise ratio (CNR) and coefficient of variation (COV) of each concentration were most improved at a smoothing factor of 0.02. Based on these results, we derived the optimized smoothing factor value of 0.02. Comparative evaluation shows that the optimized FNLM algorithm improves the CNR and COV results by approximately 3.14 and 2.45 times, respectively, compared with the DECT image, and the normalized noise power spectrum result shows a 10−1 mm2 improvement. The main contribution of this study is to demonstrate the effectiveness of an optimized FNLM algorithm in reducing noise in pediatric abdominal VMI, allowing high-quality images to be acquired while reducing contrast dose. This advancement has significant implications for minimizing the risk of contrast-induced toxicity, especially in pediatric patients. Our approach addresses the problem of limited datasets in pediatric imaging by providing a computationally efficient noise reduction technique and highlights the clinical applicability of the FNLM algorithm. In addition, effective noise reduction enables high-contrast imaging with minimal radiation and contrast exposure, which is expected to be suitable for repeat CT examinations of pediatric liver cancer patients and other abdominal diseases.

Dual-energy CT of acute bowel ischemia-influence on diagnostic accuracy and reader confidence.

This study evaluates the advantages in diagnostic accuracy, confidence, and reading time of additional dual-energy CT-derived reconstructions for assessing acute bowel ischemia. This retrospective study includes 25 patients with surgically proven acute bowel ischemia and 25 gender- and age-matched controls who underwent biphasic abdominal dual-energy CT. Two fellowship-trained abdominal radiologists and two residents evaluated all cases with and without additional dual-energy CT-derived iodine maps and virtual non-contrast images. Diagnostic confidence was rated on a 10-point Likert scale. Reading time was recorded. The inter-reader agreement was assessed using Fleiss' kappa. Sensitivity and specificity were compared using McNemar's test, reader confidence, and reading times with the Wilcoxon signed-rank test. For conventional images alone, the inter-reader agreement was moderate (κ = 0.58), with a sensitivity of 77% (95% CI: 67.5-84.8%) and specificity of 90% (95% CI: 82.4-95.1%). Adding dual-energy CT images, inter-reader agreement increased to substantial (κ = 0.69), sensitivity increased significantly to 89% (95% CI: 81.2-94.4%, p = 0.02), while specificity increased non-significantly to 93% (95% CI: 86.1-97.1%, p = 0.51). Diagnostic confidence increased significantly from 8 (IQR: 6-8) to 9 (IQR: 8-9) (p < 0.01). Equivalent diagnostic accuracy and confidence increases were observed for fellowship-trained and resident radiologists. A non-significant increase in mean reading time per case from 196 s to 201 s was observed (p = 0.30). Additional dual-energy CT reconstructions increase diagnostic accuracy and confidence without increasing reading time when evaluating suspected acute bowel ischemia. Both experienced and resident readers benefit from dual-energy CT images. Question There are too few clinical studies assessing the diagnostic accuracy of dual-energy CT (DECT) to recommend its use for evaluating suspected acute bowel ischemia. Findings Adding DECT-derived iodine maps and virtual-non-contrast images increase reader sensitivity and confidence while maintaining high specificity when evaluating for acute mesenteric ischemia. Clinical relevance Dual-energy CT should be used to investigate suspected acute bowel ischemia. Both diagnostic accuracy and confidence can be increased independent of reader experience without significantly increasing reading time.

Dual virtual non-contrast imaging: a Bayesian quantitative approach to determine radiotherapy quantities from contrast-enhanced DECT images.

Contrast agents in CT scans can compromise the accuracy of dose calculations in radiation therapy planning, especially for particle therapy. This often requires an additional non-contrast CT scan, increasing radiation exposure and introducing potential registration errors. Our goal is to resolve these issues by accurately estimating radiotherapy parameters from dual virtual non-contrast (dual-VNC) images generated by contrast-enhanced dual-energy CT (DECT) scans, while accounting for noise and variability in tissue composition.&#xD;Approach: A new Bayesian model is introduced to estimate dual-VNC Hounsfield units from contrast-enhanced DECT data. The model defines a prior distribution that describes tissue variations in terms of elemental compositions and mass densities. Multiple reference tissues are used to estimate variations across human tissues. A likelihood distribution is also defined to model the noise contained in CT data. The model is thoroughly validated in a simulated environment including 12 virtual patients under low and high iodine uptake scenarios, while incorporating noise and beam hardening effects. The eigentissue decomposition (ETD) technique is used to derive elemental compositions and parameters critical for radiotherapy from the dual-VNC images, such as electron density (ρe), particle stopping power (SPR), and photon energy absorption coefficient (EAC)&#xD;Main results: The proposed method yields accurate voxelwise estimations for ρe, SPR, and EAC, with root mean square errors of 3.09%, 3.14%, and 1.34% for highly-enhanced tissues, compared to 5.93%, 6.39%, and 17.11% when the presence of contrast agent is ignored. It also demonstrates robustness to systematic shifts in tissue composition and bandwidth variations in the prior distribution, resulting in overall uncertainties down to 1.13%, 1.33%, and 0.86% for ρe, SPR, and EAC in soft tissues; 1.17%, 1.32%, and 1.34% in enhanced soft tissues; and 4.34%, 4.00%, and 2.50% in bone.&#xD;Significance: The proposed method accurately derives radiotherapy parameters from contrast-enhanced DECT data and demonstrates robustness against systematic errors in reference data, highlighting its potential for clinical use.

Advancing precision in CT-guided bone biopsies: exploring the potential of dual-energy CT imaging.

This study aimed to investigate the integration of dual-energy CT (DECT) into CT-guided bone biopsy procedures, comparing it with conventional CT techniques. The focus was on technical aspects, accuracy and radiation dose exposure. A total of 51 bone biopsies were conducted, with 36 using conventional CT and 15 utilizing DECT. Patient data, lesion characteristics and biopsy techniques were analyzed. Statistical analyses, including Fisher's exact test and independent samples t-test, were performed to compare accuracy and radiation doses between the two methods. DECT-guided biopsies demonstrated a significantly higher accuracy (93.33%) compared to conventional CT biopsies (86.11%). The radiation dose exposure for DECT was comparable to conventional CT. DECT's ability to differentiate tissues, especially in bone marrow edema detection, led to higher precision. Integrating DECT into CT-guided bone biopsies enhances tissue differentiation and accuracy without significantly increasing radiation exposure. This advancement holds promise for improving musculoskeletal interventional radiology, leading to more precise diagnoses, informed treatment decisions and improved patient outcomes.

X-Ray Interaction Mechanisms in Medical Imaging: A Critical Exploration

X-ray interactions with matter are fundamental to the success of medical imaging, influencing image quality, diagnostic accuracy, and patient safety. This study critically explores the primary interaction mechanisms—photoelectric absorption, Compton scattering, and coherent scattering—and their implications in various medical imaging modalities. By analyzing their effects on image resolution, contrast, and radiation dose, the study highlights the strengths and limitations of each interaction mechanism. The findings underscore the role of photoelectric absorption in high-contrast imaging, the challenges posed by Compton scattering in reducing noise, and the minimal clinical significance of coherent scattering. Emphasis is placed on optimizing imaging parameters and adopting advanced technologies such as dual-energy CT and AI-enhanced imaging to balance diagnostic efficacy with radiation safety. This exploration provides valuable insights into the interplay of X-ray physics and medical imaging, paving the way for enhanced diagnostic practices and future innovations.

MMD-Net: Image domain multi-material decomposition network for dual-energy CT imaging.

Multi-material decomposition is an interesting topic in dual-energy CT (DECT) imaging; however, the accuracy and performance may be limited using the conventional algorithms. In this work, a novel multi-material decomposition network (MMD-Net) is proposed to improve the multi-material decomposition performance of DECTimaging. To achieve dual-energy multi-material decomposition, a deep neural network, named as MMD-Net, is proposed in this work. In MMD-Net, two specific convolutional neural network modules, Net-I and Net-II, are developed. Specifically, Net-I is used to distinguish the material triangles, while Net-II predicts the effective attenuation coefficients corresponding to the vertices of the material triangles. Subsequently, the material-specific density maps are calculated analytically through matrix inversion. The new method is validated using in-house benchtop DECT imaging experiments with a solution phantom and a pig leg specimen, as well as commercial medical DECT imaging experiments with a human patient. The decomposition accuracy, edge spreading function, and noise power spectrum are quantitativelyevaluated. Compared to the conventional multiple material decomposition (MMD) algorithm, the proposed MMD-Net method is more effective at suppressing image noise. Additionally, MMD-Net outperforms the iterative MMD approach in maintaining decomposition accuracy, image sharpness, and high-frequency content. Consequently, MMD-Net is capable of generating high-quality material decompositionimages. A high performance multi-material decomposition network is developed for dual-energy CTimaging.

Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient study results.

Dual-energy (DE) CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging. However, this increases radiation dose and may require a hardware upgrade due to the added second x-ray CT scan. The recently proposed PET-enabled DECT method allows dual-energy imaging using a conventional PET/CT scanner without the need to change scanner hardware or increase radiation exposure. Here we demonstrate the first-time physical phantom and patient data evaluation of this method. The PET-enabled DECT method reconstructs a gamma-ray CT (gCT) image at 511keV from the time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and then combines this image with the low-energy x-ray CT images to form a dual-energy image pair for material decomposition. To improve the image quality of gCT, a kernel MLAA method was developed using the x-ray CT as a priori information. Here we developed a general open-source implementation for gCT reconstruction and used this implementation for the first real data validation using both physical phantom study and human-subject study. Results from PET-enabled DECT were compared using x-ray DECT as the reference. Further, we applied the PET-enabled DECT method in another patient study to evaluate bone lesions. Compared to the standard MLAA, results from the kernel MLAA showed significantly improved image quality. PET-enabled DECT with the kernel MLAA was able to generate fractional images that were comparable to the x-ray DECT, with high correlation coefficients for both the phantom study and human subject study (R > 0.99). The application study also indicates that PET-enabled DECT has potential to characterize bone lesions. Results from this study have demonstrated the feasibility of this PET-enabled method for CT imaging and material decomposition. PET-enabled DECT shows promise to provide comparable results to x-ray DECT.

In situ isotropic 3D imaging of vasculature perfusion specimens using x-ray microscopic dual-energy CT.

Ex vivo x-ray angiography provides high-resolution, three-dimensional information on vascular phenotypes down to the level of capillaries. Sample preparation for ex vivo angiography starts with the removal of blood from the vascular system, followed by perfusion with an x-ray dense contrast agent mixed with a carrier such as gelatine or a polymer. Subsequently, the vascular micro-architecture of harvested organs is imaged in the intact fixed organ. In the present study, we present novel microscopic dual-energy CT (microDECT) imaging protocols that allow to visualise and analyse microvasculature in situ with reference to the morphology of hard and soft tissue. We show that the spectral contrast of µAngiofil and Micropaque barium sulphate in perfused specimens allows for the effective separation of vasculature from mineralised skeletal tissues. Furthermore, we demonstrate the counterstaining of perfused specimens using established x-ray dense contrast agents to depict blood vessels together with the morphology of soft tissue. Phosphotungstic acid (PTA) is used as a counterstain that shows excellent spectral contrast in both µAngiofil and Micropaque barium sulphate-perfused specimens. A novel Sorensen-buffered PTA protocol is introduced as a counterstain for µAngiofil specimens, as the polyurethane polymer is susceptible to artefacts when using conventional staining solutions. Finally, we demonstrate that counterstained samples can be automatically processed into three separate image channels (skeletal tissue, vasculature and stained soft tissue), which offers multiple new options for data analysis. The presented microDECT workflows are suited as tools to screen and quantify microvasculature and can be implemented in various correlative imaging pipelines to target regions of interest for downstream light microscopic investigation.

The diagnostic performance of dual-energy CT imaging in cervical lymph node metastasis of papillary thyroid cancer: a meta-analysis.

This meta-analysis aimed to evaluate the diagnostic efficacy of dual-energy computed tomography (DECT) in detecting cervical lymph node metastasis among papillary thyroid cancer (PTC) patients. A comprehensive search across PubMed, Embase, and Web of Science databases was conducted to identify pertinent publications up to May 2024. This search focused on studies examining the diagnostic accuracy of DECT in detecting cervical lymph node metastases in PTC patients. We employed a bivariate random-effects model to calculate pooled sensitivity and specificity of DECT. The degree of heterogeneity in the studies was quantified using the I 2 statistic. Furthermore, the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was utilized to evaluate the methodological quality of the included studies. This meta-analysis encompassed 14 articles, collectively involving 1,615 patients. The pooled sensitivity, specificity, and AUC for DECT in detecting cervical lymph node metastases in PTC patients were 0.81 (95% CI: 0.76-0.85), 0.86 (95% CI: 0.80-0.91), and 0.89 (95% CI: 0.86-0.92), respectively. According to Fagan's nomogram, for DECT, with a pre-test probability of 50%, the post-test probability was calculated as 85% for a positive result and 18% for a negative result. Deeks' funnel plot asymmetry test showed no significant publication bias was observed for DECT (p = 0.28). Our meta-analysis indicates that DECT demonstrates superior sensitivity and specificity in cervical lymph node metastasis among PTC patients. To corroborate these findings and evaluate their clinical applicability, further prospective studies are necessary.

Spondylodiscitis of the thoraco-lumbar spine: diagnostic performance of dual-energy CT vs MRI.

Dual-energy computed tomography (DECT) could combine the high-resolution bone window images made available by multi-detector CT technology with its capability to identify bone marrow edema (BME) in the spine, for diagnosing spondylodiscitis. Our objective was to compare the diagnostic performance of contrast-enhanced MRI and non-contrast DECT to identify spondylodiscitis of the thoraco-lumbar spine. This prospective study included 77 consecutive participants (39 males; mean age of 61) who underwent DECT and MRI (within 7 days) between January 2020 and October 2023. DECT data were post-processed on a dedicated offline workstation (SyngoVia® VB20) by using a three-material decomposition algorithm. Four radiologists, blinded to clinical data, evaluated non-contrast DECT and contrast-enhanced MRI images. The diagnosis of spondylodiscitis was based on vertebral edema, disc edema, endplate erosions, and paraspinal involvement. Diagnostic accuracy values were calculated by using biopsy as a standard of reference. A multi-reader multi-case analysis was performed. Biopsy revealed a diagnosis of spondylodiscitis in 46 patients (60%). Thoracic and lumbar spondylodiscitis were diagnosed in 37/46 (80%) and 9/46 (20%) patients, respectively. DECT and MRI overall sensitivity, specificity, and AUC were 0.91, 0.89, and 0.90, and 0.94, 0.93, and 0.93, respectively. At lumbar and thoracic levels, the difference between AUC values between DECT and MRI was not significant (p = 0.15). For DECT and MRI, a very good inter-reader agreement was achieved (k = 0.90 and k = 0.97, respectively). Contrast-enhanced MRI represents the most accurate imaging tool for the diagnosis of spondylodiscitis. However, only a non-significant drop in diagnostic performance was achieved by evaluating non-contrast DECT images. Question To compare the diagnostic performance of contrast-enhanced MRI and non-contrast DECT for identifying spondylodiscitis of the thoraco-lumbar spine. Findings MRI was not significantly superior compared to DECT in diagnosing spondylodiscitis, whereas the inter-reader agreement was near perfect for both MRI and DECT. Clinical relevance DECT represents a fast and accurate imaging tool for the demonstration of BME, erosions, and peri-vertebral inflammation in thoraco-lumbar spondylodiscitis.

Short-term treatment response assessment in non-surgical treatment of advanced non-small cell lung cancer based on radiomics of dual-energy CT

PurposeTo build and evaluate a pre-treatment dual-energy CT(DECT)-based clinical-radiomics nomogram for individualized prediction of short-term treatment response to non-surgical treatment in advanced non-small cell lung cancer (NSCLC). MethodsPre-treatment DECT images were retrospectively collected from 98 pathologically confirmed NSCLC with clinical stage III or IV. Short-term treatment response was determined with follow-up CT of 4–6 courses of treatment. Quantitative radiomics metrics of the lesion were extracted from dual-energy mixed images at venous phase. Least absolute shrinkage and selection operator and correlation analysis were used to select the most relevant radiomics features. Radiomics model, clinical model and clinical-radiomics model were established by multivariate logistic regression. The model with the best prediction performance was visualized as a nomogram, and the consistency between the probability of the actual occurrence of the outcome and the probability predicted by the model was measured by calibration curves. ResultsClinical stage, difference in electron density in arteriovenous phase, difference in slope of energy spectrum in arteriovenous phase, and slope of energy spectrum in venous phase of the tumor were significant clinical predictors of therapy response (P < 0.05). The clinical-radiomics model showed a higher predictive capability (AUC: 0.87 and 0.85 in training and validation sets, respectively) than the radiomics models and the clinical model. The clinical-radiomics nomogram integrating the DECT radiomics signature with clinical stage and spectrum parameters showed good calibration and discrimination. ConclusionThe clinical-radiomics nomogram based on pre-treatment DECT showed good performance in predicting clinical response to non-surgical therapy in NSCLC.

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.

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.

Head and neck automatic multi-organ segmentation on Dual-Energy Computed Tomography

Background and purposeDeep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT. Material and methodsDifferent types of DECT images from seventy four head-and-neck (HN) patients were retrieved, including polyenergetic images at different voltages [80 kV reconstructed with a kernel corresponding to the commercial algorithm DirectDensity™ (PEI80-DD), 80 kV (PEI80), 120 kV-mixed (PEI120)] and a virtual-monoenergetic image at 40 keV (VMI40). Delineations used for treatment planning were considered as ground truth (GT) and were compared with the auto-segmentations performed on the 4 DECT images. A blinded qualitative evaluation of 3 structures (thyroid, left parotid, left nodes level II) was carried out. Performance metrics were calculated for thirteen HN structures to evaluate the auto-contours including dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and mean surface distance (MSD). ResultsWe observed a high rate of low scores for PEI80-DD and VMI40 auto-segmentations on the thyroid and for GT and VMI40 contours on the nodes level II. All images received excellent scores for the parotid glands. The metrics comparison between GT and auto-segmented contours revealed that PEI80-DD had the highest DSC scores, significantly outperforming other reconstructed images for all organs (p < 0.05). ConclusionsThe results indicate that the auto-contouring system cannot generalize to images derived from DECT acquisition. It is therefore crucial to identify which organs benefit from these acquisitions to adapt the training datasets accordingly.

Benefits of Brain Dual-Energy CT Imaging in Detecting Intracranial Hemorrhage in Non-Contrast Brain CT Scans

Benefits of Brain Dual-Energy CT Imaging in Detecting Intracranial Hemorrhage in Non-Contrast Brain CT Scans

Assessing muscle invasion in bladder cancer via virtual biopsy: a study on quantitative parameters and classical radiomics features from dual-energy CT imaging

ObjectiveTo evaluate the prediction value of Dual-energy CT (DECT)-based quantitative parameters and radiomics model in preoperatively predicting muscle invasion in bladder cancer (BCa).Materials and methodsA retrospective study was performed on 126 patients with BCa who underwent DECT urography (DECTU) in our hospital. Patients were randomly divided into training and test cohorts with a ratio of 7:3. Quantitative parameters derived from DECTU were identified through univariate and multivariate logistic regression analysis to construct a DECT model. Radiomics features were extracted from the 40, 70, 100 keV and iodine-based material-decomposition (IMD) images in the venous phase to construct radiomics models from individual and combined images using a support vector machine classifier, and the optimal performing model was chosen as the final radiomics model. Subsequently, a fusion model combining the DECT parameters and the radiomics model was established. The diagnostic performances of all three models were evaluated through receiver operating characteristic (ROC) curves and the clinical usefulness was estimated using decision curve analysis (DCA).ResultsThe normalized iodine concentration (NIC) in DECT was an independent factor in diagnosing muscle invasion of BCa. The optimal multi-image radiomics model had predictive performance with an area-under-the-curve (AUC) of 0.867 in the test cohort, better than the AUC = 0.704 with NIC. The fusion model showed an increased level of performance, although the difference in AUC (0.893) was not statistically significant. Additionally, it demonstrated superior performance in DCA. For lesions smaller than 3 cm, the fusion model showed a high predictive capability, achieving an AUC value of 0.911. There was a slight improvement in model performance, although the difference was not statistically significant. This improvement was observed when comparing the AUC values of the DECT and radiomics models, which were 0.726 and 0.884, respectively.ConclusionThe proposed fusion model combing NIC and the optimal multi-image radiomics model in DECT showed good diagnostic capability in predicting muscle invasiveness of BCa.

Demonstrating the Efficacy of Dual Energy Computer Tomography with Gemstone Spectral Imaging Software to Determine Mixed and Single Composition ex vivo Urolithiasis.

To assess the capability of determining the mixed chemical composition of urinary stones using spectral imaging properties of Dual Energy Computed Tomography (DECT) Gemstone Spectral Imaging (GSI) software. Twenty-six single and 24 mixed composition ex vivo urinary stones with known chemical composition determined by Fourier-transform infrared spectroscopy (FTIR) prior to this project were scanned with DECT imaging and GSI in vitro. The major components of the stones included Uric Acid (UA), Calcium Oxalate (CaOx), Calcium Phosphate (CaP), Magnesium Ammonium Phosphate (MAP), and Cystine (Cys). A histogram to display the distribution of the effective atomic number (Z-eff) of each pixel of the tested area, spectral curve (40-140 keV, with 10 keV interval) and Hounsfield Units (HU) of each stone scanned was provided with analysis of monochromatic images at 140 keV in the axial plane. The overall pooled sensitivity, specificity, and accuracy of DECT for identifying major stone composition were 0.802, 0.831, and 0.807, respectively, with a 95% confidence interval. Accuracy was 100% for identifying UA and Cys stones. DECT is a superior imaging modality when compared to low dose computed tomography kidney ureter bladder scans. It allows for improved characterization of major components of urinary stones, in an accurate, non-invasive approach to pre-treatment. This can translate to urologists having greater confidence in determining patient suitability for medical or surgical management of their renal stones, in clinical practice.