Single-energy CT Research Articles

Dual-energy computed tomography for improved contrast on a polyphase graphitic ore

Proper ore characterisation is essential for understanding ore deposits and developing efficient mineral processing flow sheets. Conventional mineralogical and chemical techniques are usually used to study ores, but they can be destructive and, in some cases, provide only 2D information. Computed tomography (CT) is an emerging technology in the raw materials sector enabling the non-destructive 3D analysis of the ore mineralogy and microstructure. However, single-energy CT (SECT) has some limitations concerning the accurate imaging and differentiation of polyphase geomaterials comprising a broad range of attenuation properties. By contrast, dual-energy CT (DECT) uses two different X-ray energies to acquire data, which can be used to distinguish between materials with similar attenuation properties. This study explored the application of DECT for the analysis of a polyphase graphitic ore. A sequential fusion approach was utilized to combine data obtained from different X-ray energy scans at high spatial resolution, and varying weighting factors were applied to determine the optimal contribution of each energy level and spectrum. Both, SECT and DECT datasets were quantitatively evaluated based on the contrast-to-noise-ratio (CNR) and Q factor. The findings demonstrate that DECT significantly improves image contrast compared to SECT while further increases image sharpness. As a result, DECT may enable more accurate segmentation and, therefore, more accurate quantitative 3D analysis of graphite ores.

Diagnostic performance and feasibility of dual-layer detector dual-energy CT for characterization of urinary stones in patients of different sizes.

Urinary stones are frequently encountered in urology and are typically identified using non-contrast CT scans. Dual-energy CT (DECT) is a valuable imaging technique that produces material-specific images and allows for precise assessment of stone composition by estimating the effective atomic number (Zeff), a capability not achievable with the conventional single-energy CT's attenuation measurement method. To investigate the diagnostic performance and image quality of dual-layer detector DECT (dlDECT) in characterizing urinary stones in patients of different sizes. All consecutive dlDECT examinations with stone protocol and presence of urinary stones between July 2018 and November 2019 were retrospectively evaluated. Two radiologists independently reviewed 120 kVp and color-overlay Zeff images to determine stone composition (reference standard = crystallography) and image quality. The objective analysis included image noise and Zeff values measurement. A total of 739 urinary stones (median size 3.7 mm, range 1-35 mm) were identified on 177 CT examinations from 155 adults (mean age, 57 ± 15 years, 80 men, median weight 82.6 kg, range 42.6-186.9 kg). Using color-overlay Zeff images, the radiologists could subjectively interpret the composition in all stones ≥ 3 mm (n = 491). For stones with available reference standards (n = 74), dlDECT yielded a sensitivity of 80% (95%CI 44-98%) and a specificity of 98% (95%CI 92-100%) in visually discriminating uric acid from non-uric acid stones. Patients weighing > 90 kg and ≤ 90 kg had similar stone characterizability (p = 0.20), with 86% of stones characterized in the > 90 kg group and 87% in the ≤ 90 kg group. All examinations throughout various patients' weights revealed acceptable image quality. A Zeff cutoff of 7.66 accurately distinguished uric acid from non-uric acid stones (AUC = 1.00). Zeff analysis revealed AUCs of 0.78 and 0.91 for differentiating calcium-based stones from other non-uric stones and all stone types, respectively. dlDECT allowed accurate differentiation of uric acid and non-uric acid stones among patients with different body sizes with acceptable image quality. The ability to accurately differentiate uric acid stones from non-uric acid stones using color-overlay Zeff images allows for better tailored treatment strategies, helping to choose appropriate interventions and prevent potential complications related to urinary stones in patient care.

Diagnostic performance of modern computed tomography in cruciate ligament injury detection: A comprehensive study

BackgroundThis study aimed to evaluate the clinical utility of modern single and dual-energy computed tomography (CT) for assessing the integrity of the cruciate ligaments in patients that sustained acute trauma. MethodsPatients who underwent single- or dual-energy CT followed by 3 Tesla magnetic resonance imaging (MRI) or knee joint arthroscopy between 01/2016 and 12/2022 were included in this retrospective, monocentric study. Three radiologists specialized in musculoskeletal imaging independently evaluated all CT images for the presence of injury to the cruciate ligaments. An MRI consensus reading of two experienced readers and arthroscopy provided the reference standard. Diagnostic accuracy parameters and area under the receiver operator characteristic curve (AUC) were the primary metrics for diagnostic performance. ResultsCT images of 204 patients (median age, 49 years; IQR 36 – 64; 113 males) were evaluated. Dual-energy CT yielded significantly higher diagnostic accuracy and AUC for the detection of injury to the anterior (94% [240/255] vs 75% [266/357] and 0.89 vs 0.66) and posterior cruciate ligaments (95% [243/255] vs 87% [311/357] and 0.90 vs 0.61) compared to single-energy CT (all parameters, p <.005). Diagnostic confidence and image quality were significantly higher in dual-energy CT compared to single-energy CT (all parameters, p <.005). ConclusionsModern dual-energy CT is readily available and can serve as a screening tool for detecting or excluding cruciate ligament injuries in patients with acute trauma. Accurate diagnosis of cruciate ligament injuries is crucial to prevent adverse outcomes, including delayed treatment, chronic instability, or long-term functional limitations.

Single energy CT-based mass density and relative stopping power estimation for proton therapy using deep learning method

BackgroundThe number of patients undergoing proton therapy has increased in recent years. Current treatment planning systems (TPS) calculate dose maps using three-dimensional (3D) maps of relative stopping power (RSP) and mass density. The patient-specific maps of RSP and mass density were obtained by translating the CT number (HU) acquired using single-energy computed tomography (SECT) with appropriate conversions and coefficients. The proton dose calculation uncertainty of this approach is 2.5%-3.5% plus 1 mm margin. SECT is the major clinical modality for proton therapy treatment planning. It would be intriguing to enhance proton dose calculation accuracy using a deep learning (DL) approach centered on SECT.ObjectivesThe purpose of this work is to develop a deep learning method to generate mass density and relative stopping power (RSP) maps based on clinical single-energy CT (SECT) data for proton dose calculation in proton therapy treatment.MethodsArtificial neural networks (ANN), fully convolutional neural networks (FCNN), and residual neural networks (ResNet) were used to learn the correlation between voxel-specific mass density, RSP, and SECT CT number (HU). A stoichiometric calibration method based on SECT data and an empirical model based on dual-energy CT (DECT) images were chosen as reference models to evaluate the performance of deep learning neural networks. SECT images of a CIRS 062M electron density phantom were used as the training dataset for deep learning models. CIRS anthropomorphic M701 and M702 phantoms were used to test the performance of deep learning models.ResultsFor M701, the mean absolute percentage errors (MAPE) of the mass density map by FCNN are 0.39%, 0.92%, 0.68%, 0.92%, and 1.57% on the brain, spinal cord, soft tissue, bone, and lung, respectively, whereas with the SECT stoichiometric method, they are 0.99%, 2.34%, 1.87%, 2.90%, and 12.96%. For RSP maps, the MAPE of FCNN on M701 are 0.85%, 2.32%, 0.75%, 1.22%, and 1.25%, whereas with the SECT reference model, they are 0.95%, 2.61%, 2.08%, 7.74%, and 8.62%. ConclusionThe results show that deep learning neural networks have the potential to generate accurate voxel-specific material property information, which can be used to improve the accuracy of proton dose calculation.Advances in knowledgeDeep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

Quantification of extracellular volume with cardiac computed tomography in patients with left ventricle dysfunction

Abstract Background Myocardial fibrosis (MF) is a common endpoint in the majority of pathological mechanisms affecting cardiac muscle, ultimately resulting in cardiac failure. Cardiovascular magnetic resonance (CMR) with gadolinium-based contrast agent and T1 mapping technique is able to deliver a non-invasive quantification of the extracellular volume (ECV) expansion. Recently, ECV estimation with cardiac computed tomography (ECV-CCT) has been validated in the setting of cardiac amyloidosis, showing good agreement with ECV-CMR. However, no evidence is available with last generation single source single energy CT scanner in the clinical context of newly diagnosed left ventricular dysfunction. Therefore, the aim of this study is to test the diagnostic accuracy of ECV-CCT for the detection of MF in patients with recent diagnosis of reduced left ventricle ejection fraction (LVEF), having ECV-CMR as reference technique. Methods A consecutive cohort of 41 patients with newly diagnosed left ventricle dysfunction (LVEF &amp;lt; 50%) was enrolled. Time interval of maximum 30 days was present between CCT and CMR. Myocardial segment evaluability with each technique, agreement between ECV-CMR and ECV-CCT, regression analysis and Bland-Altman analysis were performed. Results Age of enrolled patients was 62 ± 11 years, and mean LVEF at CMR was 35.2 ± 10.7%. Mean total amount of administrated iodinated contrast was 112.6 ± 22.2 ml, while overall radiation exposure for ECV estimation was2.1 ± 1.1 mSv. Out of 656 myocardial segments available, 656 (100%) segments were evaluable at CCT while 640 (97.6%) were evaluable at CMR. ECV-CCT demonstrated slightly lower values compared to ECV-CMR both at global and region-based comparison (all segments, 33.1 ± 8.5% vs 35.5 ± 10.7%, p &amp;lt; 0.001). At regression analysis, strong correlations were described both at global and region-based analysis (all segments, r = 0.877, 95% CI: 0.840 to 0.914). On Bland-Altman analysis, bias between ECV-CMR and ECV-CCT for global analysis was 2.4 (95% CI: -7.8 to 12.7). Conclusions ECV estimation with last-generation single source single energy CT scanner is feasible and accurate. Integration of ECV measurement in comprehensive CCT evaluation of patients with newly diagnosed left ventricular dysfunction can be performed with a small increase in overall radiation exposure.

Deep-learned generation of renal dual-energy CT from a single-energy scan

To investigate the role of the deep-learning (DL) method in the generation of dual-energy computed tomography (DECT) images from single-energy images for precise diagnosis of kidney stone type. DECT of 23 patients was acquired, and the stone types were investigated based on the DECT software suggestions. The data were divided into two paired groups:120 kVp input and 80 kVp target and 120 kVp input and 135 kVp targets, p2p-UNet-GAN was exploited to generate the different energy images based on the common CT protocols. The images generated of the generative adversarial network (GAN) network were evaluated based on the SSIM, PSNR, and MSE metrics, and the values were estimated as 0.85-0.95, 28-32, and 0.85-0.89 respectively. The attenuation ratio of test patient images were estimated and compared with real patient reports. The network achieved high accuracy in stone region localisation and resulted in accurate stone type predictions. This study presents a useful method based on the DL technique to reduce patient radiation dose and facilitate the prediction of urinary stone types using single-energy CT imaging.

Comparison of image quality, contrast administration, and radiation doses in pediatric abdominal dual-layer detector dual-energy CT using propensity score matching analysis

PurposeTo compare the image quality, contrast administration, and radiation dose between single-energy CT (SECT) and dual-energy CT (DECT) in pediatric patients. MethodsFrom March to December 2021, children who underwent abdominal SECT or DECT were retrospectively included in this study. The DECT group received 10–30 % less contrast than the routine dose. CT images were obtained at hepatic venous phase using a routine reconstruction method (iDose4). DECT scans were additionally reconstructed with a virtual monoenergetic image (VMI) at 40 and 65 keV. Quantitative image evaluations compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, portal vein, and pancreas. Qualitative analysis assessed degree of contrast enhancement, lesion or organ conspicuity, image noise, artificiality, and overall image quality. ResultsAmong 318 patients, 112 (median age, 16 years; 56 in each group) were included after propensity score matching. Compared with the SECT group, DECT group with iDose4 demonstrated lower CNRs and SNRs, while VMI at 40 or 65 keV showed no significant difference. In qualitative analysis, iDose4 produced higher scores on artificiality, and VMI at 40 keV demonstrated superior contrast enhancement and lesion conspicuity in the DECT group. Overall image quality was higher with VMI 65 keV among the DECT patients, and there was no significant difference compared to SECT. The volume CT dose index (CTDIvol) did not differ significantly between the two groups (median, 2.8 mGy vs. 2.9 mGy; p = 0.802). The injected contrast volume was reduced by 10 % in the DECT group. ConclusionPediatric abdominal DECT with reduced contrast administration showed no significant differences in image quality and radiation dose compared to SECT.

Development of Cu application using dual-energy computed tomography for detecting medication-related osteonecrosis of the jaw.

The present study developed an application using dual-energy computed tomography (DECT) focused on Cu for detecting medication-related osteonecrosis of the jaw (MRONJ). First, we performed two types of phantom studies using a Cu wire syringe and pig mandible with Cu wire to detect Cu on DECT. Second, DECT examinations of 44 patients with MRONJ were performed to compare lesion and normal bone sites using single-energy CT, DECT-virtual non-calcium (VNCa), and DECT-Cu applications. Quantitative analyses of VNCa CT and CT values were performed, and a cut-off value was calculated using receiver operating characteristic analysis. Third, we compared the Cu content in the MRONJ and normal bone groups using inductively coupled plasma atomic emission spectroscopy (ICP-AES). The material-specific differences in attenuation between the two different energies enabled the accurate separation of Cu from Ca in phantom studies. The sensitivity and specificity for single-energy CT, DECT-VNCa, and DECT-Cu applications were 97.7% and 2.3%, 86.4% and 81.8%, and 88.6% and 97.7%, respectively. Thus, VNCa CT values obtained on DECT-Cu application images showed the highest area under the curve value and maximal diagnostic efficacy in differentiating lesion sites from normal bone sites. On ICP-AES analyses, the Cu content was significantly higher in the MRONJ group than in the normal bone group. DECT-Cu application demonstrated better diagnostic performance in detecting MRONJ compared with single-energy CT or DECT-VNCa.

Evaluation of three-dimensional dual-energy CT cholangiopancreatography image quality in patients with pancreatobiliary dilatation: Comparison with conventional single-energy CT

ObjectiveThis study aimed to evaluate three-dimensional (3D) negative-contrast CT cholangiopancreatography (nCTCP) image quality using dual-energy CT (DECT) with iterative reconstruction (IR) technique in patients with pancreatobiliary dilatation compared with single-energy CT (SECT). MethodsOf the patients, 67 and 56 underwent conventional SECT (SECT set) and DECT with IR technique (DECT set), respectively. All patients were retrospectively analyzed during the portal phase to compare objective image quality and other data including patient demographics, hepatic and pancreatic parenchymal enhancement, noise, and attenuation difference (AD) between dilated ducts and enhanced hepatic parenchyma, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and CT volume dose index (CTDIvol). Two radiologists used the five-point Likert scale to evaluate the subjective image quality of 3D nCTCP regarding image noise, sharpness of dilated ducts, and overall image quality. Statistical analyses used the Mann–Whitney U test. ResultsNo significant difference in patient demographics in either CT set was showed during objective evaluation (p > 0.05). However, higher hepatic and pancreatic parenchymal enhancement, AD, SNR, and CNR and lower hepatic and pancreatic noise (p < 0.005) as well as CTDIvol (p = 0.005) on DECT than on SECT were observed. Higher mean grades on DECT than on SECT were showed for image noise (4.65 vs 3.92), sharpness of dilated ducts (4.52 vs 3.94), and overall image quality (4.45 vs 3.91; p < 0.001), respectively during subjective evaluation. ConclusionA higher overall image quality and lower radiation dose on 3D nCTCP can be obtained by DECT with IR technique than with conventional SECT in patients with pancreatobiliary dilatation.

A Study on the Comparison of VNCa and VMI+ Image Quality and Optimized Method according to kV Modulation of DECT: A Porcine Ankle Ligament Model

This study conducted a quantitative and qualitative assessment of the effectiveness of Single-Energy and Dual-Energy Virtual non-calcium imaging, as well as Virtual Monoenergetic Imaging at 55, 65, and 75 keV, for diagnosing ligament conditions with regard to tube voltage modulation. A porcine hindlimb ankle ligament model was utilized for this evaluation. The two widest regions within the ligament model were designated as regions of interest, and measurements of Hounsfield Units, Signal-to-Noise Ratios, and Contrast-to-Noise Ratios were obtained. Quantitative evaluation was then conducted based on the average values. Furthermore, qualitative assessments of ligament diagnosis were carried out by five musculoskeletal radiologists and five radiological technologists, each with over five years of experience in Computed Tomography laboratory work. Following both quantitative and qualitative evaluations, it was observed that the Dual-Energy Virtual non-calcium image obtained at 70/Sn150 kVp and the Virtual Monoenergetic Imaging Plus 65 keV image exhibited superior image quality compared to the Single-Energy CT image. These differences were statistically significant (p&lt;0.05). Moreover, the highest image quality was noted at 65 keV within the Virtual Monoenergetic Imaging Plus energy range. The Dual-Energy Virtual non-calcium image was found to be more suitable for imaging ligaments than the Virtual Monoenergetic Imaging Plus 65 keV image.(p&lt;0.05) Notably, lower tube voltages were associated with improved image quality across all images. Consequently, it was determined that 70/Sn150 kVp Dual-Energy Virtual non-calcium images are more optimized than Single-Energy CT and Virtual Monoenergetic Imaging Plus 65 keV images for diagnosing ligament diseases in patients who present challenges for Magnetic Resonance Imaging and in emergency cases. Additionally, employing Virtual non-calcium Color Coding images for accurately describing ligament thickness or length may enhance the diagnostic value of the images.

Pelvis Treatment Plan Dose Comparison for Proton Therapy Using Single Energy and Dual Energy Computed Tomography Simulation Methods

In proton therapy, plan robustness is ameliorated by expanding the irradiation volume beyond the target, in order to mitigate setup and proton range uncertainties (RU). A byproduct of this expansion is elevated doses to surrounding organs at risk (OARs) which reduce some of the benefits associated with proton therapy. Dual-energy CT (DECT) has been shown in multiple phantom and animal tissue studies to reduce RU without compromising plan robustness. This study quantifies dosimetric differences between single-energy CT (SECT) and DECT for pelvis patients treated with intensity modulated proton therapy (IMPT). Under IRB approval, SECT and DECT scans from 25 IMPT pelvis patients were acquired using a CT scanner. Clinical plans were generated on the SECT images using robust optimization settings of 3.5% RU and 5 mm setup uncertainty in a treatment planning system. Subsequently, the clinical plans were recomputed on the corresponding DECT generated SPR-map images. For each patient, target coverage, mean and maximum OAR doses were compared for both image sets. Comparison of two plans showed systematic differences in target minimum dose (D99%) and V100%. Variations as high as 3.1% with DECT were observed in D99% indicating target under-dosage. On average, use of SECT overestimated V100% by 2.4% when compared to DECT. Since all clinical plans were optimized robustly to meet V95% coverage, higher agreement (<1%) was achieved for V95% (99.9±0.2%), and D95% (99.6±0.3%). DECT relative to SECT indicated slightly higher OAR maximum doses for femoral heads (2.3±2.78%), penile bulb/external genital (1.33±4.00%) and large bowel (0.5±1.25%). Similarly, higher mean dose was observed to rectum (2.08±4.88%), bladder (1.41±0.25%), femoral heads (1.18±6.61%) and penile bulb/external genital (1.78±5.74%) for DECT relative to SECT. Our results show a disparity between evaluated SECT and DECT target and OAR dosimetric parameters. Given the higher accuracies in proton range estimation associated with the use of DECT, its use can imply a potential for more conformal proton plans as well as higher TPS computed target coverage and OAR dose accuracy, both of which enhance overall treatment quality.

Accuracy of liver metastasis detection and characterization: Dual-energy CT versus single-energy CT with deep learning reconstruction

PurposeTo assess whether image quality differences between SECT (single-energy CT) and DECT (dual-energy CT 70 keV) with equivalent radiation doses result in altered detection and characterization accuracy of liver metastases when using deep learning image reconstruction (DLIR), and whether DECT spectral curve usage improves accuracy of indeterminate lesion characterization. MethodsIn this prospective Health Insurance Portability and Accountability Act–compliant study (March through August 2022), adult men and non-pregnant adult women with biopsy-proven colorectal cancer and liver metastases underwent SECT (120 kVp) and a DECT (70 keV) portovenous abdominal CT scan using DLIR in the same breath-hold (Revolution CT ES; GE Healthcare). Participants were excluded if consent could not be obtained, if there were nonequivalent radiation doses between the two scans, or if the examination was cancelled/rescheduled. Three radiologists independently performed lesion detection and characterization during two separate sessions (SECT DLIRmedium and DECT DLIRhigh) as well as reported lesion confidence and overall image quality. Hounsfield units were measured. Spectral HU curves were provided for any lesions rated as indeterminate. McNemar’s test was used to test the marginal homogeneity in terms of diagnostic sensitivity, accuracy and lesion detection. A generalized estimating equation method was used for categorical outcomes. Results30 participants (mean age, 58 years ± 11, 21 men) were evaluated. Mean CTDIvol was 34 mGy for both scans. 141 lesions (124 metastases, 17 benign) with a mean size of 0.8 cm ± 0.3 cm were identified. High scores for image quality (scores of 4 or 5) were not significantly different between DECT (N = 71 out of 90 total scores from the three readers) and SECT (N = 62) (OR, 2.01; 95% CI:0.89, 4.57; P = 0.093). Equivalent image noise to SECT DLIRmed (HU SD 10 ± 2) was obtained with DECT DLIRhigh (HU SD 10 ± 3) (P = 1). There was no significant difference in lesion detection between DECT and SECT (140/141 lesions) (99.3%; 95% CI:96.1%, 100%).The mean lesion confidence scores by each reader were 4.2 ± 1.3, 3.9 ± 1.0, and 4.8 ± 0.8 for SECT and 4.1 ± 1.4, 4.0 ± 1.0, and 4.7 ± 0.8 for DECT (odds ratio [OR], 0.83; 95% CI: 0.62, 1.11; P = 0.21). Small lesion (≤5mm) characterization accuracy on SECT and DECT was 89.1% (95% CI:76.4%, 96.4%; 41/46) and 84.8% (71.1%, 93.7%; 39/46), respectively (P = 0.41). Use of spectral HU lesion curves resulted in 34 correct changes in characterizations and no mischaracterizations. ConclusionDECT required a higher strength of DLIR to obtain equivalent noise compared to SECT DLIR. At equivalent radiation doses and image noise, there was no significant difference in subjective image quality or observer lesion performance between DECT (70 keV) and SECT. However, DECT spectral HU curves of indeterminate lesions improved characterization.

Split-filter dual energy computed tomography radiotherapy: From calibration to image guidance

Background and purposeDual-energy computed tomography (DECT) is an emerging technology in radiotherapy (RT). Here, we investigate split-filter DECT throughout the RT treatment chain as compared to single-energy CT (SECT). Materials and methodsDECT scans were acquired with a tin-gold split-filter at 140 kV resulting in a low- and high-energy CT reconstruction (recon). Ten cancer patients (four head-and-neck (HN)​, three rectum​, two anal/pelvisand one abdomen) were DECT scanned without and with iodine administered. A cylindrical and an anthropomorphic HN phantom were scanned with DECT and 120 kV SECT. The DECT images generated were: 120 kV SECT-equivalent (CTmix), virtual monoenergetic images (VMIs), iodine map, virtual non-contrast (VNC), effective atomic number (Zeff), and relative electron density (ρe,w). The clinical utility of these recons was investigated for calibration, delineation, dose calculation and image-guided RT (IGRT). ResultsA calibration curve for 75 keV VMI had a root-mean-square-error (RMSE) of 34 HU in closest agreement with the RSME of SECT calibration. This correlated with a phantom-based dosimetric agreement to SECT of γ1%1mm > 98%. A 40 keV VMI recon was most promising to improve tumor delineation accuracy with an average evaluation score of 1.6 corresponding to “partial improvement”. The dosimetric impact of iodine was in general < 2%. For this setup, VNC vs. non-contrast CTmix based dose calculations are considered equivalent. SECT- and DECT-based IGRT was in agreement within the setup uncertainty. ConclusionsDECT-based RT could be a feasible alternative to SECT providing additional recons to support the different steps of the RT workflow.

Synthetic dual-energy CT reconstruction from single-energy CT Using artificial intelligence.

To develop and assess the utility of synthetic dual-energy CT (sDECT) images generated from single-energy CT (SECT) using two state-of-the-art generative adversarial network (GAN) architectures for artificial intelligence-based image translation. In this retrospective study, 734 patients (389F; 62.8 years ± 14.9) who underwent enhanced DECT of the chest, abdomen, and pelvis between January 2018 and June 2019 were included. Using 70-keV as the input images (n = 141,009) and 50-keV, iodine, and virtual unenhanced (VUE) images as outputs, separate models were trained using Pix2PixHD and CycleGAN. Model performance on the test set (n = 17,839) was evaluated using mean squared error, structural similarity index, and peak signal-to-noise ratio. To objectively test the utility of these models, synthetic iodine material density and 50-keV images were generated from SECT images of 16 patients with gastrointestinal bleeding performed at another institution. The conspicuity of gastrointestinal bleeding using sDECT was compared to portal venous phase SECT. Synthetic VUE images were generated from 37 patients who underwent a CT urogram at another institution and model performance was compared to true unenhanced images. sDECT from both Pix2PixHD and CycleGAN were qualitatively indistinguishable from true DECT by a board-certified radiologist (avg accuracy 64.5%). Pix2PixHD had better quantitative performance compared to CycleGAN (e.g., structural similarity index for iodine: 87% vs. 46%, p-value < 0.001). sDECT using Pix2PixHD showed increased bleeding conspicuity for gastrointestinal bleeding and better removal of iodine on synthetic VUE compared to CycleGAN. sDECT from SECT using Pix2PixHD may afford some of the advantages of DECT.

Added value of arterial enhancement fraction derived from dual-energy computed tomography for preoperative diagnosis of cervical lymph node metastasis in papillary thyroid cancer: initial results.

To explore the added value of arterial enhancement fraction (AEF) derived from dual-energy computed tomography CT (DECT) to conventional image features for diagnosing cervical lymph node (LN) metastasis in papillary thyroid cancer (PTC). A total of 273 cervical LNs (153 non-metastatic and 120 metastatic) were recruited from 92 patients with PTC. Qualitative image features of LNs were assessed. Both single-energy CT (SECT)-derived AEF (AEFS) and DECT-derived AEF (AEFD) were calculated. Correlation between AEFD and AEFS was determined using Pearson's correlation coefficient. Multivariate logistic regression analysis with the forward variable selection method was used to build three models (conventional features, conventional features + AEFS, and conventional features + AEFD). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Abnormal enhancement, calcification, and cystic change were chosen to build model 1 and the model provided moderate diagnostic performance with an area under the ROC curve (AUC) of 0.675. Metastatic LNs demonstrated both significantly higher AEFD (1.14 vs 0.48; p < 0.001) and AEFS (1.08 vs 0.38; p < 0.001) than non-metastatic LNs. AEFD correlated well with AEFS (r = 0.802; p < 0.001), and exhibited comparable performance with AEFS (AUC, 0.867 vs 0.852; p = 0.628). Combining CT image features with AEFS (model 2) and AEFD (model 3) could significantly improve diagnostic performances (AUC, 0.865 vs 0.675; AUC, 0.883 vs 0.675; both p < 0.001). AEFD correlated well with AEFS, and exhibited comparable performance with AEFS. Integrating qualitative CT image features with both AEFS and AEFD could further improve the ability in diagnosing cervical LN metastasis in PTC. Arterial enhancement fraction (AEF) values, especially AEF derived from dual-energy computed tomography, can help to diagnose cervical lymph node metastasis in patients with papillary thyroid cancer, and complement conventional CT image features for improved clinical decision making. • Metastatic cervical lymph nodes (LNs) demonstrated significantly higher arterial enhancement fraction (AEF) derived from dual-energy computed tomography (DECT) and single-energy CT (SECT)-derived AEF (AEFS) than non-metastatic LNs in patients with papillary thyroid cancer. • DECT-derived AEF (AEFD) correlated significantly with AEFS, and exhibited comparable performance with AEFS. • Integrating qualitative CT images features with both AEFS and AEFD could further improve the differential ability.

Dual-Energy CT for Pediatric Thoracic Imaging: A Review.

Dual-energy CT has expanded the potential of thoracic imaging in both children and adults. Data processing allows material- and energy-specific reconstructions, which improve material differentiation and tissue characterization compared with single-energy CT. Material-specific reconstructions include iodine, virtual unenhanced, perfusion blood volume, and lung vessel images, which can improve assessment of vascular, mediastinal, and parenchymal abnormalities. The energy-specific reconstruction algorithm allows virtual monoenergetic reconstructions, including low-energy images to increase iodine conspicuity and high-energy images to reduce beam-hardening and metal artifacts. This review highlights dual-energy CT principles, hardware, and postprocessing algorithms; the clinical applications of dual-energy CT; and the potential benefits of photon counting (the most recently introduced iteration of spectral imaging) in pediatric thoracic imaging.

Technical Note: Assessment of Pulse Pileup on Single-Energy and Multienergy Images From a Clinical Photon-Counting Detector Computed Tomography.

Pulse pileup effects occur when pulses occur so close together that they fall on top of one another, resulting in count loss and errors in energy thresholding. To date, there has been little work systematically detailing the quantitative effects of pulse pileup on material decomposition accuracy for photon-counting detector (PCD) computed tomography (CT). Our aim in this work was to quantify the effects of pulse pileup on single-energy and multienergy CT images, including low-energy bin (BL), high-energy bin (BH), iodine map, and virtual noncontrast images from a commercial PCD-CT. Scans of a 20-cm diameter multienergy CT phantom with 10 solid inserts were acquired at a fixed tube potential as the tube current was varied across the available range. Four types of images (BL, BH, iodine map, and virtual noncontrast) were reconstructed using an iterative reconstruction algorithm at strength 2, a quantitative reconstruction kernel (Qr40), 2-/1-mm slice thickness/increment, and a 210-mm field-of-view. The mean and standard deviation of CT numbers were recorded and the ratios of CT number between BL and BH images were calculated and plotted, along with noise versus tube current and noise × versus tube current. As tube current was increased, the range of variations in CT numbers was less than 13.4 HU for all inserts and image types evaluated. Noise × versus tube current showed a small positive slope equal to a noise increase from 100 mA of 10% at 500 mA and 15% at 900 mA compared with what would be expected if the slope was zero. Minimal impact on single-energy and multienergy CT numbers and noise performance was observed for the evaluated clinical PCD-CT system.

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Emerging Quantitative Imaging Techniques in Sports Medicine.

This article describes recent advances in quantitative imaging of musculoskeletal extremity sports injuries, citing the existing literature evidence and what additional evidence is needed to make such techniques applicable to clinical practice. Compositional and functional MRI techniques including T2 mapping, diffusion tensor imaging, and sodium imaging as well as contrast-enhanced US have been applied to quantify pathophysiologic processes and biochemical compositions of muscles, tendons, ligaments, and cartilage. Dual-energy and/or spectral CT has shown potential, particularly for the evaluation of osseous and ligamentous injury (eg, creation of quantitative bone marrow edema maps), which is not possible with standard single-energy CT. Recent advances in US technology such as shear-wave elastography or US tissue characterization as well as MR elastography enable the quantification of mechanical, elastic, and physical properties of tissues in muscle and tendon injuries. The future role of novel imaging techniques such as photon-counting CT remains to be established. Eventual prediction of return to play (ie, the time needed for the injury to heal sufficiently so that the athlete can get back to playing their sport) and estimation of risk of repeat injury is desirable to help guide sports physicians in the treatment of their patients. Additional values of quantitative analyses, as opposed to routine qualitative analyses, still must be established using prospective longitudinal studies with larger sample sizes.

Effect of iodine concentration reduction by comparison of virtual monoenergetic image quality with dual-energy computed tomography

This study aimed to evaluate the image quality of virtual monoenergetic images (VMIs) with tube voltage modulation in pediatric abdominal computed tomography (CT) examination and to determine the effect of decreasing contrast agent concentration. Using a 1-year old pediatric phantom, five contrast agent concentration diluent tubes of 100%, 80%, 60%, 40%, and 20% of the same concentration as the average Hounsfield unit (HU) in the descending aorta were inserted, and the mixed image and VMIs (40, 60, and 80 keV) acquired using dual-energy CT were compared with single-energy CT (SECT) images. For quantitative evaluation, the HU and coefficient of variation (COV) of each image were compared and analyzed. The analysis revealed that the HU of the 40 keV VMIs, acquired with a tube voltage of 70 kV and 100% contrast agent concentration, was 61% higher than that of the SECT image. The results showed that SECT had the lowest COV among all contrast agent concentration and tube voltage combinations, while the 40 keV image acquired at 70 kV had the second-lowest COV value. The HU of the 40 keV image acquired at 70 kV at a contrast agent concentration of 100% was 9% higher than that of SECT at 80% concentration. This study confirms that 40 keV VMIs are more useful than SECT images for vascular diagnosis with contrast in pediatric abdominal CT examinations and that a 20% reduction in contrast agent concentration can reduce the risk of contrast agent concentration-induced nephrotoxicity in pediatric patients by increasing the subjective acceptability of image quality for diagnosis.