Search

To investigate the value of contrast-enhanced dual-energy spectral computed tomography (CT) in differentiating borderline epithelial ovarian tumours (BEOTs) from malignant epithelial ovarian tumours (MEOTs).Sixty patients who underwent pelvic contrast-enhanced spectral CT were divided into two groups for analysis based on the tumour types confirmed at histopathological examination (26 BEOTs and 34 MEOTs). The regions of interest (ROIs) were selected on solid tumour components to measure attenuation values on monochromatic image sets (40-140 keV) in all imaging phases and tumour iodine concentrations (IC) on material decomposition images. Differences in the attenuation value between the unenhanced and contrast-enhanced phases (enhancement degree) and between energy strengths (slope k, k = [attenuation at 40 keV- attenuation at 140 keV]/100) were calculated. All measurements between the two groups were compared with independent t-test. Receiver operating characteristic (ROC) curves were generated to calculate the sensitivity, specificity and area under the ROC curve (AUC). Logistic regression analysis was used to evaluate the diagnostic efficacy of using combined parameters in two-phase contrast-enhanced images.In the arterial phase (AP) and venous phase (VP), the BEOTs had significantly lower enhancement than MEOTs from 40 to 100 keV (p0.05). The k values and IC values both showed significant differences in the AP and VP (p0.05). Combining parameters in two contrast-enhanced phases provided 80.8% sensitivity and 82.4% specificity in differentiating MEOTs from BEOTs with an AUC of 0.844.Dual-energy spectral CT provides a multiparametric approach in differentiating BEOTs from MEOTs with the best diagnostic efficacy using combined parameters in the AP and VP images.

Computed tomography (CT) -guided bone biopsy is a diagnostic procedure performed on the musculoskeletal system with a high diagnostic yield and low complications. However, CT-guided bone biopsy has the disadvantage that it is difficult to confirm the presence of tumor cells during the biopsy procedure. Recently, the clinical benefits of dual-energy CT (DECT) over single-energy CT have been revealed. DECT can provide material decomposition images including calcium suppression images, and effective atomic number (Zeff) and electron density (ED) maps. ED maps have been reported to indicate cellularity. A 61-year-old woman with a history of breast cancer surgery was admitted to our hospital and underwent a CT-guided bone biopsy of the right ilium using ED maps. As a result, she was diagnosed with breast cancer metastases of intertrabecular bone. A comparison of ED maps with a pathological specimen revealed that high ED values occurred exclusively in the tumor area with high cellularity. This study indicates that ED maps produced using DECT may have potential utility in the accurate identification of metastases with high cellularity in bone lesions.

Purpose This study developed methods to quantify and improve the accuracy of dual-energy CT (DECT)-based bone marrow edema imaging using a clinical CT system. Objectives were: (a) to quantitatively compare DECT with gold-standard, fluid-sensitive MRI for imaging of edema-like marrow signal intensity (EMSI) and (b) to identify image analysis parameters that improve delineation of EMSI associated with acute knee injury on DECT images. Methods DECT images from ten participants with acute knee injury were decomposed into estimated fractions of bone, healthy marrow, and edema based on energy-dependent differences in tissue attenuation. Fluid-sensitive MR images were registered to DECT for quantitative, voxel-by-voxel comparison between the two modalities. An optimization scheme was developed to find attenuation coefficients for healthy marrow and edema that improved EMSI delineation, compared to MRI. DECT method accuracy was evaluated by measuring dice coefficients, mutual information, and normalized cross correlation between the DECT result and registered MRI. Results When applying the optimized three-material decomposition method, dice coefficients for EMSI identified through DECT vs MRI were 0.32 at the tibia and 0.13 at the femur. Optimization of attenuation coefficients improved dice coefficient, mutual information, and cross-correlation between DECT and gold-standard MRI by 48%-107% compared to three-material decomposition using non-optimized parameters, and improved mutual information and cross-correlation by 39%-58% compared to the manufacturer-provided two-material decomposition. Conclusions This study quantitatively evaluated the performance of DECT in imaging knee injury-associated EMSI and identified a method to optimize DECT-based visualization of complex tissues (marrow and edema) whose attenuation parameters cannot be easily characterized. Further studies are needed to improve DECT-based EMSI imaging at the femur.

BACKGROUND: Rapid-kilovoltage-switching dual-energy computed tomography (RDECT) is a non-invasive, alternative technique for quantitative diagnosis. This study aimed to investigate the value of RDECT for differentiating spinal osteolytic metastases (SOM) from spinal infections (SIs). METHODS: RDECT was performed on 29 patients with SOM and 18 patients with SIs. Both iodine-based and water-based material decomposition images were generated from the spectral CT scan. The iodine/water densities of lesions on iodine/water material-decomposition images and the CT attenuation values on traditional CT images were measured three times at different image levels, and the averages were calculated. The lesion-to-muscle ratio (LMR) and lesion-to-artery ratio (LAR) for iodine density measurements were calculated. All parameters were compared between the two groups using the two-tailed Student’s t-test. A P value

Impending major hardware advances in cardiac CT include three areas: ultra-high-resolution (UHR) CT, photon-counting CT, and phase-contrast CT. Cardiac CT is a particularly demanding CT application that requires a high degree of temporal resolution, spatial resolution, and soft-tissue contrast in a moving structure. In this review, cardiac CT is used to highlight the strengths of these technical advances. UHR CT improves visualization of calcified and stented vessels but may result in increased noise and radiation exposure. Photon-counting CT uses multiple photon energies to reduce artifacts, improve contrast resolution, and perform material decomposition. Finally, phase-contrast CT uses x-ray refraction properties to improve spatial and soft-tissue contrast. This review describes these hardware advances in CT and their relevance to cardiovascular imaging. © RSNA, 2020

Objectives Dual-energy and multienergy computed tomography (DECT/MECT) has the potential to simultaneously visualize two contrast agents in the small bowel: arterial enhancement of iodine in the bowel wall and enteric enhancement of bismuth in the bowel lumen. The purpose of this study was to explore its feasibility in a swine study using a research whole-body photon-counting-detector (PCD) computed tomography (CT) system. Materials and methods A phantom study was initially performed to evaluate the quantification accuracy of iodine and bismuth separation from a single PCD-CT scan, which also served as the calibration reference for material decomposition of in vivo swine PCD-CT data. In the animal study, a test bolus scan was first performed to determine the time-attenuation curve for the arterial enhancement, based on which the timing of the PCD-CT dual-contrast scan was determined. A 600 mL homogeneous bismuth-saline solution (180 mL Pepto-Bismol + 420 mL normal saline) was orally administered to the pig using esophageal intubation. Approximately 1 hour after bismuth administration, 40 mL iodine contrast (Omnipaque 350, 5 mL/s) was injected intravenously. A PCD-CT scan was performed 13 seconds after the initiation of the contrast injection to simultaneously capture the arterial enhancement of iodine and the enteric enhancement of bismuth. To provide optimal material separation and quantification, all PCD-CT scans in both phantom and animal studies were operated at 140 kV with 4 energy thresholds of 25, 50, 75, and 90 keV. Results Using a generic image-based material decomposition method, the iodine and bismuth samples were successfully delineated and quantified in the phantom images with a root-mean-square-error of 1.32 mg/mL in iodine measurement and 0.64 mg/mL in bismuth measurement. In the pig study, the enhancing bowel wall containing iodine and the small bowel loop containing bismuth were not differentiable in the original PCD-CT images. However, they were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. In addition, quantitative analysis showed that the misclassification between the two contrast materials was less than 1.0 mg/mL. Conclusions Our study demonstrated the feasibility of simultaneous imaging of iodine and bismuth in small bowel of swine using PCD-CT.

Amiodarone accumulates in the liver, where it increases x-ray attenuation due to its iodine content. We evaluated liver attenuation in patients treated and not treated with amiodarone using true-non-contrast (TNC) and virtual-non-contrast (VNC) images acquired with spectral-detector-CT (SDCT). 142 patients, of which 21 have been treated with amiodarone, receiving SDCT-examinations (unenhanced-chest CT [TNC], CT-angiography of chest and abdomen [CTA-Chest, CTA-Abdomen]) were included. TNC, CTA-Chest, CTA-Abdomen, and corresponding VNC-images (VNC-Chest, VNC-Abdomen) were reconstructed. Liver-attenuation-index (LAI) was calculated as difference between liver- and spleen-attenuation. Liver-attenuation and LAI derived from TNC-images of patients receiving amiodarone were higher. Contrary to TNC, liver-attenuation and LAI were not higher in amiodarone patients in VNC-Chest and in VNC-Abdomen. To verify these initial results, a phantom scan was performed and an additional patient cohort included, both confirming that VNC is viable of accurately subtracting iodine of hepatic amiodarone-deposits. This might help to monitor liver-attenuation more accurately and thereby detect liver steatosis as a sign of liver damage earlier as well as to verify amiodarone accumulation in the liver.

To assess the diagnostic image quality and material decomposition characteristics of portal venous phase abdominal CT scans performed on rapid kVp-switching DECT (rsDECT) in patients with large body habitus.We retrospectively included consecutive patients with large body habitus (≥ 90 kg) undergoing portal venous phase abdominal CT scans on rsDECT scanners between Sep 2014 and March 2018. Qualitative and quantitative assessment of the DECT data sets [65 keV monoenergetic, material density iodine (MD-I) and material density water (MD-W) images] was performed for determination of image quality (IQ) and image noise. Correlation of qualitative assessment scores with weight, BMI and patients' diameter were calculated using Pearson correlation test. Optimal thresholds were calculated using AUC and Youden index to define most appropriate size cut off, below which the IQ of material density images is largely acceptable.The 65 keV monoenergetic images were of diagnostic quality (diagnostic acceptability, DA ≥ 3) in 97.8% of patients (n = 91/93). However, there was significant IQ degradation of MD-I images in 20.4% (n = 19/93, DA 3) of patients. Similarly, there was significant degradation (DA 3) of MD-W images in 26.9% (25/92). Clinically significant artifacts (PA ≥ 3/4) were seen in 31% (n = 29/93) and 32.3% (30/93) of MD-I and MD-W images respectively. Optimal threshold for diagnostic acceptability of MD-I images were 110 kg for weight and 33.5 kg/mRapid kVp-switching DECT provides diagnostically acceptable monoenergetic images for patients with large body habitus (≥ 90 kg). There is degradation of IQ in the material density specific images particularly in patients weighing 110 kg and with BMI 33.5 kg/m

X-ray computed tomography (XCT) can reconstruct the internal image of an object by measuring the information of passing X-rays. However, the conventional XCT not only has poor performance in tissue contrast and spatial resolution but also fails to provide quantitative analysis results and specific material components. To overcome these limitations, the multi-energy CT (MECT) emerges and attracts increasing attention. A typical MECT system has a great potential in reducing X-ray radiation dose, improving spatial resolution, enhancing material discrimination ability, and providing quantitative results by collecting several projections from different energy windows or spectrums either sequentially or simultaneously. It has obtained great achievement in terms of tissue characterization, lesion detection, material decomposition, and so on. This can enhance the capabilities of imaging internal structures for accurate diagnosis and optimized treatments.

Dual-energy computed tomography (DE CT) is a promising tool with many current and evolving applications. Available DE CT scanners usually consist of one or two tubes, or use layered detectors for spectral separation. Most DE CT scanners can be used in single energy or dual-energy mode, except for the layered detector scanners that always acquire data in dual-energy mode. However, the layered detector scanners can retrospectively integrate the data from two layers to obtain conventional single energy images. DE CT mode enables generation of virtual monochromatic images, blended images, iodine quantification, improving conspicuity of iodinated contrast enhancement, and material decomposition maps or more sophisticated quantitative analysis not possible with conventional SE CT acquisition with an acceptable or even lower dose than the SE CT. This article reviews the basic principles of dual-energy CT and highlights many of its clinical applications in the evaluation of neurological conditions.

Conventional two-material dual-energy CT (DECT) decomposition is insufficient to model bone marrow, which contains three materials [bone minerals, red marrow (water), yellow marrow (fat)]. We explore an image-domain three-material decomposition DECT technique accounting for bone minerals in a bone-water-fat phantom. Three-material decomposition fat fraction (FF3MD) exhibited stronger correlation than two-material decomposition fat fraction (FF2MD) with FFMRI (r=0.95 vs r=0.69). With increasing bone minerals, correlation of FF3MD remained stable (r=0.81-1.02), whereas correlation of FF2MD decreased (r=0.21-0.65).

The physical principles of dual-energy computed tomography (DECT) are as old as computed tomography (CT) itself. To understand the strengths and the limits of this technology, a brief overview of theoretical basis of DECT will be provided. Specific attention will be focused on the interaction of X-rays with matter, on the principles of attenuation of X-rays in CT toward the intrinsic limits of conventional CT, on the material decomposition algorithms (two- and three-basis-material decomposition algorithms) and on effective Rho-Z methods. The progresses in material decomposition algorithms, in computational power of computers and in CT hardware, lead to the development of different technological solutions for DECT in clinical practice. The clinical applications of DECT are briefly reviewed in relation to the specific algorithms.

Background Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. Objective This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. Methods To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. Results Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. Conclusions The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.

We investigated an efficient method for material decomposition in radiography, which can separate soft tissues and bones from a single radiograph with the aid of a surface image obtained using a three-dimensional laser scanner through which the attenuation length within an object is estimated. This approach does not require double radiation exposures; thus, it can eliminate the technical difficulties associated with the conventional dual-energy material decomposition (DEMD) method, such as increased patient doses, increased execution time, and misregistration errors between two scans. We implemented the proposed algorithm and performed a computational simulation and an experiment to demonstrate its viability for single-energy material decomposition in radiography (80 kVp was used). The image characteristics of the proposed method were investigated and compared with those obtained using the DEMD method (50 kVp and 80 kVp were used). Our results indicate that the estimate of the attenuation length by using the surface image of the examined object may substitute for one of the two dual-energy measurements in conventional DEMD. Accordingly, the proposed method yielded material decomposition results similar to the results elicited by the DEMD method in radiography.

Dual-energy material decomposition (DEMD) is a well-established theoretical x-ray technique that uses low- and high-kilovoltage radiographs to separate soft tissue and bone in radiography and compu...

RATIONALE AND OBJECTIVES To evaluate the material decomposition (MD) techniques in rapid kVp switching dual-energy CT (rsDECT) for quantifying liver fat content in rats with nonalcoholic fatty liver. MATERIALS AND METHODS Fifty male Sprague-Dawley (SD) rats were divided into study group (n=37) and control group (n = 13) and underwent rsDECT examination at different intervals. All the data analysis was performed using AW4.7 workstation. The fat contents under the traditional fat(water), fat(blood), and fat(muscle) material decomposition (MD) images and the fat volume fraction (FVF) from the liver fat maps generated using multi-material decomposition (MMD) technique were measured. The pathological grades (grade 0, 1, 2 and 3) of fatty liver were determined after euthanasia. The measurement differences among different grades and the correlation of measurements with different grades was analyzed using ANOVA and Spearman correlation, respectively. A receiver operating characteristics (ROC) curve was used to analyze the diagnostic efficacies of fat contents and FVF. RESULTS There were statistically significant differences in FVF and fat contents under fat(water), fat(blood), fat(muscle) based MD images among different grades. These values correlated well with the pathological grades (R-value: 0.90, 0.75, 0.79, 0.80, all p

Material decomposition in computed tomography is a method for differentiation and quantification of materials in a sample and it utilizes the energy dependence of the linear attenuation coefficient. In this study, a post-image reconstruction material decomposition method is constructed for a low-energy micro-CT setup using a photon counting x-ray detector. The low photon energy range (4–11 keV) allows for K-edge contrast separation of naturally occurring materials in organic tissue without the need of additional contrast agents. The decomposition method was verified using a phantom and its capability to decompose biomedical samples was evaluated with paraffin embedded human atherosclerotic plaques. Commonly, the necessary dual energy data for material decomposition is obtained by manipulating the emitted x-ray spectrum from the source. With the photon counting detector, this data was obtained by acquiring two energy window images on each side of the K-edge of one material in the sample. The samples were decomposed into three materials based on attenuation values in manually selected regions. The method shows a successful decomposition of the verification phantom and a distinct distribution of iron, calcium and paraffin in the atherosclerotic plaque samples. Though the decompositions are affected by beam hardening and ring artifacts, the method shows potential for spectral evaluation of biomedical samples.

Dual-energy computer tomography (DECT) has high application prospects in distinguishing and quantifying materials. However, DECT requires higher hardware cost and higher radiation dose than single-energy CT imaging. In this paper, we have developed a cascaded network method to realize DECT imaging through SECT images, then get the basis material decomposition images. Specifically, we design the mapping convolutional neural network and the material decomposition U-Net to realize the mapping of low- to high-energy images and material decomposition, respectively. To verify the feasibility of the proposed method, we extracted 1442 cranial cavity slice images of 5 patients for experiments. The qualitative and quantitative results show that the proposed method can achieve high-quality DECT imaging of single energy data and high-precision basis material decomposition.

The aim of this study is to assess whether perifocal bone marrow edema (BME) in patients with osteoid osteoma (OO) can be accurately detected on dual-layer spectral CT (DLCT) with three-material decomposition. To that end, 18 patients with OO (25.33 ± 12.44 years; 7 females) were pairwise-matched with 18 patients (26.72 ± 9.65 years; 9 females) admitted for suspected pathologies other than OO in the same anatomic location but negative imaging findings. All patients were examined with DLCT and MRI. DLCT data was decomposed into hydroxyapatite and water- and fat-equivalent volume fraction maps. Two radiologists assessed DLCT-based volume fraction maps for the presence of perifocal BME, using a Likert scale (1 = no edema; 2 = likely no edema; 3 = likely edema; 4 = edema). Accuracy, sensitivity, and specificity for the detection of BME on DLCT were analyzed using MR findings as standard of reference. For the detection of BME in patients with OO, DLCT showed a sensitivity of 0.92, a specificity of 0.94, and an accuracy of 0.92 for both radiologists. Interreader agreement for the assessment of BME with DLCT was substantial (weighted κ = 0.78; 95% CI, 0.59, 0.94). DLCT with material-specific volume fraction maps allowed accurate detection of BME in patients with OO. This may spare patients additional examinations and facilitate the diagnosis of OO.

Neutron cross-sections have an energy dependence which is uniquely varying according to isotopic composition. By measuring the local neutron beam attenuation of an object as a function of energy, this can be related to its elemental composition. Therefore material decomposition measurements using a deuterium-deuterium (D-D) compact fast neutron generator in an energy-selective fashion have been carried out. Previous work described determining setup-specific effective reference cross-section data for homogeneous samples. This work presents first tests of this non-destructive analysis principle in a 2D computed tomography (CT) setup using heterogeneous samples, based on the aforementioned reference cross-sections. Measurement data were collected at five angles around the neutron generator corresponding to five effective energies. At each angle a CT reconstruction was performed to obtain energy-specific CT images. These CT images were use to calculate an elemental decomposition of the samples of interest. Good agreement between theory and experiment was found for most of the samples, while the more challenging cases highlighted limitations of the technique. These first tomographic measurements are discussed along with an outlook for further developments of the technique. © 2020 The Author(s), Radiation Physics and Chemistry, 181, ISSN:0969-806X

The feasibility of acquiring multi-energy CT data through slow modulation of the kVp as an alternative to photon-counting detectors (PCDs) is currently under exploration. A low kVp-switching rate can be enabled with a conventional CT system but raises challenges due to missing sinogram views. Our previous work used a CNN-based method for sinogram completion by generating full-sampled images from undersampled sinograms, providing an acceptable image quality at a 22°/kVp switching rate. The purpose of this study was to investigate a GAN-based spectral sinogram completion method for enabling a lower kVp switching rate. A Pix2Pix GAN model with paired undersampled sinogram of 45° or 120° projections/kVp and its corresponding full-sampled sinogram was implemented and trained. The completed data was subsequently used to perform sinogram domain material decomposition. Our results on a simulated FORBILD abdomen phantom dataset showed that the GAN-based method can further lower the kVp switching rate to 45° projections/kVp. The proposed GAN-based sinogram completion method facilitates slow-kVp switching acquisitions and thus further relaxes hardware requirements.

To investigate the feasibility of quantifying the chemical composition of coronary artery plaque in terms of water, lipid, protein, and calcium contents using dual-energy computed tomography (CT) in a simulation study. A CT simulation package was developed based on physical parameters of a clinical CT scanner. A digital thorax phantom was designed to simulate coronary arterial plaques in the range of 2-5mm in diameter. Both non-calcified and calcified plaques were studied. The non-calcified plaques were simulated as a mixture of water, lipid, and protein, while the calcified plaques also contained calcium. The water, lipid, protein, and calcium compositions of the plaques were selected to be within the expected clinical range. A total of 95 plaques for each lesion size were simulated using the CT simulation package at 80 and 135 kVp. Half-value layer measurements were made to make sure the simulated dose was within the range of clinical dual energy scanning protocols. Dual-energy material decomposition using a previously developed technique was performed to determine the volumetric fraction of water, lipid, protein, and calcium contents in each plaque. For non-calcified plaque, the total volume conservation provides the third constrain for three-material decomposition with dual energy CT. For calcified plaque, a fourth criterion was introduced from a previous report suggesting a linear correlation between water and protein contents in soft tissue. For non-calcified plaque, the root mean-squared error (RMSE) of the image-based decomposition was estimated to be 0.7%, 1.5%, and 0.3% for water, lipid, and protein contents, respectively. As for the calcified plaques, the RMSE of the 5mm plaques were estimated to be 5.6%, 5.7%, 0.2%, and 3.1%, for water, lipid, calcium, and protein contents, respectively. The RMSE increases as the plaque size reduces. The simulation results indicate that chemical composition of coronary arterial plaques can be quantified using dual-energy CT. By accurately quantifying the content of a coronary plaque lesion, our decomposition method may provide valuable insight for the assessment and stratification of coronary artery disease.

Generative Adversarial Networks (GANs) have been widely used and it is expected to use for the clinical examination and image. The objective of the current study was to synthesize material decomposition images of bone-water (bone(water)) and fat-water (fat(water)) reconstructed from dual-energy computed tomography (DECT) using an equivalent kilovoltage-CT (kV-CT) image and a deep conditional GAN. The effective atomic number images were reconstructed using DECT. We used 18,084 images of 28 patients divided into two datasets: the training data for the model included 16,146 images (20 patients) and the test data for evaluation included 1938 images (8 patients). Image prediction frameworks of the equivalent single energy CT images at 120 kVp to the effective atomic number images were created. The image-synthesis framework was based on a CNN with a generator and discriminator. The mean absolute error (MAE), relative mean square error (MSE), relative root mean square error (RMSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI) were evaluated. The Hounsfield unit (HU) difference between the synthesized and reference material decomposition images of bone(water) and fat(water) were within 5.3 HU and 20.3 HU, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the bone(water) images were 0.8, 1.3, 0.9, 0.9, 55.3, and 0.8, respectively. The average MAE, MSE, RMSE, SSIM, and MI of the synthesized and reference material decomposition of the fat(water) images were 0.0, 0.0, 0.1, 0.9, 72.1, and 1.4, respectively. The proposed model can act as a suitable alternative to the existing methods for the reconstruction of material decomposition images of bone(water) and fat(water) reconstructed via DECT from kV-CT.

International audience; Dual-energy computed tomography (DECT) is of great significance for clinical practice due to its huge potential to provide material-specific information. However, DECT scanners are usually more expensive than standard single-energy CT (SECT) scanners and thus are less accessible to undeveloped regions. In this paper, we show that the energy-domain correlation and anatomical consistency between standard DECT images can be harnessed by a deep learning model to provide high-performance DECT imaging from fully-sampled low-energy data together with single-view high-energy data. We demonstrate the feasibility of the approach with two independent cohorts (the first cohort including contrast-enhanced DECT scans of 5753 image slices from 22 patients and the second cohort including spectral CT scans without contrast injection of 2463 image slices from other 22 patients) and show its superior performance on DECT applications. The deep-learning-based approach could be useful to further significantly reduce the radiation dose of current premium DECT scanners and has the potential to simplify the hardware of DECT imaging systems and to enable DECT imaging using standard SECT scanners.

OBJECTIVE Dual-layer spectral detector computed tomography (DLCT) can detect noncalcified biliary stones. The diagnostic ability of DLCT for detecting biliary stones may be comparable to that of magnetic resonance cholangiopancreatography (MRCP). This study seeks to compare the diagnostic ability for biliary stones between these two imaging modalities. METHODS This retrospective study included 102 cases with a diagnosis of biliary stones including gallstones (n = 66) and common bile duct (CBD) stones (n = 25) or spontaneously passing CBD stones (n = 11). The reference standard used was operative findings, endoscopic retrograde cholangiopancreatography or follow-up over 6 months. In DLCT, 120-kVp images, 40-keV virtual monoenergetic images and material decomposition images were created. We compared the diagnostic ability of DLCT and MRCP for biliary stones using the McNemar's test. RESULTS The sensitivity and specificity of DLCT versus MRCP for biliary stones were 91.2% versus 95.6% and 90.9% versus 90.9%. Thus, the sensitivity and specificity were not significantly different (P = 0.25 and P = 1.0). Although in small stones (

Spectral photon-counting X-ray computed tomography (sCT) opens up new possibilities for the quantitative measurement of materials in an object compared with conventional energy-integrating CT or dual-energy CT. However, achieving reliable and accurate material decomposition in sCT is extremely challenging, due to the similarity between different basis materials, strong quantum noise, and photon-counting detector limitations. We propose a novel material decomposition method that works in a region-wise manner. The method consists in optimizing basis materials based on spatioenergy segmentation of regions of interest (ROIs) in sCT images and performing a fine material decomposition involving optimized decomposition matrix and sparsity regularization. The effectiveness of the proposed method was validated on both digital and physical data. The results showed that the proposed ROI-wise material decomposition method presents clearly higher reliability and accuracy compared with common decomposition methods based on total variation (TV) or L1-norm (lasso) regularization.

Evaluation of water material density images (wMDIm) of dual-energy CT (DECT) for earlier prediction of final infarct volume (fiV) in follow-up single-energy CT (SECT) and correlation with clinical outcome. Fifty patients (69 years, ± 12.1, 40–90, 50% female) with middle cerebral artery (MCA) occlusions were included. Early infarct volumes were analyzed in monoenergetic images (MonoIm) and wMDIm at 60 keV and compared with the fiV in SECT 4.9 days (± 4) after thrombectomy. Association between infarct volume and functional outcome was tested by linear regression analysis. wMDIm shows a prior visible infarct demarcation (60.7 ml, ± 74.9 ml) compared with the MonoIm (37.57 ml, ± 76.7 ml). Linear regression analysis, Bland–Altman plots and Pearson correlation coefficients show a close correlation of infarct volume in wMDIm to the fiV in SECT (r = 0.86; 95% CI 0.76–0.92), compared with MonoIm and SECT (r = 0.81; 95% CI 0.69–0.89). The agreement with SECT is substantially higher in patients with infarct volumes < 70 ml (n = 33; 66%). Coefficients were smaller with r = 0.59 (95% CI 0.31; 0.78) for MonoIm and SECT compared with r = 0.77 (95% CI 0.57; 0.88) for wMDIm and SECT. At admission, the mean NIHSS score and mRS were 17.02 (± 4.7) and 4.9 (± 0.2). mRS ≤ 2 was achieved in 56% at 90 days with a mean mRS of 2.5 (± 0.8) at discharge. Material decomposition allows earlier visibility of the final infarct volume. This promises an earlier evaluation of the dimension and severity of infarction and may lead to faster initiation of secondary stroke prophylaxis.

Cone-beam CT (CBCT) is widely used in diagnostic imaging and image-guided procedures, leading to an increasing need for advanced CBCT techniques, such as dual energy (DE) imaging. Previous studies have shown that DE-CBCT can perform quantitative material decomposition, including quantification of contrast agents, electron density, and virtual monoenergetic images. Currently, most CBCT systems perform DE imaging using a kVp switching technique. However, the disadvantages of this method are spatial and temporal misregistration as well as total scan time increase, leading to errors in the material decomposition. DE-CBCT with a dual layer flat panel detector potentially overcomes these limitations by acquiring the dual energy images simultaneously. In this work, we investigate the DE imaging performance of a prototype dual layer detector by evaluating its material decomposition capability and comparing its performance to that of the kVp switching method. Two sets of x-ray spectra were used for kVp switching: 80/120 kVp and 80/120 kVp + 1 mm Cu filtration. Our results show the dual layer detector outperforms kVp switching at 80/120 kVp with matched dose. The performance of kVp switching was better by adding 1 mm copper filtration to the high energy images (80/120 kVp + 1 mm Cu), though the dual layer detector still provided comparable performance for material decomposition tasks. Overall, both the dual layer detector and kVp switching methods provided quantitative material decomposition images in DE-CBCT, with the dual layer detector having additional potential advantages.

Dual-energy spectral computed tomography (DESCT) is based on fast switching between high and low voltages from view to view to obtain dual-energy imaging data, and it can generate monochromatic image sets, iodine-based material decomposition images and spectral CT curves. Quantitative spectral CT parameters may be valuable for reflecting Ki-67 expression and EGFR mutation status in non-small-cell lung cancer (NSCLC). We investigated the associations among the quantitative parameters generated in DESCT and Ki-67 expression and EGFR mutation in NSCLC. We studied sixty-five NSCLC patients with preoperative DESCT scans, and their specimens underwent Ki-67 and EGFR evaluations. Statistical analyses were performed to identify the spectral CT parameters for the diagnosis of Ki-67 expression and EGFR mutation status. We found that tumour grade and the slope of the spectral CT curve in the venous phase were the independent factors influencing the Ki-67 expression level, and the area under the curve (AUC) of the slope of the spectral CT curve in the venous phase in the receiver operating characteristic analysis for distinguishing different Ki-67 expression levels was 0.901. Smoking status and the normalized iodine concentration in the venous phase were independent factors influencing EGFR mutation, and the AUC of the two-factor combination for predicting the presence of EGFR mutation was 0.807. These results show that spectral CT parameters may be useful for predicting Ki-67 expression and the presence of EGFR mutation in NSCLC.

Purpose: Conventional stenosis quantification from single-energy computed tomography (SECT) images relies on segmentation of lumen boundaries, which suffers from partial volume averaging and calcium blooming effects. We present and evaluate a method for quantifying percent area stenosis using multienergy CT (MECT) images. Approach: We utilize material decomposition of MECT images to measure stenosis based on the ratio of iodine mass between vessel locations with and without a stenosis, thereby eliminating the requirement for segmentation of iodinated lumen. The method was first assessed using simulated MECT images created with different spatial resolutions. To experimentally assess this method, four phantoms with different stenosis severity (30% to 51%), vessel diameters (5.5 to 14 mm), and calcification densities (700 to 1100 mgHA / cc ) were fabricated. Conventional SECT images were acquired using a commercial CT system and were analyzed with commercial software. MECT images were acquired using a commercial dual-energy CT (DECT) system and also from a research photon-counting detector CT (PCD-CT) system. Three-material-decomposition was performed on MECT data, and iodine density maps were used to quantify stenosis. Clinical radiation doses were used for all data acquisitions. Results: Computer simulation verified that this method reduced partial volume and blooming effects, resulting in consistent stenosis measurements. Phantom experiments showed accurate and reproducible stenosis measurements from MECT images. For DECT and two-threshold PCD-CT images, the estimation errors were 4.0% to 7.0%, 2.0% to 9.0%, 10.0% to 18.0%, and - 1.0 % to - 5.0 % (ground truth: 51%, 51%, 51%, and 30%). For four-threshold PCD-CT images, the errors were 1.0% to 3.0%, 4.0% to 6.0%, - 1.0 % to 9.0%, and 0.0% to 6.0%. Errors using SECT were much larger, ranging from 4.4% to 46%, and were especially worse in the presence of dense calcifications. Conclusions: The proposed approach was shown to be insensitive to acquisition parameters, demonstrating the potential to improve the accuracy and precision of stenosis measurements in clinical practice.

To retrospectively quantify liver iron content in haematological patients suspected of transfusional haemosiderosis using dual-energy CT (DECT) and correlate with serum ferritin levels and estimated quantity of transfused iron. One hundred forty-seven consecutive dual-source dual-energy non-contrast chest-CTs in 110 haematologic patients intended primarily for exclusion of pulmonary infection between September 2016 and June 2017 were retrospectively evaluated. Image data was post-processed with a software prototype. After material decomposition, an iron enhancement map was created and freehand ROIs were drawn including most of the partially examined liver. The virtual iron content (VIC) was calculated and expressed in milligram/millilitre. VIC was correlated with serum ferritin and estimated amount of transfused iron. Scans of patients who had not received blood products were considered controls. Forty-eight (32.7%) cases (controls) had not received any blood transfusions whereas 67.3% had received one transfusion or more. Median serum ferritin and VIC were 138.0 μg/dl (range, 6.0–2628.0 μg/dl) and 1.33 mg/ml (range, − 0.94–7.56 mg/ml) in the post-transfusional group and 27.0 μg/dl (range, 1.0–248.0 μg/dl) and 0.61 mg/ml (range, − 2.1–2.4) in the control group. Correlation between serum ferritin and VIC was strong (r = 0.623; p

Dual-energy computed tomography (DECT) enables material decomposition and virtual monochromatic images by acquiring 2 different energy X-ray data sets. DECT can detect musculoskeletal pathologic conditions that CT alone cannot, and that would otherwise require MR imaging. In this review, the authors discuss several useful techniques and applications of DECT in musculoskeletal research: virtual monochromatic images, virtual noncalcium images, gout, iodine map, and tendons.

Aslan, Serdar/0000-0003-2950-8767; Camlidag, Ilkay/0000-0001-6151-1312; Aslan, Serdar/0000-0003-2950-8767 WOS: 000460502900019 PubMed: 30155698 PurposeMultidetector computed tomography (MDCT) is used in the diagnosis of pancreatic ductal adenocarcinoma (PDAC), but it may be inadequate in some cases. Tumor detection can be improved using rapid kV-switching dual-energy CT (rsDECT) and iodine maps. Our aim this study is to evaluate tumor conspicuity in PDAC cases using rsDECT and iodine maps.MethodsNinety cases with PDAC were evaluated rsDECT. Tumor contrast (HU) differences, tumor size, CNR (contrast-noise ratio), and noise were measured at 70keV, individual CNR-energy level, and 45keV, respectively. Quantitative differences in contrast gain 70-CNR and CNR-45 were compared. On iodine maps, the iodine concentration measured in the tumor and parenchyma was normalized to the aorta as normalized iodine concentration (NIC) and compared.ResultsThe median optimized viewing energy level was 51keV. The meanSD tumor contrast values were 62 +/- 20, 115 +/- 48, and 152 +/- 48 HU (p

Objectives To assess whether bone marrow edema in patients with acute vertebral fractures can be accurately diagnosed based on three-material decomposition with dual-layer spectral CT (DLCT). Materials and methods Acute (n = 41) and chronic (n = 18) osteoporotic thoracolumbar vertebral fractures as diagnosed by MRI (hyperintense signal in STIR sequences) in 27 subjects (72 ± 11 years; 17 women) were assessed with DLCT. Spectral data were decomposed into hydroxyapatite, edema-equivalent, and fat-equivalent density maps using an in-house-developed algorithm. Two radiologists, blinded to clinical and MR findings, assessed DLCT and conventional CT independently, using a Likert scale (1 = no edema; 2 = likely no edema; 3 = likely edema; 4 = edema). For DLCT and conventional CT, accuracy, sensitivity, and specificity for identifying acute fractures (Likert scale, 3 and 4) were analyzed separately using MRI as standard of reference. Results For the identification of acute fractures, conventional CT showed a sensitivity of 0.73–0.76 and specificity of 0.78–0.83, whereas the sensitivity (0.93–0.95) and specificity (0.89) of decomposed DLCT images were substantially higher. Accuracy increased from 0.76 for conventional CT to 0.92–0.93 using DLCT. Interreader agreement for fracture assessment was high in conventional CT (weighted κ [95% confidence interval]; 0.81 [0.70; 0.92]) and DLCT (0.96 [0.92; 1.00]). Conclusions Material decomposition of DLCT data substantially improved accuracy for the diagnosis of acute vertebral fractures, with a high interreader agreement. This may spare patients additional examinations and facilitate the diagnosis of vertebral fractures.

Purpose Evaluation of the influence of iterative reconstruction, tube settings and patient habitus on the accuracy of iodine quantification with dual-layer spectral CT (DL-CT). Material and methods A CT abdomen phantom with different extension rings and four iodine inserts (1, 2, 5 and 10 mg/ml) was scanned on a DL-CT. The phantom was scanned with tube-voltages of 120 and 140 kVp and CTDIvol of 2.5, 5, 10 and 20 mGy. Reconstructions were performed for eight levels of iterative reconstruction (i0-i7). Diagnostic dose levels are classified depending on patient-size and radiation dose. Results Measurements of iodine concentration showed accurate and reliable results. Taking all CTDIvol-levels into account, the mean absolute percentage difference (MAPD) showed less accuracy for low CTDIvol-levels (2.5 mGy: 34.72%) than for high CTDIvol-levels (20 mGy: 5.89%). At diagnostic dose levels, accurate quantification of iodine was possible (MAPD 3.38%). Level of iterative reconstruction did not significantly influence iodine measurements. Iodine quantification worked more accurately at a tube voltage of 140 kVp. Phantom size had a considerable effect only at low-dose-levels; at diagnostic dose levels the effect of phantom size decreased (MAPD Conclusion With DL-CT, even low iodine concentrations can be accurately quantified. Accuracies are higher when diagnostic radiation doses are employed.

Our objective was to evaluate image quality (IQ) and material decomposition in patients with large body habitus undergoing portal venous phase abdominal computed tomography (CT) scans on dual-source dual-energy CT (dsDECT) scanners.This retrospective analysis included 30 scans from consecutive patients (19 males/11 females, mean ± SD age = 55.3 ± 17.5 years, range = 27-87 years) with large body habitus (≥90 kg, mean ± SD weight = 105.4 ± 12.35, range = 91-145 kg) who underwent portal venous phase abdominal DECT examinations on dsDECT scanner between Jan 2015 and Dec 2015. Qualitative and quantitative evaluation of IQ of DECT data sets (blended, iodine, and virtual noncontrast images) was performed. The patients were categorized into 2 groups (group A, ≤104 kg; group B,104 kg).The mean ± SD patient body weight in group A was 97.2 ± 4.5 kg (range = 91-104 kg) and 114.8 ± 11.7 kg (range = 104.3-145.2 kg) for patients in group B. The diagnostic acceptability of the blended images in patients104 kg was lower (3.6 vs 4,3 in 4/14 vs 0/16, P = 0.03). The extension of visceral anatomy beyond DE field of view (DEFOV) was seen in 60% (28 organs in 18 patients), the most common organs being liver and spleen. The incidence of visceral organs outside DEFOV was significantly higher in patients104 kg (18 vs 10, P = 0.03). Outside the DEFOV, blended images demonstrated higher image noise (mean: 14.48, range = 10.09-26.83 vs mean: 9.5, range = 7.3-15.8) P0.001) and lower signal-to-noise ratio (mean: 4.15, range = 1.5-7.6 vs mean: 7.5, range = 4.2-9.9) P0.001), and material-specific information was not available in this region. Within the DEFOV, the IQ of iodine maps and virtual non-contrast images were diagnostically acceptable with diagnostic acceptability of 3 or greater in nearly all patients. A 40-cm transverse diameter cut-off provided a good predictor of extension of visceral anatomy outside the effective DEFOV.Dual-source DECT allows diagnostically acceptable IQ and material separation in patients with large body habitus with the major limitation of exclusion of patient anatomy and organs outside the effective dual-energy field of view.

Dual-energy mega-electron-volt (MeV) X-ray container radiography has become a well-established technique in customs security application, because of its material discrimination capability. The main difficulty of X-ray radiography is dealing with the materials overlapping problem. When two or more materials exist along the X-ray beam path, its material discrimination performance will be obviously affected. Computed tomography (CT) collects many X-ray measurements taken from different angles surrounding an object to produce cross-sectional (tomographic) images of the scanned object. Therefore, CT can provide real 3-D images inside the object. However, due to the bulky container volume and complex types of cargos, it is very hard to develop such a huge CT system for container inspection. To the best of our knowledge, there has no such commercial X-ray CT system for container inspection yet. This paper presents the design of a dual MeV energy X-ray CT system for cargo container inspection which uses an accelerator with fast 6/9 MeV switching spectra, an arc detector array and rotating mechanism. A dual MeV energy X-ray CT image reconstruction and material decomposition algorithm are developed. An experimental system was built with the same accelerator and detector array as used in the designed container CT system. Experimental results that prove the validity and effectiveness of the algorithm and CT system are presented.

Spectral imaging is an umbrella term for energy-resolved X-ray imaging in medicine. The technique makes use of the energy dependence of X-ray attenuation to either increase the contrast-to-noise ratio, or to provide quantitative image data and reduce image artefacts by so-called material decomposition. Spectral imaging is not new, but has gained interest in recent years because of rapidly increasing availability of spectral and dual-energy CT and the dawn of energy-resolved photon-counting detectors. This review examines the current technological status of spectral and dual-energy imaging and a number of practical applications of the technology in medicine.

Dual-energy computed tomography (CT) has the potential to improve detection of abnormalities and increase diagnostic confidence in the evaluation of a variety of neurologic conditions by using different x-ray energy-dependent absorption behaviors of different materials. This article reviews the virtual monochromatic imaging applications of dual-energy CT, particularly material decomposition algorithms to improve lesion conspicuity, define lesion-normal tissue interface using different reconstruction techniques, and discuss miscellaneous emerging applications of dual-energy CT for neuroimaging, with an emphasis on their potential clinical utility.

There is increasing use of dual-energy computed tomography (DECT) for the evaluation of head and neck pathologic entities. Optimal DECT utilization requires familiarity with the appearance of normal tissues variants, and pathologic entities on different DECT reconstructions that may be used in clinical practice. The purpose of this article is to provide a practical, pictorial review of the appearance of normal anatomic structures and different neoplastic and nonneoplastic head and neck pathologic entities on commonly used DECT reconstructions.

Purpose To investigate the feasibility of accurate quantification of iodine mass thickness in contrast-enhanced spectral mammography. Materials and methods A computer simulation model was developed to evaluate the performance of a photon-counting spectral mammography system in the application of contrast-enhanced spectral mammography. A figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and prefiltrations for breasts of various thicknesses and densities. Experimental phantom studies were also performed using a beam energy of 40 kVp and a splitting energy of 34 keV with 3 mm Al prefiltration. A two-step calibration method was investigated to quantify the iodine mass thickness, and was validated using phantoms composed of a mixture of glandular and adipose materials, for various breast thicknesses and densities. Finally, the traditional dual-energy log-weighted subtraction method was also studied as a comparison. The measured iodine signal from both methods was compared to the known value to characterize the quantification accuracy and precision. Results The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy between 42 and 46 kVp with a splitting energy at 34 keV. The optimal tube voltage decreased as the breast thickness or the Al prefiltration increased. The proposed quantification method was able to measure iodine mass thickness on phantoms of various thicknesses and densities with high accuracy. The root-mean-square (RMS) error for cm-scale lesion phantoms was estimated to be 0.20 mg/cm2 . The precision of the technique, characterized by the standard deviation of the measurements, was estimated to be 0.18 mg/cm2 . The traditional weighted subtraction method also predicted a linear correlation between the measured signal and the known iodine mass thickness. However, the correlation slope and offset values were strongly dependent on the total breast thickness and density. Conclusion The results of this study suggest that iodine mass thickness for cm-scale lesions can be accurately quantified with contrast-enhanced spectral mammography. The quantitative information can potentially improve the differential power for malignancy.

Currently, no standardized method for measuring intrahepatic fat density via conventional computed tomography (CT) exists.We aim to quantify intrahepatic fat density via material decomposition analysis using rapid kilovolt peak-switching dual-energy (RSDE) CT.Homogenized porcine liver and fat (lard) were mixed in various ratios to produce phantoms for fat density verification. The actual fat density was measured on the basis of the phantom volume and weight, and these measurements were used as reference densities. The fat and liver mass attenuation coefficients, which were used as the material basis pairs, were employed in the material decomposition analysis. Then, the measured fat density of each phantom was compared with the reference densities.For fat content differences exceeding 2%, the measured fat density for the phantoms became statistically significant (p 0.01). The correlation between the reference densities and RSDE-measured fat densities was reasonably high (R 0.9997); this indicates the validity of this analysis method.Intrahepatic fat density can be measured using the mass attenuation coefficients of fat and liver in a material decomposition analysis. Given the knowledge of the accuracy and the limitations found in this study, our method can quantitatively evaluate fat density.

Dual-energy CT has remained underutilized over the past decade probably due to a cumbersome workflow issue and current technical limitations. Clinical radiologists should be made aware of the potential clinical benefits of dual-energy CT over single-energy CT. To accomplish this aim, the basic principle, current acquisition methods with advantages and disadvantages, and various material-specific imaging methods as clinical applications of dual-energy CT should be addressed in detail. Current dual-energy CT acquisition methods include dual tubes with or without beam filtration, rapid voltage switching, dual-layer detector, split filter technique, and sequential scanning. Dual-energy material-specific imaging methods include virtual monoenergetic or monochromatic imaging, effective atomic number map, virtual non-contrast or unenhanced imaging, virtual non-calcium imaging, iodine map, inhaled xenon map, uric acid imaging, automatic bone removal, and lung vessels analysis. In this review, we focus on dual-energy CT imaging including related issues of radiation exposure to patients, scanning and post-processing options, and potential clinical benefits mainly to improve the understanding of clinical radiologists and thus, expand the clinical use of dual-energy CT; in addition, we briefly describe the current technical limitations of dual-energy CT and the current developments of photon-counting detector.

Low electrical resistivity (high dark carrier concentration) of CH3NH3SnI3 often leads to short-circuiting in solar cells, and appropriate thin-film modifications are required to ensure functional devices. The long-term durability of organic–inorganic perovskite solar cells necessitates the protection of perovskite thin films from moisture to prevent material decomposition. Herein, we report that the electrical resistivity and the moisture stability of two-dimensional (2D) Ruddlesden–Popper (CH3(CH2)3NH3)2(CH3NH3)n−1SnnI3n+1 perovskites are considerably improved compared to those of the three-dimensional (3D) CH3NH3SnI3 perovskite and subsequently show the solar cell fabrication using a simple one-step spin-coating method. These 2D perovskites are semiconductors with optical band gaps progressively decreasing from 1.83 eV (n = 1) to 1.20 eV (n = ∞). The n = 3 and n = 4 members with optimal band gaps of 1.50 and 1.42 eV for solar cells, respectively, were thus chosen for in-depth studies. We demonstrate th...

Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondestructive testing. In a standard CT image, pixels having the same Hounsfield Units (HU) can correspond to different materials, and it is therefore challenging to differentiate and quantify materials. Dual-energy CT (DECT) is desirable to differentiate multiple materials, but the costly DECT scanners are not widely available as single-energy CT (SECT) scanners. Recent advancement in deep learning provides an enabling tool to map images between different modalities with incorporated prior knowledge. Here we develop a deep learning approach to perform DECT imaging by using the standard SECT data. The end point of the approach is a model capable of providing the high-energy CT image for a given input low-energy CT image. The feasibility of the deep learning-based DECT imaging method using a SECT data is demonstrated using contrast-enhanced DECT images and evaluated using clinical relevant indexes. This work opens new opportunities for numerous DECT clinical applications with a standard SECT data and may enable significantly simplified hardware design, scanning dose and image cost reduction for future DECT systems.

Objectives The purpose of this study was to assess the feasibility of dual-energy CT-based material decomposition using dual-X-ray spectra information to determine local concentrations of holmium microspheres in phantoms and in an animal model. Materials and methods A spectral calibration phantom with a solution containing 10 mg/mL holmium and various tube settings was scanned using a third-generation dual-energy CT scanner to depict an energy-dependent and material-dependent enhancement vectors. A serial dilution of holmium (microspheres) was quantified by spectral material decomposition and compared with known holmium concentrations. Subsequently, the feasibility of the spectral material decomposition was demonstrated in situ in three euthanized rabbits with injected (radioactive) holmium microspheres. Results The measured CT values of the holmium solutions scale linearly to all measured concentrations and tube settings (R2 = 1.00). Material decomposition based on CT acquisitions using the tube voltage combinations of 80/150 Sn kV or 100/150 Sn kV allow the most accurate quantifications for concentrations down to 0.125 mg/mL holmium. Conclusion Dual-energy CT facilitates image-based material decomposition to detect and quantify holmium microspheres in phantoms and rabbits. Key Points • Quantification of holmium concentrations based on dual-energy CT is obtained with good accuracy. • The optimal tube-voltage pairs for quantifying holmium were 80/150 Sn kV and 100/150 Sn kV using a third-generation dual-source CT system. • Quantification of accumulated holmium facilitates the assessment of local dosimetry for radiation therapies.