Single-energy Computed Tomography Images Research Articles

Optimizing twin-beam dual-energy CT reconstruction: Quantitative consistency and stability assessment in reference to 120 kV: An observational study.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Synthesis of virtual monoenergetic images from kilovoltage peak images using wavelet loss enhanced CycleGAN for improving radiomics features reproducibility.

Dual-energy computed tomography (CT) can provide a range of image information beyond conventional CT through virtual monoenergetic images (VMIs). The purpose of this study was to investigate the impact of material decomposition in detector-based spectral CT on radiomics features and effectiveness of using deep learning-based image synthesis to improve the reproducibility of radiomics features. In this paper, spectral CT image data from 45 esophageal cancer patients were collected for investigation retrospectively. First, we computed the correlation coefficient of radiomics features between conventional kilovoltage peak (kVp) CT images and VMI. Then, a wavelet loss-enhanced CycleGAN (WLL-CycleGAN) with paired loss terms was developed to synthesize virtual monoenergetic CT images from the corresponding conventional single-energy CT (SECT) images for improving radiomics reproducibility. Finally, the radiomic features in 6 different categories, including gray-level co-occurrence matrix (GLCM), gray-level difference matrix (GLDM), gray-level run-length matrix (GLRLM), gray-level size-zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and wavelet, were extracted from the gross tumor volumes from conventional single energy CT, synthetic virtual monoenergetic CT images, and virtual monoenergetic CT images. Comparison between errors in the VMI and synthetic VMI (sVMI) suggested that the performance of our proposed deep learning method improved the radiomic feature accuracy. Material decomposition of dual-layer dual-energy CT (DECT) can substantially influence the reproducibility of the radiomic features, and the degree of impact is feature dependent. The average reduction of radiomics errors for 15 patients in testing sets was 96.9% for first-order, 12.1% for GLCM, 12.9% for GLDM, 15.7% for GLRLM, 50.3% for GLSZM, 53.4% for NGTDM, and 6% for wavelet features. The work revealed that material decomposition has a significant effect on the radiomic feature values. The deep learning-based method reduced the influence of material decomposition in VMIs and might improve the robustness and reproducibility of radiomic features in esophageal cancer. Quantitative results demonstrated that our proposed wavelet loss-enhanced paired CycleGAN outperforms the original CycleGAN.

Improved detection of cholesterol gallstones using quasi-material decomposition images generated from single-energy computed tomography images via deep learning.

In this study, we developed a method for generating quasi-material decomposition (quasi-MD) images from single-energy computed tomography (SECT) images using a deep convolutional neural network (DCNN). Our aim was to improve the detection of cholesterol gallstones and to determine the clinical utility of quasi-MD images. Four thousand pairs of virtual monochromatic images (70keV) and MD images (fat/water) of the same section, obtained via dual-energy computed tomography (DECT), were used to train the DCNN. The trained DCNN can automatically generate quasi-MD images from the SECT images. Additional SECT images were obtained from 70 patients (40 with and 30 without cholesterol gallstones) to generate quasi-MD images for testing. The presence of gallstones in this dataset was confirmed by ultrasonography. We conducted a receiver operating characteristic (ROC) observer study with three radiologists to validate the clinical utility of the quasi-MD images for detecting cholesterol gallstones. The mean area under the ROC curve for the detection of cholesterol gallstones improved from 0.867 to 0.921 (p = 0.001) when quasi-MD images were added to SECT images. The clinical utility of quasi-MD imaging for detecting cholesterol gallstones was showed. This study demonstrated that the lesion detection capability of images obtained from SECT can be improved using a DCNN trained with DECT images obtained using high-end computed tomography systems.

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.

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.

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.

Improved GAN: Using a transformer module generator approach for material decomposition

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

Virtual computed-tomography system for deep-learning-based material decomposition

Objective. Material decomposition (MD) evaluates the elemental composition of human tissues and organs via computed tomography (CT) and is indispensable in correlating anatomical images with functional ones. A major issue in MD is inaccurate elemental information about the real human body. To overcome this problem, we developed a virtual CT system model, by which various reconstructed images can be generated based on ICRP110 human phantoms with information about six major elements (H, C, N, O, P, and Ca). Approach. We generated CT datasets labelled with accurate elemental information using the proposed generative CT model and trained a deep learning (DL)-based model to estimate the material distribution with the ICRP110 based human phantom as well as the digital Shepp–Logan phantom. The accuracy in quad-, dual-, and single-energy CT cases was investigated. The influence of beam-hardening artefacts, noise, and spectrum variations were analysed with testing datasets including elemental density and anatomical shape variations. Main results. The results indicated that this DL approach can realise precise MD, even with single-energy CT images. Moreover, noise, beam-hardening artefacts, and spectrum variations were shown to have minimal impact on the MD. Significance. Present results suggest that the difficulty to prepare a large CT database can be solved by introducing the virtual CT system and the proposed technique can be applied to clinical radiodiagnosis and radiotherapy.

Evaluation of Stopping Power Ratio Calculation Using Dual-energy Computed Tomography With Fast Kilovoltage Switching for Treatment Planning of Particle Therapy.

This study evaluated the calculation accuracy of the stopping power ratio (SPR) using dual-energy computed tomography with fast kilovoltage switching (FKSCT) for particle therapy. A tissue characterization phantom with various reference materials was scanned to obtain single-energy computed tomography (SECT) images and generate virtual monochromatic images at 77 keV (VMI77keV) and 140 keV (VMI140keV), water density (WD) images, and effective Z (Zeff) images. For SECT, VMI77keV and VMI140keV lookup tables were generated to convert the measured Hounsfield value into the theoretical SPR for a normal phantom size. Subsequently, the reference materials were scanned in small and large phantoms. The SPR was calculated using the lookup tables of SECT (SPRSECT) images, VMI77keV (SPR77keV), and VMI140keV (SPR140keV), and it was derived from the WD and Zeff (SPRWD). In the normal-sized phantom, the overall mean difference between SPRWD and theoretical SPR was -0.3%, and remained below 2% for most reference materials. For the large phantom, the overall mean absolute difference for SPR140keV (3.0%, p=0.006) and SPRWD (3.2%, p=0.002) for the reference materials was significantly lower than that for SPRSECT (5.9%). For the small phantom, a significant reduction in the mean difference in the SPR calculation was observed in SPR77keV (1.0%, p=0.001) and SPR140keV (1.1%, p=0.013) compared with SPRSECT (2.2%). VMI140keV generated using FKSCT significantly improves the estimation accuracy of SPR compared with SECT. Thus, FKSCT may be used to improve the dose calculation accuracy for treatment planning of particle therapy.

Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography.

To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality. Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model. The contrast-to-noise ratio was significantly improved (p<0.001) in the generated VMIs (4.1±1.8) compared to the SECT images (2.8±1.1). The mean overall image quality (4.1±0.6) and tumor enhancement (3.6±0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p<0.001) than that in the SECT images (3.2±0.4 and 2.8±0.4 for overall image quality and tumor enhancement, respectively). The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.

Image synthesis of monoenergetic CT image in dual‐energy CT using kilovoltage CT with deep convolutional generative adversarial networks

PurposeTo synthesize a dual‐energy computed tomography (DECT) image from an equivalent kilovoltage computed tomography (kV‐CT) image using a deep convolutional adversarial network.MethodsA total of 18,084 images of 28 patients are categorized into training and test datasets. Monoenergetic CT images at 40, 70, and 140 keV and equivalent kV‐CT images at 120 kVp are reconstructed via DECT and are defined as the reference images. An image prediction framework is created to generate monoenergetic computed tomography (CT) images from kV‐CT images. The accuracy of the images generated by the CNN model is determined by evaluating the mean absolute error (MAE), mean square error (MSE), relative root mean square error (RMSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and mutual information between the synthesized and reference monochromatic CT images. Moreover, the pixel values between the synthetic and reference images are measured and compared using a manually drawn region of interest (ROI).ResultsThe difference in the monoenergetic CT numbers of the ROIs between the synthetic and reference monoenergetic CT images is within the standard deviation values. The MAE, MSE, RMSE, and SSIM are the smallest for the image conversion of 120 kVp to 140 keV. The PSNR is the smallest and the MI is the largest for the synthetic 70 keV image.ConclusionsThe proposed model can act as a suitable alternative to the existing methods for the reconstruction of monoenergetic CT images in DECT from single‐energy CT images.

The Calcium Versus Hemorrhage Trial: Developing Diagnostic Criteria for Chronic Intracranial Susceptibility Lesions Using Single-Energy Computed Tomography, Dual-Energy Computed Tomography, and Quantitative Susceptibility Mapping.

Chronic susceptibility lesions in the brain can be either hemorrhagic (potentially dangerous) or calcific (usually not dangerous) but are difficult to discriminate on routine imaging. We proposed to develop quantitative diagnostic criteria for single-energy computed tomography (SECT), dual-energy computed tomography (DECT), and quantitative susceptibility mapping (QSM) to distinguish hemorrhage from calcium. Patients with positive susceptibility lesions on routine T2*-weighted magnetic resonance of the brain were recruited into this prospective imaging clinical trial, under institutional review board approval and with informed consent. The SECT, DECT, and QSM images were obtained, the lesions were identified, and the regions of interest were defined, with the mean values recorded. Criteria for quantitative interpretation were developed on the first 50 patients, and then applied to the next 45 patients. Contingency tables, scatter plots, and McNemar test were applied to compare classifiers. There were 95 evaluable patients, divided into a training set of 50 patients (328 lesions) and a validation set of 45 patients (281 lesions). We found the following classifiers to best differentiate hemorrhagic from calcific lesions: less than 68 Hounsfield units for SECT, calcium level of less than 15 mg/mL (material decomposition value) for DECT, and greater than 38 ppb for QSM. There was general mutual agreement among the proposed criteria. The proposed criteria outperformed the current published criteria. We provide the updated criteria for the classification of chronic positive susceptibility brain lesions as hemorrhagic versus calcific for each major clinically available imaging modality. These proposed criteria have greater internal consistency than the current criteria and should likely replace it as gold standard.

Diagnostic value of quantitative parameters for myocardial perfusion assessment in patients with suspected coronary artery disease by single- and dual-energy computed tomography myocardial perfusion imaging.

PurposeTo compare performance of visual and quantitative analyses for detecting myocardial ischaemia from single- and dual-energy computed tomography (CT) in patients with suspected coronary artery disease (CAD).MethodsEighty-four patients with suspected CAD were scheduled for dual-energy cardiac CT at rest (CTA) and pharmacological stress (CTP). Myocardial CT perfusion was analysed visually and using three parameters: mean attenuation density (MA), transmural perfusion ratio (TPR) and myocardial perfusion reserve index (MPRI), on both single-energy CT and CT-based iodine images. Significant CAD was defined in AHA-segments by concomitant myocardial hypoperfusion identified visually or quantitatively (parameter < threshold) and coronary stenosis detected by CTA. Single-photon emission CT and invasive coronary angiography were used as reference. Perfusion-parameter cut-off values were calculated in a randomly-selected subgroup of 30 patients.ResultsThe best-performing thresholds for TPR, MPRI and MA were 0.96, 23 and 0.5 for single-energy CT and 0.97, 47 and 0.3 for iodine imaging. For both CT-imaging modalities, TPR yielded the highest area under receiver operating characteristic curve (AUC) (0.99 and 0.97 for single-energy CT and iodine imaging, respectively, in vessel-based analysis) compared to visual analysis, MA and MPRI. Visual interpretation on iodine imaging resulted in higher AUC compared to that on single-energy CT in per-vessel (AUC: 0.93 vs 0.86, respectively) and per-patient (0.94 vs 0.93) analyses.ConclusionTransmural perfusion ratio on both CT-imaging modalities is the best-performing parameter for detecting myocardial ischaemia compared to visual method and other perfusion parameters. Visual analysis on CT-based iodine imaging outperforms that on single-energy CT.

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within [Formula: see text]1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p>0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images.

Overcoming calcium blooming and improving the quantification accuracy of percent area luminal stenosis by material decomposition of multi-energy computed tomography datasets.

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 14mm), and calcification densities (700 to ) 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 to (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%, 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.

Physics-informed Deep Learning for Dual-Energy Computed Tomography Image Processing

Dual-energy CT (DECT) was introduced to address the inability of conventional single-energy computed tomography (SECT) to distinguish materials with similar absorbances but different elemental compositions. However, material decomposition algorithms based purely on the physics of the underlying attenuation process have several limitations, leading to low signal-to-noise ratio (SNR) in the derived material-specific images. To overcome these, we trained a convolutional neural network (CNN) to develop a framework to reconstruct non-contrast SECT images from DECT scans. We show that the traditional physics-based decomposition algorithms do not bring to bear the full information content of the image data. A CNN that leverages the underlying physics of the DECT image generation process as well as the anatomic information gleaned via training with actual images can generate higher fidelity processed DECT images.

Comparison of image quality between split-filter twin beam dual energy and single energy images in abdominal CT

PurposeTo compare the objective and subjective image quality between composed images from split-filter twin beam dual energy (TBDE) and single-energy computed tomography (SECT) in abdominal CT. MethodsIn this prospective study, 103 patients were imaged using TBDE (n = 51) or SECT (n = 52). The CT number and noise were measured for the following six abdominal structures: liver, spleen, fat, muscle, aorta and portal vein. The normalised noise level for the liver was separately measured and compared. The consistency of the SNR and CT number was compared between the two groups. The subjective image quality was evaluated using six aspects in a blinded manner. Cohen’s Kappa statistic was used to determine the level of agreement between the two radiologists. ResultsFor the objective image quality comparison, the SNR of all structures was higher using TBDE compared to SECT (p < 0.05). The CT value for different structures were comparable between the two groups (p > 0.05). Among all patient sizes, the noise level for TBDE images was significantly lower (7–17% reduction) compared to the SECT images (p < 0.01). Furthermore, noise reduction’s magnitude increases with body size. For image quality’s subjective evaluation, TBDE images are superior for certain aspects. Cohen’s Kappa values (0.7634–0.8460) suggest an adequate level of agreement between the two observers. ConclusionsTBDE scan mode can yield similar or even better objective and subjective image quality at the same level of radiation than conventional SECT. Quantitatively, TBDE images have a 7–17% reduction in noise, depending on the size of the scanned body regions.

Improved repeatability of subsolid nodule measurement in low-dose lung screening with monoenergetic images: a phantom study.

To investigate whether monoenergetic images captured with dual-layer spectral computed tomography (CT) can improve the repeatability of subsolid nodule measurement, and whether this approach can further reduce the radiation dose of CT while maintaining its measurement repeatability. An anthropomorphic phantom with simulated subsolid nodules at three different levels was repeatedly scanned with both conventional single-energy CT and dual-layer spectral CT. A proxy for the measurement repeatability in the National Lung Screening Trial (proxy for NLST) was calculated with the typical CT protocol used in NLST. Using the dual-layer spectral CT, monoenergetic images of 40 to 110 keV, with an interval of 10 keV, were generated. The average diameter and volume of a total of 15,120 nodules in 840 CT images were measured by using a commercially-available computer-aided detection (CAD) system. The repeatability coefficient (RC), %RC, and 95% confidence intervals (CIs) of each image set were calculated and compared. At the same tube voltage and tube current-time product, monoenergetic images resulted in significantly lower RC than the proxy for NLST, indicating that measurement repeatability was enhanced. When the radiation dose was lowered by 30% or 55%, monoenergetic images showed significantly lower RC at high-energy keV than the proxy for NLST. The estimated measurement repeatability from monoenergetic images with 30% or 55% lower radiation dose was comparable to the repeatability from conventional single-energy CT images with standard radiation dose and iterative reconstruction. Monoenergetic images captured by using dual-layer spectral CT can improve the repeatability of subsolid nodule measurement. The use of monoenergetic images would allow lung cancer screening with a lower radiation dose, while maintaining comparable measurement repeatability.

Structure delineation in the presence of metal - A comparative phantom study using single and dual-energy computed tomography with and without metal artefact reduction.

Background and purposeMetal artefacts in computed tomography (CT) images impairs structure delineation. These artefacts can potentially be reduced with dual-energy CT (DECT) with or without using metal artefact reduction (MAR). The purpose was to investigate how structure delineation in DECT with or without MAR and single-energy CT (SECT) images were affected by metals.Materials and methodsA phantom with known irregular structures was developed. Reference structures were determined from a low-noise scan without metal. Bilateral hip prostheses were simulated with steel or titanium inserts. The phantom was scanned with SECT and fast-kV switching DECT with optional MAR. Four radiation oncologists delineated the structures in two phantom set-ups. Delineated structures were evaluated with Dice similarity coefficient (DSC) and Hausdorff distance relative to the reference structures.ResultsWith titanium inserts, more structures were detected for non-MAR DECT compared to SECT while the same or less were detected with steel inserts. MAR improved delineation in DECT images. For steel inserts, three structures in the region of artefacts, were delineated by at least two oncologists with MAR-DECT compared to none with non-MAR DECT or SECT. The highest values of DSC for MAR-DECT were 0.69, 0.81 and 0.77 for those structures.ConclusionsDelineation was improved with non-MAR DECT compared to SECT, especially for titanium inserts. A larger improvement was seen with the use of MAR for both steel and titanium inserts. The improvement was dependent on the location of the structure relative to the inserts, and the structure contrast relative to the background.

Marrow Adipose Tissue in Older Men: Association with Visceral and Subcutaneous Fat, Bone Volume, Metabolism, and Inflammation.

Marrow (MAT) and subcutaneous (SAT) adipose tissues display different metabolic profiles and varying associations with aging, bone density, and fracture risk. Using a non-invasive imaging methodology, we aimed to investigate the associations between MAT, SAT, and visceral fat (VAT) with bone volume, bone remodeling markers, insulin resistance, and circulating inflammatory mediators in a population of older men. In this cross-sectional study, 96 healthy men (mean age 67 ± 5.5) were assessed for anthropometric parameters, body composition, serum biochemistry, and inflammatory panel. Using single-energy computed tomography images, MAT (in L2 and L3 and both hips), VAT, and SAT (at the level of L2-L3 and L4-L5) were measured employing Slice-O-Matic software (Tomovision), which enables specific tissue demarcation applying previously reported Hounsfield unit thresholds. MAT volume was similar in all anatomical sites and independent of BMI. In all femoral regions of interest (ROIs) there was a strong negative association between bone and MAT volumes (r=-0.840 to -0.972, p < 0.001), with location-dependent variations in the lumbar spine. Unlike VAT and SAT, no associations between MAT and serum glucose, inflammatory markers or insulin resistance indicators were found. Bone decline occurred without red marrow expansion; thus lost bone was mainly (if not exclusively) replaced by MAT. In conclusion, strong inverse correlations between MAT and bone mass, which have been previously observed in women, were also confirmed in older men. However, MAT volume in all ROIs was interrelated and unlike women, mainly independent of VAT or SAT. The lack of strong association between MAT vs VAT/SAT, and its discordant associations with metabolic and inflammatory mediators provide further evidence on MAT's distinct attributes in older men.