Quantitative imaging of element composition and mass fraction using dual-energy CT: three-material decomposition

Real-time diagnosis of intracerebral hemorrhage by generating dual-energy CT from single-energy CT

Real-time diagnosis of intracerebral hemorrhage after thrombectomy is crucial for follow-up treatment. However, this is difficult to achieve with standard single-energy CT (SECT) due to similar CT values of blood and contrast agents under a single energy spectrum. In contrast, dual-energy CT (DECT) scanners employ two different energy spectra, which allows for real-time differentiation between hemorrhage and contrast extravasation based on energy-related attenuation characteristics. Unfortunately, DECT scanners are not as widely used as SECT scanners due to their high costs. To address this dilemma, in this paper, we generate pseudo DECT images from a SECT image for real-time diagnosis of hemorrhage. More specifically, we propose a SECT-to-DECT Transformer-based Generative Adversarial Network (SDTGAN), which is a 3D transformer-based multi-task learning framework equipped with a shared attention mechanism. In this way, SDTGAN can be guided to focus more on high-density areas (crucial for hemorrhage diagnosis) during the generation. Meanwhile, the introduced multi-task learning strategy and the shared attention mechanism also enable SDTGAN to model dependencies between interconnected generation tasks, improving generation performance while significantly reducing model parameters and computational complexity. In the experiments, we approximate real SECT images using mixed 120kV images from DECT data to address the issue of not being able to obtain the true paired DECT and SECT data. Extensive experiments demonstrate that SDTGAN can generate DECT images better than state-of-the-art methods. The code of our implementation is available at https://github.com/jiang-cw/SDTGAN.

An Image-Based Prior Knowledge-Free Approach for a Multi-Material Decomposition in Photon-Counting Computed Tomography

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

Imaging the pulmonary vasculature in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

Quantitative evaluation of disc degeneration using dual-energy CT: advantages of R-VH, D-VH values and the IVNCa + CT model

OBJECTIVE

To evaluate the correlation between dual-energy CT (DECT) virtual calcium free (VNCA), CT attenuation, the ratio and difference of VNCA to CT attenuation, and Pfirrmann grading of lumbar disc degeneration.

METHODS

A retrospective analysis on 135 intervertebral discs from 30 patients who underwent DECT and MR. Discs was graded using the Pfirrmann system. ROIs on the sagittal plane assessed HU value, VNCA value, Rho value, Z value, R-VH value, and D-VH value. Correlation, grade differences, and multivariate regression models were assessed. Diagnostic performance and cut-off values were determined using AUC.

RESULTS

VNCA (r = 0.589, P < 0.001), R-VH (r = 0.622, P < 0.001), and D-VH (r = 0.613, P < 0.001) moderately correlated with Pfirrmann grading. HU (r = 0.388, P < 0.001), Rho (r = 0.142, P = 0.102), and Z (r = -0.125, P = 0.153) showed a weak correlation. R-VH, D-VH, and VNCA had significantly higher correlation than HU. Statistically significant differences were observed in P values of VNCA, HU, R-VH, and D-VH in relative groups (P < 0.05), but not in Rho and Z values (P > 0.05). R-VH and D-VH had significant differences between Pfirrmann grades 1 and 2, and grades 2 and 3 (early stage) (P < 0.05). AUC readings of R-VH and D-VH (≥2,  ≥3, ≥4) were higher. The multivariate model IVNCa + CT had the highest AUC.

CONCLUSION

The new quantitative indices R-VH value and D-VH value of DECT have advantages over VNCA value and HU value in evaluating early-stage disc degeneration (≥2 grades, ≥3 grades). The multivariate model IVNCa + CT has the best AUC values for evaluating disc degeneration at all stages.

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Edge-illumination spectral phase-contrast tomography

Following the rapid, but independent, diffusion of x-ray spectral and phase-contrast systems, this work demonstrates the first combination of spectral and phase-contrast computed tomography (CT) obtained by using the edge-illumination technique and a CdTe small-pixel (62μm) spectral detector. A theoretical model is introduced, starting from a standard attenuation-based spectral decomposition and leading to spectral phase-contrast material decomposition. Each step of the model is followed by quantification of accuracy and sensitivity on experimental data of a test phantom containing different solutions with known concentrations. An example of a micro CT application (20μm voxel size) on an iodine-perfusedex vivomurine model is reported. The work demonstrates that spectral-phase contrast combines the advantages of spectral imaging, i.e. high-Zmaterial discrimination capability, and phase-contrast imaging, i.e. soft tissue sensitivity, yielding simultaneously mass density maps of water, calcium, and iodine with an accuracy of 1.1%, 3.5%, and 1.9% (root mean square errors), respectively. Results also show a 9-fold increase in the signal-to-noise ratio of the water channel when compared to standard spectral decomposition. The application to the murine model revealed the potential of the technique in the simultaneous 3D visualization of soft tissue, bone, and vasculature. While being implemented by using a broad spectrum (pink beam) at a synchrotron radiation facility (Elettra, Trieste, Italy), the proposed experimental setup can be readily translated to compact laboratory systems including conventional x-ray tubes.

Dual-Energy Computed Tomography in Detecting and Predicting Lymph Node Metastasis in Malignant Tumor Patients: A Comprehensive Review

The accurate and timely assessment of lymph node involvement is paramount in the management of patients with malignant tumors, owing to its direct correlation with cancer staging, therapeutic strategy formulation, and prognostication. Dual-energy computed tomography (DECT), as a burgeoning imaging modality, has shown promising results in the diagnosis and prediction of preoperative metastatic lymph nodes in recent years. This article aims to explore the application of DECT in identifying metastatic lymph nodes (LNs) across various cancer types, including but not limited to thyroid carcinoma (focusing on papillary thyroid carcinoma), lung cancer, and colorectal cancer. Through this narrative review, we aim to elucidate the clinical relevance and utility of DECT in the detection and predictive assessment of lymph node metastasis in malignant tumors, thereby contributing to the broader academic discourse in oncologic radiology and diagnostic precision.

Quantitative multi-energy micro-CT: A simulation and phantom study for simultaneous imaging of four different contrast materials using an energy integrating detector

Emerging from the development of single-energy Computed Tomography (CT) and Dual-Energy Computed Tomography, Multi-Energy Computed Tomography (MECT) is a promising tool allowing advanced material and tissue decomposition and thereby enabling the use of multiple contrast materials in preclinical research. The scope of this work was to evaluate whether a usual preclinical micro-CT system is applicable for the decomposition of different materials using MECT together with a matrix-inversion method and how different changes of the measurement-environment affect the results. A matrix-inversion based algorithm to differentiate up to five materials (iodine, iron, barium, gadolinium, residual material) by applying four different acceleration voltages/energy levels was established. We carried out simulations using different ratios and concentrations (given in fractions of volume units, VU) of the four different materials (plus residual material) at different noise-levels for 30 keV, 40 keV, 50 keV, 60 keV, 80 keV and 100 keV (monochromatic). Our simulation results were then confirmed by using region of interest-based measurements in a phantom-study at corresponding acceleration voltages. Therefore, different mixtures of contrast materials were scanned using a micro-CT. Voxel wise evaluation of the phantom imaging data was conducted to confirm its usability for future imaging applications and to estimate the influence of varying noise-levels, scattering, artifacts and concentrations. The analysis of our simulations showed the smallest deviation of 0.01 (0.003-0.15) VU between given and calculated concentrations of the different contrast materials when using an energy-combination of 30 keV, 40 keV, 50 keV and 100 keV for MECT. Subsequent MECT phantom measurements, however, revealed a combination of acceleration voltages of 30 kV, 40 kV, 60 kV and 100 kV as most effective for performing material decomposition with a deviation of 0.28 (0-1.07) mg/ml. The feasibility of our voxelwise analyses using the proposed algorithm was then confirmed by the generation of phantom parameter-maps that matched the known contrast material concentrations. The results were mostly influenced by the noise-level and the concentrations used in the phantoms. MECT using a standard micro-CT combined with a matrix inversion method is feasible at four different imaging energies and allows the differentiation of mixtures of up to four contrast materials plus an additional residual material.

Prototyping optimization-based image reconstructions from limited-angular-range data in dual-energy CT

Image reconstruction from data collected over full-angular range (FAR) in dual-energy CT (DECT) is well-studied. There exists interest in DECT with advanced scan configurations in which data are collected only over limited-angular ranges (LARs) for meeting unique workflow needs in certain practical imaging applications, and thus in the algorithm development for image reconstruction from such LAR data. The objective of the work is to investigate and prototype image reconstructions in DECT with LAR scans. We investigate and prototype optimization programs with various designs of constraints on the directional-total-variations (DTVs) of virtual monochromatic images and/or basis images, and derive the DTV algorithms to numerically solve the optimization programs for achieving accurate image reconstruction from data collected in a slew of different LAR scans. Using simulated and real data acquired with low- and high-kV spectra over LARs, we conduct quantitative studies to demonstrate and evaluate the optimization programs and their DTV algorithms developed. As the results of the numerical studies reveal, while the DTV algorithms yield images of visual quality and quantitative accuracy comparable to that of the existing algorithms from FAR data, the former reconstruct images with improved visualization, reduced artifacts, and also enhanced quantitative accuracy when applied to LAR data in DECT. Optimization-based, one-step algorithms, including the DTV algorithms demonstrated, can be developed for quantitative image reconstruction from spectral data collected over LARs of extents that are considerably smaller than the FAR in DECT. The theoretical and numerical results obtained can be exploited for prototyping designs of optimization-based reconstructions and LAR scans in DECT, and they may also yield insights into the development of reconstruction procedures in practical DECT applications. The approach and algorithms developed can naturally be applied to investigating image reconstruction from LAR data in multi-spectral and photon-counting CT.

Technical note: Error analysis of material-decomposition-based effective atomic number quantification method

BACKGROUND

The effective atomic number (Zeff ) is widely applied to the identification of unknown materials. One method to determine the Zeff is material-decomposition-based spectral X-ray imaging. The method relies on certain approximations of the X-ray interaction cross-sections such as empirical model coefficients. The impact of such approximations on the accuracy of Zeff quantification has not been fully investigated.

PURPOSE

To perform an error analysis of the material-decomposition-based Zeff quantification method and propose a coefficient calibration-in-groups method to improve the modeling accuracy and reduce the Zeff quantification error.

METHODS

The model of the material-decomposition-based Zeff quantification method relies on the dependence of the interaction cross-sections  (σPE ) on the atomic number Z and corresponding coefficient, that is,σ PE ∝ Z m $\sigma _\mathrm{PE}\propto Z^m$ . In this work, all the data is from the National Institute of Standards and Technology (NIST) website. First, the coefficient m is calibrated through a logarithmic fitting method to quickly determine the m values for any certain energy and Zeff ranges. Then materials including elements and common compounds with Zeff ranging from 6-20 are selected as the objects whose effective atomic numbers are to be quantified. Different combinations of basis materials are applied to decompose the object materials and their quantification errors are analyzed. With the help of error analysis, the object materials are divided into high-error and low-error groups based on the decomposition coefficient ratioa m i n / a m a x $a_{min}/a_{max}$ , which is found to have a strong correlation with error, and their coefficients are calibrated in groups. Finally, the average errors of three m selection strategies: (1) using an empirical m value of 3.94, which is also considered a standard method; (2) using a single m value, which is calibrated through the logarithmic fitting method; (3) using different m values calibrated in groups, are calculated to test the effectiveness of our method.

RESULTS

The approximation of the X-ray interaction cross-section leads to certain errors in Zeff quantification and the error distributions for different basis materials are different. The average errors for most basis material combinations (C(6)/Ca(20), C(6)/Al(13), Al(13)/Ca(20), C(6)/Ne(10), Na(11)/P(15)) are lower than 0.5, maintaining good average accuracy. While the average error for S(16)/Ca(20) is up to 0.8461, leading to more misjudgments on atomic number. Meanwhile, the error distribution regularity can be observed. The Pearson's correlation coefficients of absolute errors and decomposition coefficient ratios are 0.743, 0.8432 and 0.7126 for basis material combinations C(6)/Ca(20), C(6)/Al(13) and Al(13)/Ca(20), indicating a good correlation. The method using either empirical m value of 3.94 or single calibrated m value of 4.619 has relatively high average errors. The proposed method using different m values calibrated in groups has the lowest average errors 0.254, 0.203 and 0.169, which are reduced by 21.6%(0.07), 3.8%(0.008) and 62.9%(0.286) respectively compared with the standard method.

CONCLUSIONS

The error analysis demonstrates that the approximation of X-ray interaction cross-sections leads to inevitable errors, while also revealing certain error distribution regularity. The coefficient calibrated-in-groups method has better modeling accuracy and has effectively reduced the error compared with the standard method using a single empirical m value of 3.94.

Material decomposition using dual-energy CT with unsupervised learning

Material decomposition (MD) is an application of dual-energy computed tomography (DECT) that decomposes DECT images into specific material images. However, the direct inversion method used in MD often amplifies noise in the decomposed material images, resulting in lower image quality. To address this issue, we propose an image-domain MD method based on the concept of deep image prior (DIP). DIP is an unsupervised learning method that can perform different tasks without using a large training dataset with known targets (i.e., basis material images). We retrospectively recruited patients who underwent non-contrast brain DECT scans and investigated the feasibility of using the proposed DIP-based method to decompose DECT images into two (i.e., bone and soft tissue) and three (i.e., bone, soft tissue, and fat) basis materials. We evaluated the decomposed material images in terms of signal-to-noise ratio (SNR) and modulation transfer function (MTF). The proposed DIP-based method showed greater improvement in SNR in the decomposed soft-tissue images compared to the direct inversion method and the iterative method. Moreover, the proposed method produced similar MTF curves in both two- and three-material decompositions. Additionally, the proposed DIP-based method demonstrated better separation ability than the other two studied methods in the case of three-material decomposition. Our results suggest that the proposed DIP-based method is capable of unsupervisedly generating high-quality basis material images from DECT images.

Dual-Energy Computed Tomography: Technological Considerations

Compared to conventional single-energy CT (SECT), dual-energy CT (DECT) provides additional information to better characterize imaged tissues. Approaches to DECT acquisition vary by vendor and include source-based and detector-based systems, each with its own advantages and disadvantages. Despite the different approaches to DECT acquisition, the most utilized DECT images include routine SECT equivalent, virtual monoenergetic, material density (eg, iodine map), and virtual non-contrast images. These images are generated either through reconstructions in the projection or image domains. Designing and implementing an optimal DECT workflow into routine clinical practice depends on radiologist and technologist input with special considerations including appropriate patient and protocol selection and workflow automation. In addition to better tissue characterization, DECT provides numerous advantages over SECT such as the characterization of incidental findings and dose reduction in radiation and iodinated contrast.

Dual-Energy Computed Tomography Applications in Lung Cancer

This article examines the intrathoracic applications for dual-energy computed tomography (DECT), focusing on lung cancer. The topics covered include the image data sets, methods for iodine quantification, and clinical applications. The applications of DECT are to differentiate benign and malignant lung nodules, determining the grade of lung cancer and expression of ki-67 expression. Iodine quantification has role in assessment of treatment response in both the primary tumor and nodal metastases.

Calcium scoring on coronary computed angiography tomography with photon-counting detector technology: Predictors of performance

INTRODUCTION

Obtaining accurate coronary artery calcium (CAC) score measurements from CCTA datasets with virtual non-iodine (VNI) algorithms would reduce acquisition time and radiation dose. We aimed to assess the agreement of VNI-derived and conventional true non-contrast (TNC)-based CAC scores and to identify the predictors of accuracy.

METHODS

CCTA datasets were acquired with either 120 or 140 ​kVp. CAC scores and volumes were calculated from TNC and VNI images in 197 consecutive patients undergoing CCTA. CAC density score, mean volume/lesion, aortic Hounsfield units and standard deviations were then measured. Finally, percentage deviation (VNI - TNC/TNC∗100) of CTA-derived CAC scores from non-enhanced scans was calculated for each patient. Predictors (including anthropometric and acquisition parameters, as well as CAC characteristics) of the degree of discrepancy were evaluated using linear regression analysis.

RESULTS

While the agreement between TNC and VNI was substantial (mean bias, 6.6; limits of agreement, 178.5/145.3), a non-negligible proportion of patients (36/197, 18.3%) were falsely reclassified as CAC score ​= ​0 on VNI. The use of higher tube voltage significantly decreased the percentage deviation relative to TNC-based values (β ​= ​-0.21 [95%CI: 0.38 to -0.03], p ​= ​0.020) and a higher CAC density score also proved to be an independent predictor of a smaller difference (β ​= ​-0.22 [95%CI: 0.37 to -0.07], p ​= ​0.006).

CONCLUSION

The performance of VNI-based calcium scoring may be improved by increased tube voltage protocols, while the accuracy may be compromised for calcified lesions of lower density. The implementation of VNI in clinical routine, however, needs to be preceded by a solution for detecting smaller lesions as well.

Comparison of true non-contrast and virtual non-contrast images in the characterization of renal lesions using detector-based spectral CT

OBJECTIVES

Renal lesions are sometimes incidentally detected during computed tomography (CT) examinations in which an unenhanced series is not included, preventing the lesions from being fully characterized. The aim of this study was to investigate the feasibility to use virtual non-contrast (VNC) images, acquired using a detector-based dual-energy CT, for the characterization of renal lesions.

METHODS

Twenty-seven patients (12 women) underwent a renal CT scan, including a non-contrast, an arterial, and a venous phase contrast-enhanced series, using a detector-based dual-energy CT scanner. VNC images were reconstructed from the venous contrast-enhanced series. The mean attenuation values of 65 renal lesions in both the VNC and true non-contrast (TNC) images were measured and compared quantitatively. Three radiologists blindly assessed all lesions using either VNC or TNC images in combination with contrast-enhanced images.

RESULTS

Sixteen patients had cystic lesions, five had angiomyolipoma (AML), and six had suspected renal cell carcinomas (RCC). Attenuation values in VNC and TNC images were strongly correlated (ρ = 0.7, mean difference -6.0 ± 13 HU). The largest differences were found for unenhanced high-attenuation lesions. Radiologists classified 86% of the lesions correctly using VNC images.

CONCLUSIONS

In 70% of the patients, incidentally detected renal lesions could be accurately characterized using VNC images, resulting in less patient burden and a reduction in radiation exposure.

ADVANCES IN KNOWLEDGE

This study shows that renal lesions can be accurately characterized using VNC images acquired by detector-based dual-energy CT, which is in agreement with previous studies using dual-source and rapid X-ray tube potential switching technique.

A Dual-Energy Metal Artifact Redcution Method for DECT Image Reconstruction

Metal implants are one of the culprits for image quality degradation in CT imaging, introducing so-called metal artifacts. With the help of the virtual-monochromatic imaging technique, dual-energy CT has been proven to be effective in metal artifact reduction. However, the virtual monochromatic images with suppressed metal artifacts show reduced CNR compared to polychromatic images. To remove metal artifacts on polychromatic images, we propose a dual-energy NMAR (deNMAR) algorithm in this paper that adds material decomposition to the widely used NMAR framework. The dual energy sinograms are first decomposed into water and bone sinograms, and metal regions are replaced with water on the reconstructed material maps. Prior sinograms are constructed by polyenergetically forward projecting the material maps with corresponding spectra, and they are used to guide metal trace interpolation in the same way as in the NMAR algorithm. We performed experiments on authentic human body phantoms, and the results show that the proposed deNMAR algorithm achieves better performance in tissue restoration compared to other compelling methods. Tissue boundaries become clear around metal implants, and CNR rises to 2.58 from ~1.70 on 80 kV images compared to other dual-energy-based algorithms.

Dual-Energy CT in Cardiothoracic Imaging: Current Developments

This article describes the technical principles and clinical applications of dual-energy computed tomography (DECT) in the context of cardiothoracic imaging with a focus on current developments and techniques. Since the introduction of DECT, different vendors developed distinct hard and software approaches for generating multi-energy datasets and multiple DECT applications that were developed and clinically investigated for different fields of interest. Benefits for various clinical settings, such as oncology, trauma and emergency radiology, as well as musculoskeletal and cardiovascular imaging, were recently reported in the literature. State-of-the-art applications, such as virtual monoenergetic imaging (VMI), material decomposition, perfused blood volume imaging, virtual non-contrast imaging (VNC), plaque removal, and virtual non-calcium (VNCa) imaging, can significantly improve cardiothoracic CT image workflows and have a high potential for improvement of diagnostic accuracy and patient safety.

Quantification of spinal bone marrow fat fraction using three-material decomposition technique on dual-energy CT: A phantom study

BACKGROUND

Two-material decomposition is insufficient to quantify the fat fraction of spinal bone marrow, which is comprised of a mixture of bone minerals, water, and yellow marrow (fat).

PURPOSE

To develop an accurate three-material decomposition-based bone marrow fat fraction ( F F 3 M D $F{F_{3MD}}$ ) quantification technique for dual-energy CT.

METHODS

Bone marrow edema phantoms containing trabecular bone minerals, water, and fat were constructed using fat fractions and bone mineral density values matching those expected in healthy and edematous bone, and scanned on a commercial dual-energy CT. Fat quantified by F F 3 M D $F{F_{3MD}}$ were compared to MRI-based fat fraction ( F F M R I $F{F_{MRI}}$ ) and conventional two-material-decomposition-based fat fraction ( F F 2 M D $F{F_{2MD}}$ ) to evaluate its accuracy and dependency on various bone mineral densities.

RESULTS

F F 3 M D $F{F_{3MD}}$ demonstrated an excellent correlation with F F M R I $F{F_{MRI}}\;$ (r = 0.97, R²  = 0.96) in the phantom, significantly more accurate than FF_2MD when confounding bone minerals are present (50 mg/cm³ : r = 1.02, R²  = 0.95 vs. r = 0.65, R²  = 0.79 (p < 0.01); 100 mg/cm³ : r = 0.81, R²  = 0.47 vs. r = 0.21, R²  = 0.21 (p < 0.05)).

CONCLUSIONS

F F 3 M D $F{F_{3MD}}$ accurately quantified bone marrow fat fraction, when compared with F F M R I $F{F_{MRI}}$ , in the specially constructed bone marrow phantom.

Dual Energy-Derived Metrics for Differentiating Adrenal Adenomas From Nonadenomas on Single-Phase Contrast-Enhanced CT

BACKGROUND. Adrenal masses are often indeterminate on single-phase postcontrast CT. Dual-energy CT (DECT) with three-material decomposition algorithms may aid characterization. OBJECTIVE. The purpose of this study was to compare the diagnostic performance of metrics derived from portal venous phase DECT, including virtual noncontrast (VNC) attenuation, fat fraction, iodine density, and relative enhancement ratio, for characterizing adrenal masses. METHODS. This retrospective study included 128 patients (82 women, 46 men; mean age, 64.6 ± 12.7 [SD] years) who between January 2016 and December 2019 underwent portal venous phase abdominopelvic DECT that showed a total of 139 adrenal lesions with an available reference standard based on all imaging, clinical, and pathologic records (87 adenomas, 52 nonadenomas [48 metastases, two adrenal cortical carcinomas, one ganglioneuroma, one hematoma]). Two radiologists placed ROIs to determine the following characteristics of the masses: VNC attenuation, fat fraction, iodine density normalized to portal vein, and for masses with VNC greater than 10 HU, relative enhancement ratio (ratio of portal venous phase attenuation to VNC attenuation). Readers' mean measurements were used for ROC analyses, and clinically optimal thresholds were derived as thresholds yielding the highest sensitivity at 100% specificity. RESULTS. Adenomas and nonadenomas were significantly different (all p < .001) in VNC attenuation (mean ± SD, 18.5 ± 12.9 vs 34.1 ± 8.9 HU), fat fraction (mean ± SD, 24.3% ± 8.2% vs 14.2% ± 5.6%), normalized iodine density (mean ± SD, 0.34 ± 0.15 vs 0.17 ± 0.17), and relative enhancement ratio (mean ± SD, 186% ± 96% vs 58% ± 59%). AUCs for all metrics ranged from 0.81 through 0.91. The metric with highest sensitivity for adenoma at the clinically optimal threshold (i.e., 100% specificity) was fat fraction (threshold, ≥ 23.8%; sensitivity, 59% [95% CI, 48-69%]) followed by VNC attenuation (≤ 15.2 HU; sensitivity, 39% [95% CI, 29-50%]), relative enhancement ratio (≥ 214%; sensitivity, 37% [95% CI, 25-50%]), and normalized iodine density (≥ 0.90; sensitivity, 1% (95% CI, 0-60%]). VNC attenuation at the traditional true noncontrast attenuation threshold of 10 HU or lower had sensitivity of 28% (95% CI, 19-38%) and 100% specificity. Presence of fat fraction 23.8% or greater or relative enhancement ratio 214% or greater yielded sensitivity of 68% (95% CI, 57-77%) with 100% specificity. CONCLUSION. For adrenal lesions evaluated with single-phase DECT, fat fraction had higher sensitivity than VNC attenuation at both the clinically optimal threshold and the traditional threshold of 10 HU or lower. CLINICAL IMPACT. By helping to definitively diagnose adenomas, DECT-derived metrics can help avoid downstream imaging for incidental adrenal lesions.

Iodine maps derived from sparse-view kV-switching dual-energy CT equipped with a deep learning reconstruction for diagnosis of hepatocellular carcinoma

Deep learning-based spectral CT imaging (DL-SCTI) is a novel type of fast kilovolt-switching dual-energy CT equipped with a cascaded deep-learning reconstruction which completes the views missing in the sinogram space and improves the image quality in the image space because it uses deep convolutional neural networks trained on fully sampled dual-energy data acquired via dual kV rotations. We investigated the clinical utility of iodine maps generated from DL-SCTI scans for assessing hepatocellular carcinoma (HCC). In the clinical study, dynamic DL-SCTI scans (tube voltage 135 and 80 kV) were acquired in 52 patients with hypervascular HCCs whose vascularity was confirmed by CT during hepatic arteriography. Virtual monochromatic 70 keV images served as the reference images. Iodine maps were reconstructed using three-material decomposition (fat, healthy liver tissue, iodine). A radiologist calculated the contrast-to-noise ratio (CNR) during the hepatic arterial phase (CNR_a) and the equilibrium phase (CNR_e). In the phantom study, DL-SCTI scans (tube voltage 135 and 80 kV) were acquired to assess the accuracy of iodine maps; the iodine concentration was known. The CNR_a was significantly higher on the iodine maps than on 70 keV images (p < 0.01). The CNR_e was significantly higher on 70 keV images than on iodine maps (p < 0.01). The estimated iodine concentration derived from DL-SCTI scans in the phantom study was highly correlated with the known iodine concentration. It was underestimated in small-diameter modules and in large-diameter modules with an iodine concentration of less than 2.0 mgI/ml. Iodine maps generated from DL-SCTI scans can improve the CNR for HCCs during hepatic arterial phase but not during equilibrium phase in comparison with virtual monochromatic 70 keV images. Also, when the lesion is small or the iodine concentration is low, iodine quantification may result in underestimation.

Photon-Counting Computed Tomography (PCCT): Technical Background and Cardio-Vascular Applications

Photon-counting computed tomography (PCCT) is a new advanced imaging technique that is going to transform the standard clinical use of computed tomography (CT) imaging. Photon-counting detectors resolve the number of photons and the incident X-ray energy spectrum into multiple energy bins. Compared with conventional CT technology, PCCT offers the advantages of improved spatial and contrast resolution, reduction of image noise and artifacts, reduced radiation exposure, and multi-energy/multi-parametric imaging based on the atomic properties of tissues, with the consequent possibility to use different contrast agents and improve quantitative imaging. This narrative review first briefly describes the technical principles and the benefits of photon-counting CT and then provides a synthetic outline of the current literature on its use for vascular imaging.

Image-spectral decomposition extended-learning assisted by sparsity for multi-energy computed tomography reconstruction

Background

Multi-energy computed tomography (CT) provides multiple channel-wise reconstructed images, and they can be used for material identification and k-edge imaging. Nonetheless, the projection datasets are frequently corrupted by various noises (e.g., electronic, Poisson) in the acquisition process, resulting in lower signal-noise-ratio (SNR) measurements. Multi-energy CT images have local sparsity, nonlocal self-similarity in spatial dimension, and correlation in spectral dimension.

Methods

In this paper, we propose an image-spectral decomposition extended-learning assisted by sparsity (IDEAS) method to fully exploit these intrinsic priors for multi-energy CT image reconstruction. Particularly, a nonlocal low-rank Tucker decomposition (TD) is employed to utilize the correlation and nonlocal self-similarity priors. Moreover, considering the advantages of multi-task tensor dictionary learning (TDL) in sparse representation, an adaptive spatial dictionary and an adaptive spectral dictionary are trained during the iterative reconstruction process. Furthermore, a weighted total variation (TV) regularization term is employed to encourage local sparsity.

Results

Numerical simulation, physical phantom, and preclinical mouse experiments are performed to validate the proposed IDEAS algorithm. Specifically, in the simulation experiments, the proposed IDEAS reconstructed high-quality images that are very close to the references. For example, the root mean square error (RMSE) of IDEAS image in energy bin 1 is as low as 0.0672, while the RMSE of other methods are higher than 0.0843. Besides, the structural similarity (SSIM) of IDEAS reconstructed image in energy bin 1 is greater than 0.98. For material decomposition, the RMSE of IDEAS bone component is as low as 0.0152, and other methods are higher than 0.0199. In addition, the computational cost of IDEAS is as low as 98.8 s for one iteration, and the competing tensor decomposition method is higher than 327 s.

Conclusions

To further improve the quality of the reconstructed multi-energy CT images, multiple prior regularizations are introduced to the multi-energy CT reconstructed model, leading to an IDEAS method. Both qualitative and quantitative evaluation of our results confirm the outstanding performance of the proposed algorithm compared to the state-of-the-arts.

GATE/Geant4-based dosimetry for ex vivo in solution irradiation of blood with radionuclides

To establish a dose-response relationship between radiation-induced DNA damage and the corresponding absorbed doses in blood irradiated with radionuclides in solution under ex vivo conditions, the absorbed dose coefficient for 1 ml for 1 h internal ex vivo irradiation of peripheral blood (d_Blood) must be determined. d_Blood is specific for each radionuclide, and it depends on the irradiation geometry. Therefore, the aim of this study is to use the Monte Carlo radiation transport code GATE/Geant4 to calculate the mean absorbed dose rates for ex vivo irradiation of blood with several radionuclides used in Nuclear Medicine.

METHODS

The Monte Carlo simulation reproduces the irradiation geometry of a blood sample of 7 ml mixed with 1 ml of a water equivalent radioactive solution in an 8 ml vial. The simulation was performed for ten different radionuclides: ¹⁸F, ⁶⁸Ga, ⁹⁰Y, ^99mTc, ¹²³I, ¹²⁴I, ¹³¹I, ¹⁷⁷Lu, ²²³Ra, and ²²⁵Ac. Two sets of simulations for each radionuclide were performed with 1x10⁹ histories. The first set was simulated with a mass density of 1.0525 g/cm³ of the blood plus water mixture. The second set of simulations was performed with a mass density of 1 g/cm³ for comparison with previous studies.

RESULTS

The values of d_Blood for ten radionuclides were calculated. The values range from 10.23 mGy∙ml∙MBq^-1 for ^99mTc to 15632.02 mGy∙ml∙MBq^-1 for ²²⁵Ac. The maximum relative change compared to previous studies was 13.0% for ¹²⁴I.

CONCLUSION

This study provides a comprehensive set of absorbed dose coefficients for 1 ml for 1 h internal ex vivo irradiation of peripheral blood in a special vial geometry and radionuclides typically used in Nuclear Medicine. Furthermore, the method proposed by this work can be easily adapted to a variety of internal irradiation conditions and serve as a reference for future studies.

Dual-Energy Computed Tomography-Based Iodine Concentration Estimation for Evaluating Choroidal Malignant Melanoma Response to Treatment: Optimization and Primary Validation

Contrast-enhanced imaging for choroidal malignant melanoma (CMM) is mostly limited to detecting metastatic tumors, possibly due to difficulties in fixing the eye position. We aimed to (1) validate the appropriateness of estimating iodine concentration based on dual-energy computed tomography (DECT) for CMM and optimize the calculation parameters for estimation, and (2) perform a primary clinical validation by assessing the ability of this technique to show changes in CMM after charged-particle radiation therapy. The accuracy of the optimized estimate (eIC_optimized) was compared to an estimate obtained by commercial software (eIC_commercial) by determining the difference from the ground truth. Then, eIC_optimized, tumor volume, and CT values (80 kVp, 140 kVp, and synthesized 120 kVp) were measured at pre-treatment and 3 months and 1.5−2 years after treatment. The difference from the ground truth was significantly smaller in eIC_optimized than in eIC_commercial (p < 0.01). Tumor volume, CT values, and eIC_optimized all decreased significantly at 1.5−2 years after treatment, but only eIC_commercial showed a significant reduction at 3 months after treatment (p < 0.01). eIC_optimized can quantify contrast enhancement in primary CMM lesions and has high sensitivity for detecting the response to charged-particle radiation therapy, making it potentially useful for treatment monitoring.

Triple-source saddle-curve cone-beam photon counting CT image reconstruction: A simulation study

PURPOSE

The most common detector material in the PC CT system, cannot achieve the best performance at a relatively higher photon flux rate. In the reconstruction view, the most commonly used filtered back projection, is not able to provide sufficient reconstructed image quality in spectral computed tomography (CT). Developing a triple-source saddle-curve cone-beam photon counting CT image reconstruction method can improve the temporal resolution.

METHODS

Triple-source saddle-curve cone-beam trajectory was rearranged into four trajectory sets for simulation and reconstruction. Projection images in different energy bins were simulated by forward projection and photon counting CT respond model simulation. After simulation, the object was reconstructed using Katsevich's theory after photon counts correction using the pseudo inverse of photon counting CT response matrix. The material decomposition can be performed based on images in different energy bins.

RESULTS

Root mean square error (RMSE) and structural similarity index (SSIM) are calculated to quantify the image quality of reconstruction images. Compared with FDK images, the RMSE for the triple-source image was improved by 27%, 21%, 14%, 8%, and 6% for the reconstrued image of 20-33, 33-47, 47-58, 58-69, 69-80 keV energy bin. The SSIM was improved by 1.031%, 0.665%, 0.396%, 0.235%, 0.174% for corresponding energy bin. The decomposition image based on corrected images shows improved RMSE and SSIM, each by 33.861% and 0.345%. SSIM of corrected decomposition image of iodine reaches 99.415% of the original image.

CONCLUSIONS

A new Triple-source saddle-curve cone-beam PC CT image reconstruction method was developed in this work. The exact reconstruction of the triple-source saddle-curve improved both the image quality and temporal resolution.

Chest dual-energy CT to assess the effects of steroids on lung function in severe COVID-19 patients

BACKGROUND

Steroids have been shown to reduce inflammation, hypoxic pulmonary vasoconstriction (HPV) and lung edema. Based on evidence from clinical trials, steroids are widely used in severe COVID-19. However, the effects of steroids on pulmonary gas volume and blood volume in this group of patients are unexplored.

OBJECTIVE

Profiting by dual-energy computed tomography (DECT), we investigated the relationship between the use of steroids in COVID-19 and distribution of blood volume as an index of impaired HPV. We also investigated whether the use of steroids influences lung weight, as index of lung edema, and how it affects gas distribution.

METHODS

Severe COVID-19 patients included in a single-center prospective observational study at the intensive care unit at Uppsala University Hospital who had undergone DECT were enrolled in the current study. Patients' cohort was divided into two groups depending on the administration of steroids. From each patient's DECT, 20 gas volume maps and the corresponding 20 blood volume maps, evenly distributed along the cranial-caudal axis, were analyzed. As a proxy for HPV, pulmonary blood volume distribution was analyzed in both the whole lung and the hypoinflated areas. Total lung weight, index of lung edema, was estimated.

RESULTS

Sixty patients were analyzed, whereof 43 received steroids. Patients not exposed to steroids showed a more extensive non-perfused area (19% vs 13%, p < 0.01) and less homogeneous pulmonary blood volume of hypoinflated areas (kurtosis: 1.91 vs 2.69, p < 0.01), suggesting a preserved HPV compared to patients treated with steroids. Moreover, patients exposed to steroids showed a significantly lower lung weight (953 gr vs 1140 gr, p = 0.01). A reduction in alveolar-arterial difference of oxygen followed the treatment with steroids (322 ± 106 mmHg at admission vs 267 ± 99 mmHg at DECT, p = 0.04).

CONCLUSIONS

The use of steroids might cause impaired HPV and might reduce lung edema in severe COVID-19. This is consistent with previous findings in other diseases. Moreover, a reduced lung weight, as index of decreased lung edema, and a more homogeneous distribution of gas within the lung were shown in patients treated with steroids.

TRIAL REGISTRATION

Clinical Trials ID: NCT04316884, Registered March 13, 2020.

Spectral calibration of photon-counting detectors at high photon flux

BACKGROUND

Calibration of photon-counting detectors (PCDs) is necessary for quantitatively accurate spectral computed tomography (CT), but the calibration process can be complicated by nonlinear flux-dependent physical factors such as pulse pile-up.

PURPOSE

This work develops a method for spectral sensitivity calibration of a PCD-based spectral CT system that incorporates nonlinear flux dependence and can thus be employed at high photon flux.

METHODS

A calibration model for the spectral response and polynomial flux dependence is proposed, which incorporates prior x-ray source spectrum and PCD models and that has a small set of parameters for adjusting to the spectral CT system of interest. The model parameters are determined by fitting transmission data from a known object of known composition: a step-wedge phantom composed of different thicknesses of aluminum, a bone equivalent, and polymethyl methacrylate (PMMA), a soft-tissue equivalent. This fitting employs Tikhonov regularization, and the regularization strength and the polynomial order for the intensity modeling are determined by bias and variance analysis. The spectral calibration and nonlinear intensity correction is validated on transmission measurements through a third material, Teflon, at different x-ray photon flux levels.

RESULTS

The nonlinear intensity dependence is determined to be accurately accounted for with a third-order polynomial. The calibrated spectral CT model accurately predicts Teflon transmission to within 1% for flux levels up to 50% of the detector maximum.

CONCLUSIONS

The proposed PCD calibration method enables accurate physical modeling necessary for quantitative imaging in spectral CT. Furthermore, the model applies to high flux settings so that acquisition times will not be limited by restricting the spectral CT system to low flux levels.

Spectral computed tomography with inorganic nanomaterials: State-of-the-art

Recently, spectral computed tomography (CT) technology has received great interest in the field of radiology. Spectral CT imaging utilizes the distinct, energy-dependent X-ray absorption properties of substances in order to provide additional imaging information. Dual-energy CT and multi-energy CT (Spectral CT) are capable of constructing monochromatic energy images, material separation images, energy spectrum curves, constructing effective atomic number maps, and more. However, poor contrast, due to neighboring X-ray attenuation of organs and tissues, is still a challenge to spectral CT. Hence, contrast agents (CAs) are applied for better differentiation of a given region of interest (ROI). Currently, many different kinds of inorganic nanoparticulate CAs for spectral CT have been developed due to the limitations of clinical iodine (I)-based contrast media, leading to the conclusion that inorganic nanomedicine applied to spectral CT will be a powerful collaboration both in basic research and in clinics. In this review, the underlying principles and types of spectral CT techniques are discussed, and some evolving clinical diagnosis applications of spectral CT techniques are introduced. In particular, recent developments in inorganic CAs used for spectral CT are summarized. Finally, the challenges and future developments of inorganic nanomedicine in spectral CT are briefly discussed.

Quantitative dual-energy CT as a nondestructive tool to identify indicators for fossilized bone in vertebrate paleontology

Dual-energy computed tomography (DECT) is an imaging technique that combines nondestructive morphological cross-sectional imaging of objects and the quantification of their chemical composition. However, its potential to assist investigations in paleontology has not yet been explored. This study investigates quantitative DECT for the nondestructive density- and element-based material decomposition of fossilized bones. Specifically, DECT was developed and validated for imaging-based calcium and fluorine quantification in bones of five fossil vertebrates from different geological time periods and of one extant vertebrate. The analysis shows that DECT material maps can differentiate bone from surrounding sediment and reveals fluorine as an imaging marker for fossilized bone and a reliable indicator of the age of terrestrial fossils. Moreover, the jaw bone mass of Tyrannosaurus rex showed areas of particularly high fluorine concentrations on DECT, while conventional CT imaging features supported the diagnosis of chronic osteomyelitis. These findings highlight the relevance of radiological imaging techniques in the natural sciences by introducing quantitative DECT imaging as a nondestructive approach for material decomposition in fossilized objects, thereby potentially adding to the toolbox of paleontological studies.

Virtual non-contrast images from dual-energy CT angiography of the abdominal aorta and femoral arteries: comparison with true non-contrast CT images

OBJECTIVES

To assess whether virtual non-contrast (VNC) computed tomography (CT) images acquired from dual-energy CT (DECT) have sufficient image quality to replace true non-contrast (TNC) CT images in CT angiography (CTAG).

METHODS

This study enrolled 63 consecutive patients who underwent a CTAG that included a single-energy non-contrast scan, followed by a post-contrast DECT scan. Comparison of attenuation and noise between TNC and VNC images was made by drawing circular regions of interest (ROI) on a picture archiving and communication system (PACS) workstation within the aortic lumen at the levels of the renal arteries, the aortic bifurcation and right femoral artery. Mean attenuation and image noise (one standard deviation) were registered in Hounsfield units (HU). The VNC images were subjectively evaluated for artifacts such as subtraction of calcifications or architectural distortion based on TNC image as a standard of reference.

RESULTS

Most attenuations of the VNCs were higher than TNC, except right femoral artery of reader 2. Most image noises of TNC were higher than VNC, except abdominal aorta in reader 1. In qualitative image analysis, mean scores of VNC according to the 5-point scale were 3.68 and 3.63 (reader 1 and reader 2, respectively) which mean good to excellent to diagnose.

CONCLUSION

HUs and VNC image noises are different from TNC images in CTAG. VNC images have sufficient image quality to replace TNC images in the diagnosis of calcific lesions.

ADVANCES IN KNOWLEDGE

VNC images acquired from DECT have image quality adequate to replace TNC images in the diagnosis of the calcific lesion on the CTAG. VNC images based on DECT can avoid excessive and unnecessary patient exposure to radiation during CTAG.

Functional imaging with dual-energy computed tomography for supplementary non-invasive assessment of mast cell burden in systemic mastocytosis

Systemic mastocytosis (SM) is characterized by multifocal accumulation of neoplastic mast cells (MCs), predominately affecting the bone marrow (BM). Imaging with computed tomography (CT) is used for assessment of bone mineral density and structure. However, the value of functional imaging with dual-energy CT (DECT) and the assessment of virtual-non-calcium attenuation values (VNCa-AV) for visualization of BM disease burden in SM has not yet been assessed. DECT of the axial skeleton was performed in 18 patients with SM (indolent SM [ISM], n = 6; smoldering SM [SSM]/advanced SM [AdvSM], n = 12) and 18 control subjects. VNCa-AV were obtained in 5 representative vertebraes per patient and correlated with laboratory, morphologic and molecular parameters. VNCa-AV strongly correlated with quantitative BM MC infiltration (r = 0.7, R² = 0.49, P = 0.001) and serum tryptase levels (r = 0.7, R² = 0.54, P < 0.001). Mean VNCa-AV were significantly higher in SSM/AdvSM as compared to ISM (− 9HU vs. − 54HU, P < 0.005) and controls (− 38HU, P < 0.005). Nine of 10 (90%) patients with a VNCa-AV >  − 30HU and 7/7 (100%) patients with a VNCa-AV >  − 10HU had SSM or AdVSM. BM VNCa-AV provide information about the MC burden of SM patients and correlate with SM subtypes. DECT may therefore serve as a supplementary tool for SM diagnosis, subclassification and monitoring in a one-stop-shop session.

Model-based three-material decomposition in dual-energy CT using the volume conservation constraint

Objective. We develop a model-based optimization algorithm for ‘one-step’ dual-energy (DE) CT decomposition of three materials directly from projection measurements. Approach. Since the three-material problem is inherently undetermined, we incorporate the volume conservation principle (VCP) as a pair of equality and nonnegativity constraints into the objective function of the recently reported model-based material decomposition (MBMD). An optimization algorithm (constrained MBMD, CMBMD) is derived that utilizes voxel-wise separability to partition the volume into a VCP-constrained region solved using interior-point iterations, and an unconstrained region (air surrounding the object, where VCP is violated) solved with conventional two-material MBMD. Constrained MBMD (CMBMD) is validated in simulations and experiments in application to bone composition measurements in the presence of metal hardware using DE cone-beam CT (CBCT). A kV-switching protocol with non-coinciding low- and high-energy (LE and HE) projections was assumed. CMBMD with decomposed base materials of cortical bone, fat, and metal (titanium, Ti) is compared to MBMD with (i) fat-bone and (ii) fat-Ti bases. Main results. Three-material CMBMD exhibits a substantial reduction in metal artifacts relative to the two-material MBMD implementations. The accuracies of cortical bone volume fraction estimates are markedly improved using CMBMD, with ∼5–10× lower normalized root mean squared error in simulations with anthropomorphic knee phantoms (depending on the complexity of the metal component) and ∼2–2.5× lower in an experimental test-bench study. Significance. In conclusion, we demonstrated one-step three-material decomposition of DE CT using volume conservation as an optimization constraint. The proposed method might be applicable to DE applications such as bone marrow edema imaging (fat-bone-water decomposition) or multi-contrast imaging, especially on CT/CBCT systems that do not provide coinciding LE and HE ray paths required for conventional projection-domain DE decomposition.

Feasibility study of three-material decomposition in dual-energy cone-beam CT imaging with deep learning

Objective. In this work, a dedicated end-to-end deep convolutional neural network, named as Triple-CBCT, is proposed to demonstrate the feasibility of reconstructing three different material distribution volumes from the dual-energy CBCT projection data. Approach. In Triple-CBCT, the features of the sinogram and the CT image are independently extracted and cascaded via a customized domain transform network module. This Triple-CBCT network was trained by numerically synthesized dual-energy CBCT data, and was tested with experimental dual-energy CBCT data of the Iodine-CaCl2 solution and pig leg specimen scanned on an in-house benchtop system. Main results. Results show that the information stored in both the sinogram and CT image domains can be used together to improve the decomposition quality of multiple materials (water, iodine, CaCl2 or bone) from the dual-energy projections. In addition, both the numerical and experimental results demonstrate that the Triple-CBCT is able to generate high-fidelity dual-energy CBCT basis images. Significance. An innovative end-to-end network that joints the sinogram and CT image domain information is developed to facilitate high quality automatic decomposition from the dual-energy CBCT scans.

One-step iterative reconstruction approach based on eigentissue decomposition for spectral photon-counting computed tomography

Purpose: We propose a one-step tissue characterization method for spectral photon-counting computed tomography (SPCCT) using eigentissue decomposition (ETD), tailored for highly accurate human tissue characterization in radiotherapy. Methods: The approach combines a Poisson likelihood, a spatial prior, and a quantitative prior constraining eigentissue fractions based on expected values for tabulated tissues. There are two regularization parameters: α for the quantitative prior, and β for the spatial prior. The approach is validated in a realistic simulation environment for SPCCT. The impact of α and β is evaluated on a virtual phantom. The framework is tested on a virtual patient and compared with two sinogram-based two-step methods [using respectively filtered backprojection (FBP) and an iterative method for the second step] and a post-reconstruction approach with the same quantitative prior. All methods use ETD. Results: Optimal performance with respect to bias or RMSE is achieved with different combinations of α and β on the cylindrical phantom. Evaluated in tissues of the virtual patient, the one-step framework outperforms two-step and post-reconstruction approaches to quantify proton-stopping power (SPR). The mean absolute bias on the SPR is 0.6% (two-step FBP), 0.6% (two-step iterative), 0.6% (post-reconstruction), and 0.2% (one-step optimized for low bias). Following the same order, the RMSE on the SPR is 13.3%, 2.5%, 3.2%, and 1.5%. Conclusions: Accurate and precise characterization with ETD can be achieved with noisy SPCCT data without the need to rely on post-reconstruction methods. The one-step framework is more accurate and precise than two-step methods for human tissue characterization.

Lobar pulmonary perfusion quantification with dual-energy CT angiography: Interlobar variability and relationship with regional clot burden in pulmonary embolism

Purpose

Semi-automated lobar segmentation tools enable an anatomical assessment of regional pulmonary perfusion with Dual-Energy CTA (DE-CTA). We aimed to quantify lobar pulmonary perfusion with DE-CTA, analyze the perfusion distribution among the pulmonary lobes in subjects without cardiopulmonary diseases and assess the correlation between lobar perfusion and regional endoluminal clots in patients with acute pulmonary embolism (PE).

Methods

We evaluated 151 consecutive subjects with suspected PE and without cardiopulmonary comorbidities. DE-CTA derived perfused blood volume (PBV) of each pulmonary lobe was measured applying a semi-automated lobar segmentation technique. In patients with PE, blood clot location was assessed, and CT-based vascular obstruction index of each lobe (CTOI_lobe) was calculated and classified into three groups: CTOI_lobe= 0, low CTOI_lobe (1–50%) and high CTOI_lobe (>50%).

Results

Among patients without PE (103/151, 68.2%), median lobar PBV was 13.7% (IQR 10.2–18.0%); the right middle lobe presented lower PBV when compared to all the other lobes (p < .001). In patients with PE (48/151, 31.8%), lobar PBV was 12.6% (IQR 9.6–15.7%), 13.7% (IQR 10.1–16.7%) and 6.5% (IQR 5.1–10.2%) in the lobes with CTOI_lobe= 0, low CTOI_lobe and high CTOI_lobe scores, respectively, with a significantly decreased PBV in the lobes with high CTOI_lobe score (p < .001). ROC analysis of lobar PBV for prediction of high CTOI_lobe score revealed AUC of 0.847 (95%CI 0.785–0.908).

Conclusion

Pulmonary perfusion was heterogeneously distributed along the pulmonary lobes in patients without cardiopulmonary diseases. In patients with PE, the lobes with high vascular obstruction score (CTOI_lobe> 50%) presented a decreased lobar perfusion.

•

Semi-automated tools enable assessment of lobar perfusion with Dual-Energy CTA.

•

The pulmonary perfusion is heterogeneously distributed along the pulmonary lobes.

•

Lobar perfusion was decreased only in the lobes with high vascular obstruction index.

Spectral Photon-Counting Computed Tomography: A Review on Technical Principles and Clinical Applications

Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.

Assessment of Liver Fat: Dual-Energy CT versus Conventional CT with and without Contrast

We assessed the correlation between liver fat percentage using dual-energy CT (DECT) and Hounsfield unit (HU) measurements in contrast and non-contrast CT. This study included 177 patients in two patient groups: Group A (n = 125) underwent whole body non-contrast DECT and group B (n = 52) had a multiphasic DECT including a conventional non-contrast CT. Three regions of interest were placed on each image series, one in the left liver lobe and two in the right to measure Hounsfield Units (HU) as well as liver fat percentage. Linear regression analysis was performed for each group as well as combined. Receiver operating characteristic (ROC) curve was generated to establish the optimal fat percentage threshold value in DECT for predicting a non-contrast threshold of 40 HU correlating to moderate-severe liver steatosis. We found a strong correlation between fat percentage found with DECT and HU measured in non-contrast CT in group A and B individually (R² = 0.81 and 0.86, respectively) as well as combined (R² = 0.85). No significant difference was found when comparing venous and arterial phase DECT fat percentage measurements in group B (p = 0.67). A threshold of 10% liver fat found with DECT had 95% sensitivity and 95% specificity for the prediction of a 40 HU threshold using non-contrast CT. In conclusion, liver fat quantification using DECT shows high correlation with HU measurements independent of scan phase.

Quantification of the volume fraction of fat, water and bone mineral in spongiosa for red marrow dosimetry in molecular radiotherapy by using a dual-energy (SPECT/)CT

A patient-specific absorbed dose calculation for red marrow dosimetry requires quantifying patient-specific volume fractions of the red marrow, yellow marrow, and trabecular bone in the spongiosa of several skeletal sites. This quantification allows selecting appropriate S values calculated from the parameterized radiation transport models for bone and bone marrow dosimetry. Currently, no comprehensive, individualized, and non-invasive procedure is available for quantifying the volume fractions of red marrow, yellow marrow, and trabecular bone in the spongiosa. This study aims to provide a new quantitative method based on dual-energy computed tomography to fill this gap in red marrow dosimetry using a (SPECT/)CT system.

METHODS

First, a method for parametrizing the photon attenuation coefficients relative to water was implemented. Next, a method to calculate the effective atomic number (Z_eff) and effective mass density (ρ_eff) using dual-energy CT (DECT) was employed. Lastly, two- and three-material decomposition using a dual-energy quantitative CT method (DEQCT) was performed in an anthropomorphic spine phantom and two bone samples of a boar, respectively. The measurements of Z_eff and ρ_eff were compared with the syngo.CT DE Rho/Z tool (Siemens Healthineers). Furthermore, the DEQCT method implemented in this study (DEQCT-I) was compared with a second DEQCT method based on the use of external material standards (DEQCT-II). DEQCT-II was used as reference method for calculating relative errors.

RESULTS

The two-material decomposition in the anthropomorphic spine phantom presented a maximum relative error of -10% for the bone mineral density quantification. Furthermore, Z_eff and ρ_eff calculated by DEQCT-I differed from syngo.CT DE Rho/Z tool by less than 4.4% and 1.9%, respectively. The three-material decomposition in the two bone samples showed a maximum relative error of 21%, -17%, and 15% for the quantification of the volume fractions of fat, water, and bone mineral equivalent materials. Lastly, Z_eff and ρ_eff calculated by DEQCT-I differed from syngo.CT DE Rho/Z tool by less than 8.2% and 7.0%, respectively.

CONCLUSION

This study shows that quantifying the volume fraction of fat, water, and bone mineral using a phantom-independent and post-reconstruction DEQCT method is feasible. DEQCT-I has the advantage of not requiring prior information about the X-ray spectra or the detector sensitivity function, as is the case with spectral-based DEQCT methods. Instead, DEQCT-I, similar to other DEQCT methods depends on the chemical description of reference materials and a beam hardening correction function. DEQCT-I method provides an individualized and non-invasive procedure using a (SPECT/)CT system to apply S values based on the patient-specific volume fractions of yellow marrow, red marrow, and bone mineral in red marrow dosimetry.

Accuracy of virtual monochromatic images generated by the decomposition of photoelectric absorption and Compton scatter in dual-energy computed tomography

The decomposition of the linear attenuation coefficient into photoelectric absorption and Compton scattering provides virtual monochromatic images (VMIs). The accuracy of the computed tomography (CT) number of VMI, which is obtained by decomposing the linear attenuation coefficient into photoelectric absorption and Compton scattering, was verified in the energy range of 40-200 keV. The possibility of improving the accuracy of CT numbers by using pre-energy-calibrated images as input was also investigated. The VMIs were generated in two groups of images: (i) dual-energy scanned images and (ii) high- and low-energy images generated by two-material decomposition (i.e., pre-energy-calibrated images). The object for analysis was solid iodine rods inserted in the center of the multi-energy CT phantom. The VMIs were generated from the dual-energy scanned images and pre-energy-calibrated images, and the theoretical and measured CT numbers of solid iodine rods were compared. Furthermore, the absolute error (AE) and relative error (RE) were calculated. With both images, the accuracy of the CT numbers was extremely high for regions close to the high- and low-tube-voltage X-ray energy or the high and low energy of the input images. By using the pre-energy-calibrated images, the maximum AE was reduced from 133 to 96 HU at an energy of 40 keV. Similarly, the maximum RE was reduced from 325 to 50% at an energy of 200 keV. The pre-energy-calibrated images reduced the overall error of the CT numbers and controlled the energy region where accurate CT numbers could be obtained.

Simultaneous dual-contrast imaging using energy-integrating-detector multi-energy CT: An in vivo feasibility study

PURPOSE

To demonstrate the feasibility of simultaneous dual-contrast imaging in a large animal using a newly developed dual-source energy-integrating-detector (EID) based multi-energy computed tomography (MECT) system.

METHODS

Two imaging tasks that may have potential clinical applications were investigated: head/neck (HN) CT angiography (CTA)/CT venography (CTV) with iodine and gadolinium, and small bowel imaging with iodine and bismuth in domestic swine. Dual-source x-ray beam configurations of 70 kV+Au120/Sn120 kV and 70 kV+Au140/Sn140 kV were used for the HN-CTA/CTV and small bowel imaging studies, respectively. A test bolus scan was performed for each study. The ROIs in the carotid artery and jugular vein for HN-CTA/CTV imaging and abdominal aorta for small bowel imaging were used to determine the time-attenuation curves, based on which the timing for contrast injection and the CT scan was determined. In the HN-CTA/CTV study, a MECT scan was performed at the time point corresponding to the optimal arterial enhancement by iodine and the optimal venous enhancement by gadolinium. In the small bowel imaging study, A MECT scan was performed at the optimal time point to simultaneously capture the mesenteric arterial enhancement of iodine and the enteric enhancement of bismuth. Image-based material decomposition was performed to decompose different materials for each study. To quantitatively characterize contrast material separation and misclassification, two ROIs on left common carotid artery and left internal jugular vein in HN-CTA/CTV imaging and three ROIs on superior mesenteric artery, ileal lumen, and collapsed ileum (ileal wall) in small bowel imaging were placed to measure the mean concentration values and the standard deviations.

RESULTS

In the HN-CTA/CTV study, common carotid arteries containing iodine and internal/external jugular veins containing gadolinium were clearly delineated from each other. Fine vessels such as cephalic veins and branches of external jugular veins were noticeable but clear visualization was hindered by image noise in gadolinium-specific (CTV) images, as reviewed by a neuro radiologist. In the small bowel imaging study, the mesenteric arteries and collapsed bowel wall containing iodine and the small bowel loops containing bismuth were clearly distinctive from each other in the iodine- and bismuth-specific images after material decomposition, as reviewed by an abdominal radiologist. Quantitative analyses showed that the misclassifications between the two contrast materials were less than 1.7 mg/mL and 0.1 mg/mL for CTA/CTV and small bowel imaging studies, respectively.

CONCLUSIONS

Feasibility of simultaneous CTA/CTV imaging in head and neck with iodine and gadolinium and simultaneous imaging of arterial and enteric phases of small bowel with iodine and bismuth, using a dual-source EID-MECT system, was demonstrated in a swine study. Compared to iodine and gadolinium in CTA/CTV, better delineation and classification of iodine and bismuth in small bowel imaging were achieved mainly due to wider separation between the corresponding two K-edge energies. This article is protected by copyright. All rights reserved.

High-resolution model-based material decomposition in dual-layer flat-panel CBCT

PURPOSE

Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches.

METHOD

A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penalized weighted least-squared (PWLS) objective function. The noise and resolution performance of this approach was compared with traditional projection-domain decomposition (PDD) and image-domain decomposition (IDD) approaches as well as one-step MBMD with lower-fidelity models that use approximated geometry, projection interpolation, or an idealized system geometry without system blur model. Physical studies using high-resolution three-dimensional (3D)-printed water-iodine phantoms were conducted to demonstrate the high-resolution imaging performance of the compared decomposition methods in iodine basis images and synthetic monoenergetic images.

RESULTS

Physical experiments demonstrate that the MBMD methods incorporating an accurate geometry model can yield higher spatial resolution iodine basis images and synthetic monoenergetic images than PDD and IDD results at the same noise level. MBMD with blur modeling can further improve the spatial-resolution compared with the decomposition results obtained with IDD, PDD, and MBMD methods with lower-fidelity models. Using the MBMD without or with blur model can increase the absolute modulation at 1.75 lp/mm by 10% and 22% compared with IDD at the same noise level.

CONCLUSION

The proposed model-based material decomposition method for a dual-layer flat-panel CBCT system has demonstrated an ability to extend high-resolution performance through sophisticated detector modeling including the layer-dependent blur. The proposed work has the potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.

Fat Fraction Measurements Using a Three-Material Decomposition Dual-Energy CT Technique Accounting for Bone Minerals: Evaluation in a Bone Marrow Phantom Using MRI as Reference

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).

Principles and Applications of Dual Energy Computed Tomography in Neuroradiology

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.

Whole-organism 3D quantitative characterization of zebrafish melanin by silver deposition micro-CT

We previously described X-ray histotomography, a high-resolution, non-destructive form of X-ray microtomography (micro-CT) imaging customized for three-dimensional (3D), digital histology, allowing quantitative, volumetric tissue and organismal phenotyping (Ding et al., 2019). Here, we have combined micro-CT with a novel application of ionic silver staining to characterize melanin distribution in whole zebrafish larvae. The resulting images enabled whole-body, computational analyses of regional melanin content and morphology. Normalized micro-CT reconstructions of silver-stained fish consistently reproduced pigment patterns seen by light microscopy, and further allowed direct quantitative comparisons of melanin content across wild-type and mutant samples, including subtle phenotypes not previously noticed. Silver staining of melanin for micro-CT provides proof-of-principle for whole-body, 3D computational phenomic analysis of a specific cell type at cellular resolution, with potential applications in other model organisms and melanocytic neoplasms. Advances such as this in whole-organism, high-resolution phenotyping provide superior context for studying the phenotypic effects of genetic, disease, and environmental variables.

Virtual non-calcium dual-energy CT: clinical applications

Dual-energy CT (DECT) has emerged into clinical routine as an imaging technique with unique postprocessing utilities that improve the evaluation of different body areas. The virtual non-calcium (VNCa) reconstruction algorithm has shown beneficial effects on the depiction of bone marrow pathologies such as bone marrow edema. Its main advantage is the ability to substantially increase the image contrast of structures that are usually covered with calcium mineral, such as calcified vessels or bone marrow, and to depict a large number of traumatic, inflammatory, infiltrative, and degenerative disorders affecting either the spine or the appendicular skeleton. Therefore, VNCa imaging represents another step forward for DECT to image conditions and disorders that usually require the use of more expensive and time-consuming techniques such as magnetic resonance imaging, positron emission tomography/CT, or bone scintigraphy. The aim of this review article is to explain the technical background of VNCa imaging, showcase its applicability in the different body regions, and provide an updated outlook on the clinical impact of this technique, which goes beyond the sole improvement in image quality.

CT Noise-Reduction Methods for Lower-Dose Scanning: Strengths and Weaknesses of Iterative Reconstruction Algorithms and New Techniques

Iterative reconstruction (IR) algorithms are the most widely used CT noise-reduction method to improve image quality and have greatly facilitated radiation dose reduction within the radiology community. Various IR methods have different strengths and limitations. Because IR algorithms are typically nonlinear, they can modify spatial resolution and image noise texture in different regions of the CT image; hence traditional image-quality metrics are not appropriate to assess the ability of IR to preserve diagnostic accuracy, especially for low-contrast diagnostic tasks. In this review, the authors highlight emerging IR algorithms and CT noise-reduction techniques and summarize how these techniques can be evaluated to help determine the appropriate radiation dose levels for different diagnostic tasks in CT. In addition to advanced IR techniques, we describe novel CT noise-reduction methods based on convolutional neural networks (CNNs). CNN-based noise-reduction techniques may offer the ability to reduce image noise while maintaining high levels of image detail but may have unique drawbacks. Other novel CT noise-reduction methods are being developed to leverage spatial and/or spectral redundancy in multiphase or multienergy CT. Radiologists and medical physicists should be familiar with these different alternatives to adapt available CT technology for different diagnostic tasks. The scope of this article is (a) to review the clinical applications of IR algorithms as well as their strengths, weaknesses, and methods of assessment and (b) to explore new CT image reconstruction and noise-reduction techniques that promise to facilitate radiation dose reduction. ^©RSNA, 2021.

ASPECTS estimation using dual-energy CTA-derived virtual non-contrast in large vessel occlusion acute ischemic stroke: a dose reduction opportunity for patients undergoing repeat CT?

PURPOSE

Recent studies have shown the feasibility of dual-energy CT (DECT) virtual non-contrast (VNC) for determining infarct extent. In this study, patients presenting with large-vessel occlusion (LVO) acute ischemic stroke (AIS), we assess whether ASPECTS on DECTA-VNC differs from non-contrast CT (NCCT).

METHODS

After IRB approval, LVO-AIS patients undergoing NCCT and DECTA between October 2016 and September 2018 were retrospectively reviewed. DECTA-VNC images were derived using Syngo.via (Siemens, Erlangen, Germany). ASPECTS was scored by two blinded neuroradiologists. Square-weighted kappa statistic, diagnostic performance, Wilcoxon signed-rank tests between groups, and CT doses were calculated.

RESULTS

Fifty-one patients met inclusion criteria, with median age of 76 (IQR 67-82); 26/51 (51%) were female. Median time between last-known-well and CT was 120 min (IQR 60-252). DECTA-VNC ASPECTS score differed by ≤ 1 from consensus NCCT in 49/51 (96%) patients for reader 1 and in 46/51 (90%) for reader 2. ASPECTS on DECTA-SI and consensus NCCT differed by ≤ 1 in 45/51 (88%) for both readers. On a per ASPECTS-region basis, DECTA-VNC had 87% sensitivity, 95% specificity, 0.82% PPV, and 0.96% NPV. ASPECTS inter-rater agreement was highest for DECTA-VNC (κ = 0.71), DECTA-SI (κ = 0.48), and NCCT (κ = 0.40). NCCT median CTDIvol was 63.7 mGy (IQR 60.7-67.2); DLP was 1060.0 mGy·cm (IQR 981.0-1151.5). DECTA-VNC dose was lower: median CTDIvol was 20.9 mGy (IQR 19.8-22.2); DLP was 804.1 (IQR 691.6-869.4), p < 0.0001.

CONCLUSION

DECTA-derived VNC yielded similar ASPECTS scores as NCCT and is therefore non-inferior in early ischemia-related low attenuation edema/infarct detection in acute LVO-AIS patients. Further evaluation of the role of DECTA-VNC in AIS imaging is warranted.