Principles and applications of multienergy CT: Report of AAPM Task Group 291

CT-based synthetic iodine map generation using conditional denoising diffusion probabilistic model

BACKGROUND

Iodine maps, derived from image-processing of contrast-enhanced dual-energy computed tomography (DECT) scans, highlight the differences in tissue iodine intake. It finds multiple applications in radiology, including vascular imaging, pulmonary evaluation, kidney assessment, and cancer diagnosis. In radiation oncology, it can contribute to designing more accurate and personalized treatment plans. However, DECT scanners are not commonly available in radiation therapy centers. Additionally, the use of iodine contrast agents is not suitable for all patients, especially those allergic to iodine agents, posing further limitations to the accessibility of this technology.

PURPOSE

The purpose of this work is to generate synthetic iodine map images from non-contrast single-energy CT (SECT) images using conditional denoising diffusion probabilistic model (DDPM).

METHODS

One-hundered twenty-six head-and-neck patients' images were retrospectively investigated in this work. Each patient underwent non-contrast SECT and contrast DECT scans. Ground truth iodine maps were generated from contrast DECT scans using commercial software syngo.via installed in the clinic. A conditional DDPM was implemented in this work to synthesize iodine maps. Three-fold cross-validation was conducted, with each iteration selecting the data from 42 patients as the test dataset and the remainder as the training dataset. Pixel-to-pixel generative adversarial network (GAN) and CycleGAN served as reference methods for evaluating the proposed DDPM method.

RESULTS

The accuracy of the proposed DDPM was evaluated using three quantitative metrics: mean absolute error (MAE) (1.039 ± 0.345 mg/mL), structural similarity index measure (SSIM) (0.89 ± 0.10) and peak signal-to-noise ratio (PSNR) (25.4 ± 3.5 db) respectively. Compared to the reference methods, the proposed technique showcased superior performance across the evaluated metrics, further validated by the paired two-tailed t-tests.

CONCLUSION

The proposed conditional DDPM framework has demonstrated the feasibility of generating synthetic iodine map images from non-contrast SECT images. This method presents a potential clinical application, which is providing accurate iodine contrast map in instances where only non-contrast SECT is accessible.

Development of a separability index for task specific characterization of spectral computed tomography

PURPOSE

In this work, we define a signal detection based metrology to characterize the separability of two different multi-dimensional signals in spectral CT acquisitions.

METHOD

Signal response was modelled as a random process with a deterministic signal and stochastic noise component. A linear Hotelling observer was used to estimate a scalar test statistic distribution that predicts the likelihood of an intensity value belonging to a signal. Two distributions were estimated for two materials of interest and used to derive two metrics separability: a separability index (s') and the area under the curve of the test statistic distributions. Experimental and simulated data of photon-counting CT scanners were used to evaluate each metric. Experimentally, vials of iodine and gadolinium (2, 4, 8 mg/mL) were scanned at multiple tube voltages, tube currents and energy thresholds. Additionally, a simulated dataset with low tube current (10-150 mAs) and material concentrations (0.25-4 mg/mL) was generated.

RESULTS

Experimental data showed that conditions favorable for low noise and expression of k-edge signal produced the highest separability. Material concentration had the greatest impact on separability. The simulated data showed that under more difficult separation conditions, difference in material concentration still had the greatest impact on separability.

CONCLUSION

The results demonstrate the utility of a task specific metrology to measure the overlap in signal between different materials in spectral CT. Using experimental and simulated data, the separability index was shown to describe the relationship between image formation factors and the signal responses of material.

Coronary calcium scoring on virtual non-contrast and virtual non-iodine reconstructions compared to true non-contrast images using photon-counting computed tomography

OBJECTIVES

To compare coronary artery calcification (CAC) scores measured on virtual non-contrast (VNC) and virtual non-iodine (VNI) reconstructions computed from coronary computed tomography angiography (CCTA) using photon-counting computed tomography (PCCT) to true non-contrast (TNC) images.

METHODS

We included 88 patients (mean age = 59 years ± 13.5, 69% male) who underwent a TNC coronary calcium scan followed by CCTA on PCCT. VNC images were reconstructed in 87 patients and VNI in 88 patients by virtually removing iodine from the CCTA images. For all reconstructions, CAC scores were determined, and patients were classified into risk categories. The overall agreement of the reconstructions was analyzed by Bland-Altman plots and the level of matching classifications.

RESULTS

The median CAC score on TNC was 27.8 [0-360.4] compared to 8.5 [0.2-101.6] (p < 0.001) on VNC and 72.2 [1.3-398.8] (p < 0.001) on VNI. Bland-Altman plots depicted a bias of 148.8 (ICC = 0.82, p < 0.001) and - 57.7 (ICC = 0.95, p < 0.001) for VNC and VNI, respectively. Of all patients with CACTNC = 0, VNC reconstructions scored 63% of the patients correctly, while VNI scored 54% correctly. Of the patients with CACTNC > 0, VNC and VNI reconstructions detected the presence of coronary calcium in 90% and 92% of the patients. CACVNC tended to underestimate CAC score, whereas CACVNI overestimated, especially in the lower risk categories. According to the risk categories, VNC misclassified 55% of the patients, while VNI misclassified only 32%.

CONCLUSION

Compared to TNC images, VNC underestimated and VNI overestimated the actual CAC scores. VNI reconstructions quantify and classify coronary calcification scores more accurately than VNC reconstructions.

CLINICAL RELEVANCE STATEMENT

Photon-counting CT enables spectral imaging, which might obviate the need for non-contrast enhanced coronary calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.

KEY POINTS

• Photon-counting computed tomography uses spectral information to virtually remove the signal of contrast agents from contrast-enhanced scans. • Virtual non-contrast reconstructions tend to underestimate coronary artery calcium scores compared to true non-contrast images, while virtual non-iodine reconstructions tend to overestimate the calcium scores. • Virtual non-iodine reconstructions might obviate the need for non-contrast enhanced calcium scoring, but optimization is necessary for the clinical implementation of the algorithms.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography

OBJECTIVE

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems.

METHOD

A phantom with two iodine inserts (1 and 2 mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4 cm for first generation and 8 cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50  versus 150 keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported.

RESULTS

An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2 cm). With increased collimation (4 cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8 cm collimation width was comparable to a 4 cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45  and 0.33 mg/mL for the first and second-generation DLCT, respectively).

CONCLUSION

The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

MRI-only based material mass density and relative stopping power estimation via deep learning for proton therapy: a preliminary study

Magnetic Resonance Imaging (MRI) is increasingly being used in treatment planning due to its superior soft tissue contrast, which is useful for tumor and soft tissue delineation compared to computed tomography (CT). However, MRI cannot directly provide mass density or relative stopping power (RSP) maps, which are required for calculating proton radiotherapy doses. Therefore, the integration of artificial intelligence (AI) into MRI-based treatment planning to estimate mass density and RSP directly from MRI has generated significant interest. A deep learning (DL) based framework was developed to establish a voxel-wise correlation between MR images and mass density as well as RSP. To facilitate the study, five tissue substitute phantoms were created, representing different tissues such as skin, muscle, adipose tissue, 45% hydroxyapatite (HA), and spongiosa bone. The composition of these phantoms was based on information from ICRP reports. Additionally, two animal tissue phantoms, simulating pig brain and liver, were prepared for DL training purposes. The phantom study involved the development of two DL models. The first model utilized clinical T1 and T2 MRI scans as input, while the second model incorporated zero echo time (ZTE) MRI scans. In the patient application study, two more DL models were trained: one using T1 and T2 MRI scans as input, and another model incorporating synthetic dual-energy computed tomography (sDECT) images to provide accurate bone tissue information. The DECT empirical model was used as a reference to evaluate the proposed models in both phantom and patient application studies. The DECT empirical model was selected as the reference for evaluating the proposed models in both phantom and patient application studies. In the phantom study, the DL model based on T1, and T2 MRI scans demonstrated higher accuracy in estimating mass density and RSP for skin, muscle, adipose tissue, brain, and liver. The mean absolute percentage errors (MAPE) were 0.42%, 0.14%, 0.19%, 0.78%, and 0.26% for mass density, and 0.30%, 0.11%, 0.16%, 0.61%, and 0.23% for RSP, respectively. The DL model incorporating ZTE MRI further improved the accuracy of mass density and RSP estimation for 45% HA and spongiosa bone, with MAPE values of 0.23% and 0.09% for mass density, and 0.19% and 0.07% for RSP, respectively. These results demonstrate the feasibility of using an MRI-only approach combined with DL methods for mass density and RSP estimation in proton therapy treatment planning. By employing this approach, it is possible to obtain the necessary information for proton radiotherapy directly from MRI scans, eliminating the need for additional imaging modalities.

Utility of Dual-Energy Computed Tomography in Clinical Conundra

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Spectral performance evaluation of a second-generation spectral detector CT

PURPOSE

The aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities.

METHODS

A phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported.

RESULTS

Measured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively.

CONCLUSION

The second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS)

BACKGROUND

Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity.

PURPOSE

The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections.

METHODS

To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method.

RESULTS

Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values (E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, theE RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively.

CONCLUSIONS

We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

Enhanced in-stent luminal visualization and restenosis diagnosis in coronary computed tomography angiography via coronary stent decomposition algorithm from dual-energy image

Stent implantation is a principal therapeutic approach for coronary artery diseases. Nonetheless, the presence of stents significantly interferes with in-stent luminal (ISL) visualization and complicates the diagnosis of in-stent restenosis (ISR), thereby increasing the risk of misdiagnoses and underdiagnoses in coronary computed tomography angiography (CCTA). Dual-energy (DE) CT could calculate the volume fraction for voxels from low- and high-energy images (LHEI) and provide information on specific three basic materials. In this study, the innovative coronary stent decomposition algorithm (CSDA) was developed from the DECT three materials decomposition (TMD), through spectral simulation to determine the scan and attenuation coefficient for the stent, and preliminary execution for an in vitro sophisticated polyether ether ketone (PEEK) 3D-printed right coronary artery (RCA) replica. Furthermore, the whole-coronary-artery replica with multi-stent implantation, the RCA replica with mimetic plaque embedded, and two patients with stent further validated the effectiveness of CSDA. Post-CSDA images manifested no weakened attenuation values, no elevated noise values, and maintained anatomical integrity in the coronary lumen. The stents were effectively removed, allowing for the ISL and ISR to be clearly visualized with a discrepancy in diameters within 10%. We believe that CSDA presents a promising solution for enhancing CCTA diagnostic accuracy post-stent implantation.

Material decomposition maps based calibration of dual energy CT scanners for proton therapy planning: a phantom study

We introduce a new calibration method for dual energy CT (DECT) based on material decomposition (MD) maps, specifically iodine and water MD maps. The aim of this method is to provide the first DECT calibration based on MD maps. The experiments were carried out using a general electric (GE) revolution CT scanner with ultra-fast kV switching and used a density phantom by GAMMEX for calibration and evaluation. The calibration process involves several steps. First, we tested the ability of MD values to reproduce Hounsfield unit (HU) values of single energy CT (SECT) acquisitions and it was found that the errors were below 1%, validating their use for HU reproduction. Next, the different definitions of computedZvalues were compared and the robustness of the approach based on the materials' composition was confirmed. Finally, the calibration method was compared with a previous method by Bourqueet al, providing a similar level of accuracy and superior performance in terms of precision. Overall, this novel DECT calibration method offers improved accuracy and reliability in determining tissue-specific physical properties. The resulting maps can be valuable for proton therapy treatments, where precise dose calculations and accurate tissue differentiation are crucial for optimal treatment planning and delivery.

Assignment of the biological value of solid breast masses based on quantitative evaluations of spectral CT examinations using electron density mapping, Zeffective mapping and iodine mapping

OBJECTIVE

We aimed to asses, in a clinical setting, whether the newly available quantitative evaluation of electron density (ED) in spectral CT examinations of the breast provide information on the biological identity of solid breast masses and whether ED maps yield added value to the diagnostic information of iodine maps and Zeff maps calculated from the same CT image datasets.

METHODS

All patients at the University Breast Cancer Center who underwent a clinically indicated Dual Layer Computed Tomography (DLCT) examination for staging of invasive breast cancer from 2018 to 2020 were prospectively included. Iodine concentration maps, Zeff maps and ED maps were automatically reconstructed from the DLCT datasets. Region of interest (ROI) based evaluations in the breast target lesions and in the aorta were performed semi-automatically in identical anatomical positions using dedicated evaluation software. Case-by-case evaluations were carried independently by 2 of 4 radiologists for each examination, respectively. Statistical analysis derived from the ROIs was done by calculating ROC/AUC curves and Youden indices.

RESULTS

The evaluations comprised 166 DLCT examinations. In the ED maps the measurements in the breast target lesions yielded Youden cutpoints of 104.0% (reader 1) and 103.8% (reader 2) resulting in AUCs of 0.63 and 0.67 at the empirical cutpoints. The variables "Zeff" and "iodine content" derived from the target lesions showed superior diagnostical results, with a Youden cutpoint of 8.0 mg/ml in the iodine maps and cutpoints of 1.1/1.2 in the Zeff maps the AUCs ranging from 0.84 to 0.85 (p = 0.023 to <0.000). The computational combination of Zeff and ED measurements in the target lesions yielded a slight AUC increase (readers 1: 0.85-0.87; readers 2: 0.84-0.94). The ratios of the measured values in the target lesions normalized to the values measured in the aorta showed comparable results. The AUCs of ED derived from the cutpoints showed inferior results to those derived from the Zeff maps and iodine maps (ED: 0.64 and 0.66 for reader 1 and 2; Zeff: 0.86 for both readers; iodine content: 0.89 and 0.86 for reader 1 and 2, respectively). The computational combination of the ED results and the Zeff measurements did not lead to a clinically relevant diagnostic gain with AUCs ranging from 0.86 to 0.88.

CONCLUSIONS

Quantitative assessments of Zeff, iodine content and ED all targeting the physical and chemical aspects of iodine uptake in solid breast masses confirmed diagnostically robust cutpoints for the differentiation of benign and malignant findings (Zeff < 7.7, iodine content of <0.8 mg/ml). The evaluations of the ED did not indicate any added diagnostic value beyond the quantitative assessments of Zeff and iodine content. Further research is warranted to develop suitable clinical indications for the use of ED maps.

Prediction of proton stopping power ratios using dual-energy CT basis material decomposition

BACKGROUND

Proton radiotherapy treatment plans are currently restricted by the range uncertainties originating from the stopping power ratio (SPR) prediction based on single-energy computed tomography (SECT). Various studies have shown that multi-energy CT (MECT) can reduce the range uncertainties due to medical implant materials and age-related variations in tissue composition. None of these has directly applied the basis material density (MD) images produced by projection-based MECT systems for SPR prediction.

PURPOSE

To present and evaluate a novel proton SPR prediction method based on MD images from dual-energy CT (DECT), which could reduce the range uncertainties currently associated with proton radiotherapy.

METHODS

A theoretical basis material decomposition into water and iodine material densities was performed for various pediatric and adult human reference tissues, as well as other non-tissue materials, by minimizing the root-mean-square relative attenuation error in the energy interval from 40 to 140 keV. A model (here called MD-SPR) mapping predicted MDs to theoretically calculated reference SPRs was created with locally weighted scatterplot smoothing (LOWESS) data-fitting. The goodness of fit of the MD-SPR model was evaluated for the included reference tissues. MD images of two electron density phantoms, combined to form a head- and an abdomen-sized phantom setup, were acquired with a clinical projection-based fast-kV switching DECT scanner. The MD images were compared to the theoretically predicted MDs of the tissue surrogates and other non-tissue materials in the phantoms, as well as used for input to the MD-SPR model for generation of SPR images. The SPR images were subsequently compared to theoretical reference SPRs of the materials in the phantoms, as well as to SPR images from a commercial algorithm (DirectSPR, Siemens Healthineers, Forchheim, Germany) using image-based consecutive scan DECT for the same phantom setups.

RESULTS

The predicted SPRs of the tissue surrogates were similar for MD-SPR and DirectSPR, where the adipose and bone tissue surrogates were within 1% difference to the reference SPRs, while other non-adipose soft tissue surrogates (breast, brain, liver, muscle) were all underestimated by between -0.7% and -1.8%. The SPRs of the non-tissue materials (polymethyl methacrylate (PMMA), polyether ether ketone (PEEK), graphite and Teflon) were within 2.8% for MD-SPR images, compared to 6.8% for DirectSPR.

CONCLUSIONS

The MD-SPR model performed similar compared to other published methods for the human reference tissues. The SPR prediction for tissue surrogates was similar to DirectSPR and showed potential to improve SPR prediction for non-tissue materials.

Dual-energy computed tomography quality control: Initial experiences with a semi-automatic analysis tool

PURPOSE

A quality control (QC) system for dual-energy CT (DECT) was developed. The scope of the QC system was to monitor both the constancy of the CT images and the software used in calculating the DECT derived maps. Longitudinal analysis was based on a standard imaging protocol, a commercial multi-energy phantom, and a semi-automatic analysis tool.

METHODS

The phantom consisted of an elliptical body section with round slots for interchangeable inserts. It was scanned with 90kVp/Sn150kVp, automatic tube current modulation, and 9.6 mGy CTDIvol. From the two conventional CT images, scanner manufacturer's software was used to provide virtual monoenergetic images at two different energies, effective atomic number (Zeff) maps, and iodine concentration maps. The images were analyzed using an open-source tool allowing user-selected statistics of interest. The means and standard deviations of the phantom background and the iodine, calcium, and water inserts were recorded. The QC tool is available at github.com/tomakela/dectqatool.

RESULTS

The obtained results were generally highly consistent over time, except for the smaller diameter iodine inserts. A small change inZeff was observed after a DECT software update. The developed QC tool aided the analysis robustness: the segmentations were modifiable when needed, and small rotations or air bubbles in the water insert were easily corrected.

CONCLUSION

The developed QC system provided easy-to-use workflow for constancy measurements. A small deviation due to change in the post-processing was detected. The proposed imaging protocol and analysis steps, and the reported measurement variations can aid in determining action levels for DECT QC.

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.

Image quality improvement of a one-step spectral CT reconstruction on a prototype photon-counting scanner

Objective. X-ray spectral computed tomography (CT) allows for material decomposition (MD). This study compared a one-step material decomposition MD algorithm with a two-step reconstruction MD algorithm using acquisitions of a prototype CT scanner with a photon-counting detector (PCD).Approach. MD and CT reconstruction may be done in two successive steps, i.e. decompose the data in material sinograms which are then reconstructed in material CT images, or jointly in a one-step algorithm. The one-step algorithm reconstructed material CT images by maximizing their Poisson log-likelihood in the projection domain with a spatial regularization in the image domain. The two-step algorithm maximized first the Poisson log-likelihood without regularization to decompose the data in material sinograms. These sinograms were then reconstructed into material CT images by least squares minimization, with the same spatial regularization as the one step algorithm. A phantom simulating the CT angiography clinical task was scanned and the data used to measure noise and spatial resolution properties. Low dose carotid CT angiographies of 4 patients were also reconstructed with both algorithms and analyzed by a radiologist. The image quality and diagnostic clinical task were evaluated with a clinical score.Main results. The phantom data processing demonstrated that the one-step algorithm had a better spatial resolution at the same noise level or a decreased noise value at matching spatial resolution. Regularization parameters leading to a fair comparison were selected for the patient data reconstruction. On the patient images, the one-step images received higher scores compared to the two-step algorithm for image quality and diagnostic.Significance. Both phantom and patient data demonstrated how a one-step algorithm improves spectral CT image quality over the implemented two-step algorithm but requires a longer computation time. At a low radiation dose, the one-step algorithm presented good to excellent clinical scores for all the spectral CT images.

Quantitative dual-energy computed tomography with cesium as a novel contrast agent for localization of thermochemical ablation in phantoms and ex vivo models

BACKGROUND

Thermochemical ablation (TCA) is a minimally invasive therapy under development for hepatocellular carcinoma. TCA simultaneously delivers an acid (acetic acid, AcOH) and base (sodium hydroxide, NaOH) directly into the tumor, where the acid/base chemical reaction produces an exotherm that induces local ablation. However, AcOH and NaOH are not radiopaque, making monitoring TCA delivery difficult.

PURPOSE

We address the issue of image guidance for TCA by utilizing cesium hydroxide (CsOH) as a novel theranostic component of TCA that is detectable and quantifiable with dual-energy CT (DECT).

MATERIALS AND METHODS

To quantify the minimum concentration of CsOH that can be positively identified by DECT, the limit of detection (LOD) was established in an elliptical phantom (Multi-Energy CT Quality Assurance Phantom, Kyoto Kagaku, Kyoto, Japan) with two DECT technologies: a dual-source system (SOMATOM Force, Siemens Healthineers, Forchheim, Germany) and a split-filter, single-source system (SOMATOM Edge, Siemens Healthineers). The dual-energy ratio (DER) and LOD of CsOH were determined for each system. Cesium concentration quantification accuracy was evaluated in a gelatin phantom before quantitative mapping was performed in ex vivo models.

RESULTS

On the dual-source system, the DER and LOD were 2.94 and 1.36-mM CsOH, respectively. For the split-filter system, the DER and LOD were 1.41- and 6.11-mM CsOH, respectively. The signal on cesium maps in phantoms tracked linearly with concentration (R2  = 0.99) on both systems with an RMSE of 2.56 and 6.72 on the dual-source and split-filter system, respectively. In ex vivo models, CsOH was detected following delivery of TCA at all concentrations.

CONCLUSIONS

DECT can be used to detect and quantify the concentration of cesium in phantom and ex vivo tissue models. When incorporated in TCA, CsOH performs as a theranostic agent for quantitative DECT image-guidance.

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

Background

The 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.

Objectives

The 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.

Methods

Artificial 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.

Results

For 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%.

Conclusion

The 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 knowledge

Deep learning-based frameworks are proposed to estimate material mass density and RSP from SECT with improved accuracy compared with conventional methods.

Synthetic Megavoltage Cone Beam Computed Tomography Image Generation for Improved Contouring Accuracy of Cardiac Pacemakers

In this study, we aimed to enhance the contouring accuracy of cardiac pacemakers by improving their visualization using deep learning models to predict MV CBCT images based on kV CT or CBCT images. Ten pacemakers and four thorax phantoms were included, creating a total of 35 combinations. Each combination was imaged on a Varian Halcyon (kV/MV CBCT images) and Siemens SOMATOM CT scanner (kV CT images). Two generative adversarial network (GAN)-based models, cycleGAN and conditional GAN (cGAN), were trained to generate synthetic MV (sMV) CBCT images from kV CT/CBCT images using twenty-eight datasets (80%). The pacemakers in the sMV CBCT images and original MV CBCT images were manually delineated and reviewed by three users. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were used to compare contour accuracy. Visual inspection showed the improved visualization of pacemakers on sMV CBCT images compared to original kV CT/CBCT images. Moreover, cGAN demonstrated superior performance in enhancing pacemaker visualization compared to cycleGAN. The mean DSC, HD95, and MSD for contours on sMV CBCT images generated from kV CT/CBCT images were 0.91 ± 0.02/0.92 ± 0.01, 1.38 ± 0.31 mm/1.18 ± 0.20 mm, and 0.42 ± 0.07 mm/0.36 ± 0.06 mm using the cGAN model. Deep learning-based methods, specifically cycleGAN and cGAN, can effectively enhance the visualization of pacemakers in thorax kV CT/CBCT images, therefore improving the contouring precision of these devices.

Dual-Energy, Spectral and Photon Counting Computed Tomography for Evaluation of the Gastrointestinal Tract

The use of dual-energy computed tomography (CT) allows for reconstruction of energy- and material-specific image series. The combination of low-energy monochromatic images, iodine maps, and virtual unenhanced images can improve lesion detection and disease characterization in the gastrointestinal tract in comparison with single-energy CT.

Dual-energy head cone-beam CT using a dual-layer flat-panel detector: Hybrid material decomposition and a feasibility study

BACKGROUND

Flat panel detector (FPD) based cone-beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone-beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual-layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual-energy CT, its potential of enabling head CBCT applications has not yet been fully investigated.

PURPOSE

The relatively moderate energy separation from the dual-layer FPD and the overall low signal level especially at the bottom-layer detector, could raise significant challenges in performing high-quality dual-energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x-ray signals and prior-knowledge behind head CBCT using dual-layer FPD.

METHODS

Firstly, a regular projection-domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual-layer multi-material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics-model based guidance to generate a hybrid MD. A set of physics experiments including fan-beam scan and cone-beam scan using a head phantom and a Gammex Multi-Energy CT phantom are conducted to validate our proposed approach.

RESULTS

The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan-beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5-15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan-beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone-beam scan, after scatter correction (SC) and cone-beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2 HU.

CONCLUSIONS

Our proposed physics and model guided hybrid MD for dual-layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low-signal artifact. This preliminary feasibility study also demonstrated that the dual-layer FPD is promising to enable head CBCT spectral imaging.

CT Number Accuracy and Association With Object Size: A Phantom Study Comparing Energy-Integrating Detector CT and Deep Silicon Photon-Counting Detector CT

BACKGROUND. Variable beam hardening based on patient size causes variation in CT numbers for energy-integrating detector (EID) CT. Photon-counting detector (PCD) CT more accurately determines effective beam energy, potentially improving CT number reliability. OBJECTIVE. The purpose of the present study was to compare EID CT and deep silicon PCD CT in terms of both the effect of changes in object size on CT number and the overall accuracy of CT numbers. METHODS. A phantom with polyethylene rings of varying sizes (mimicking patient sizes) as well as inserts of different materials was scanned on an EID CT scanner in single-energy (SE) mode (120-kV images) and in rapid-kilovoltage-switching dual-energy (DE) mode (70-keV images) and on a prototype deep silicon PCD CT scanner (70-keV images). ROIs were placed to measure the CT numbers of the materials. Slopes of CT number as a function of object size were computed. Materials' ideal CT number at 70 keV was computed using the National Institute of Standards and Technology XCOM Photon Cross Sections Database. The root mean square error (RMSE) between measured and ideal numbers was calculated across object sizes. RESULTS. Slope (expressed as Hounsfield units per centimeter) was significantly closer to zero (i.e., less variation in CT number as a function of size) for PCD CT than for SE EID CT for air (1.2 vs 2.4 HU/cm), water (-0.3 vs -1.0 HU/cm), iodine (-1.1 vs -4.5 HU/cm), and bone (-2.5 vs -10.1 HU/cm) and for PCD CT than for DE EID CT for air (1.2 vs 2.8 HU/cm), water (-0.3 vs -1.0 HU/cm), polystyrene (-0.2 vs -0.9 HU/cm), iodine (-1.1 vs -1.9 HU/cm), and bone (-2.5 vs -6.2 HU/cm) (p < .05). For all tested materials, PCD CT had the smallest RMSE, indicating CT numbers closest to ideal numbers; specifically, RMSE (expressed as Hounsfield units) for SE EID CT, DE EID CT, and PCD CT was 32, 44, and 17 HU for air; 7, 8, and 3 HU for water; 9, 10, and 4 HU for polystyrene; 31, 37, and 13 HU for iodine; and 69, 81, and 20 HU for bone, respectively. CONCLUSION. For numerous materials, deep silicon PCD CT, in comparison with SE EID CT and DE EID CT, showed lower CT number variability as a function of size and CT numbers closer to ideal numbers. CLINICAL IMPACT. Greater reliability of CT numbers for PCD CT is important given the dependence of diagnostic pathways on CT numbers.

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

Background and purpose

Dual-energy computed tomography (DECT) is an emerging technology in radiotherapy (RT). Here, we investigate split-filter DECT throughout the RT treatment chain as compared to single-energy CT (SECT).

Materials and methods

DECT scans were acquired with a tin-gold split-filter at 140 kV resulting in a low- and high-energy CT reconstruction (recon). Ten cancer patients (four head-and-neck (HN)​, three rectum​, two anal/pelvis and one abdomen) were DECT scanned without and with iodine administered. A cylindrical and an anthropomorphic HN phantom were scanned with DECT and 120 kV SECT. The DECT images generated were: 120 kV SECT-equivalent (CTmix), virtual monoenergetic images (VMIs), iodine map, virtual non-contrast (VNC), effective atomic number (Zeff), and relative electron density (ρe,w). The clinical utility of these recons was investigated for calibration, delineation, dose calculation and image-guided RT (IGRT).

Results

A calibration curve for 75 keV VMI had a root-mean-square-error (RMSE) of 34 HU in closest agreement with the RSME of SECT calibration. This correlated with a phantom-based dosimetric agreement to SECT of γ1%1mm > 98%. A 40 keV VMI recon was most promising to improve tumor delineation accuracy with an average evaluation score of 1.6 corresponding to "partial improvement". The dosimetric impact of iodine was in general < 2%. For this setup, VNC vs. non-contrast CTmix based dose calculations are considered equivalent. SECT- and DECT-based IGRT was in agreement within the setup uncertainty.

Conclusions

DECT-based RT could be a feasible alternative to SECT providing additional recons to support the different steps of the RT workflow.

Monte Carlo model of a prototype flat-panel detector for multi-energy applications in radiotherapy

BACKGROUND

The incorporation of multi-energy capabilities into radiotherapy flat-panel detectors offers advantages including enhanced soft tissue visualization by reduction of signal from overlapping anatomy such as bone in 2D image projections; creation of virtual monoenergetic images for 3D contrast enhancement, metal artefact reduction and direct acquisition of relative electron density. A novel dual-layer on-board imager offering dual energy processing capabilities is being designed. As opposed to other dual-energy implementation techniques which require separate acquisition with two different x-ray spectra, the dual-layer detector design enables simultaneous acquisition of high and low energy images with a single exposure. A computational framework is required to optimize the design parameters and evaluate detector performance for specific clinical applications.

PURPOSE

In this study, we report on the development of a Monte Carlo (MC) model of the imager including model validation.

METHODS

The stack-up of the dual-layer imager (DLI) was implemented in GEANT4 Application for Tomographic Emission (GATE). The DLI model has an active area of 43×43 cm2 , with top and bottom Cesium Iodide (CsI) scintillators of 600 and 800 μm thickness, respectively. Measurement of spatial resolution and imaging of dedicated multi-material dual-energy (DE) phantoms were used to validate the model. The modulation transfer function (MTF) of the detector was calculated for a 120 kVp x-ray spectrum using a 0.5 mm thick tantalum edge rotated by 2.5o . For imaging validation, the DE phantom was imaged using a 140 kVp x-ray spectrum. For both validation simulations, corresponding measurements were done using an initial prototype of the imager. Agreement between simulations and measurement was assessed using normalized root mean square error (NRMSE) and 1D profile difference for the MTF and phantom images respectively. Further comparison between measurement and simulation was made using virtual monoenergetic images (VMIs) generated from basis material images derived using precomputed look-up tables.

RESULTS

The MTF of the bottom layer of the dual-layer model shows values decreasing more quickly with spatial frequency, compared to the top layer, due to the thicker bottom scintillator thickness and scatter from the top layer. A comparison with measurement shows NRMSE of 0.013 and 0.015 as well as identical MTF50 of 0.8 mm1 and 1.0 mm1 for the top and bottom layer respectively. For the DE imaging of the DE-phantom, although a maximum deviation of 3.3% is observed for the 10 mm aluminum and Teflon inserts at the top layer, the agreement for all other inserts is less than 2.2% of the measured value at both layers. Material decomposition of simulated scatter-free DE images gives an average accuracy in PMMA and aluminum composition of 4.9% and 10.3% for 11-30 mm PMMA and 1-10 mm aluminum objects respectively. A comparison of decomposed values using scatter containing measured and simulated DE images shows good agreement within statistical uncertainty.

CONCLUSION

Validation using both MTF and phantom imaging shows good agreement between simulation and measurements. With the present configuration of the digital prototype, the model can generate material decomposed images and virtual monoenergetic images.

Milestones in CT: Past, Present, and Future

In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.

Comprehensive evaluations of a prototype full field-of-view photon counting CT system through phantom studies

Objective. Photon counting CT (PCCT) has been a research focus in the last two decades. Recent studies and advancements have demonstrated that systems using semiconductor-based photon counting detectors (PCDs) have the potential to provide better contrast, noise and spatial resolution performance compared to conventional scintillator-based systems. With multi-energy threshold detection, PCD can simultaneously provide the photon energy measurement and enable material decomposition for spectral imaging. In this work, we report a performance evaluation of our first CdZnTe-based prototype full-size PCCT system through various phantom imaging studies.Approach.This prototype system supports a 500 mm scan field-of-view and 10 mmz-coverage at isocenter. Phantom scans were acquired using 120 kVp from 50 to 400 mAs to assess the imaging performance on: CT number accuracy, uniformity, noise, spatial resolution, material differentiation and quantification.Main results.Both qualitative and quantitative evaluations show that PCCT, under the tested conditions, has superior imaging performance with lower noise and improved spatial resolution compared to conventional energy integrating detector (EID)-CT. Using projection domain material decomposition approach with multiple energy bin measurements, PCCT virtual monoenergetic images have lower noise, and good accuracy in quantifying iodine and calcium concentrations. These results lead to increased contrast-to-noise ratio (CNR) for both high and low contrast study objects compared to EID-CT at matched dose and spatial resolution. PCCT can also generate super-high resolution images using much smaller detector pixel size than EID-CT and greatly improve image spatial resolution.Significance.Improved spatial resolution and quantification accuracy with reduced image noise of the PCCT images can potentially lead to better diagnosis at reduced radiation dose compared to conventional EID-CT. Increased CNR achieved by PCCT suggests potential reduction in iodine contrast media load, resulting in better patient safety and reduced cost.

Standardized Medical Terminology for Cardiac Computed Tomography 2023 Update: An Expert Consensus Document of the Society of Cardiovascular Computed Tomography (SCCT), American Association of Physicists in Medicine (AAPM), American College of Radiology (ACR), North American Society for Cardiovascular Imaging (NASCI), and Radiological Society of North America (RSNA) with endorsement by the Asian Society of Cardiovascular Imaging (ASCI), the European Association of Cardiovascular Imaging (EACI), and the European Society of Cardiovascular Radiology (ESCR)

Since the emergence of cardiac computed tomography (Cardiac CT) at the turn of the 21st century, there has been an exponential growth in research and clinical development of the technique, with contributions from investigators and clinicians from varied backgrounds: physics and engineering, informatics, cardiology, and radiology. However, terminology for the field is not unified. As a consequence, there are multiple abbreviations for some terms, multiple terms for some concepts, and some concepts that lack clear definitions and/or usage. In an effort to aid the work of all those who seek to contribute to the literature, clinical practice, and investigation of the field, the Society of Cardiovascular Computed Tomography updates a standard set of medical terms commonly used in clinical and research activities related to cardiac CT. Keywords: Cardiac, CT, Medical Terminology Supplemental material is available for this article. This article is published synchronously in Radiology: Cardiothoracic Imaging and Journal of Cardiovascular Computed Tomography. ©2023 Society of Cardiovascular Computed Tomography. Published by RSNA with permission.

Clinical applications of photon counting detector CT

The X-ray detector is a fundamental component of a CT system that determines the image quality and dose efficiency. Until the approval of the first clinical photon-counting-detector (PCD) system in 2021, all clinical CT scanners used scintillating detectors, which do not capture information about individual photons in the two-step detection process. In contrast, PCDs use a one-step process whereby X-ray energy is converted directly into an electrical signal. This preserves information about individual photons such that the numbers of X-ray in different energy ranges can be counted. Primary advantages of PCDs include the absence of electronic noise, improved radiation dose efficiency, increased iodine signal and the ability to use lower doses of iodinated contrast material, and better spatial resolution. PCDs with more than one energy threshold can sort the detected photons into two or more energy bins, making energy-resolved information available for all acquisitions. This allows for material classification or quantitation tasks to be performed in conjunction with high spatial resolution, and in the case of dual-source CT, high pitch, or high temporal resolution acquisitions. Some of the most promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value. These include imaging of the inner ear, bones, small blood vessels, heart, and lung. This review describes the clinical benefits observed to date and future directions for this technical advance in CT imaging. KEY POINTS: • Beneficial characteristics of photon-counting detectors include the absence of electronic noise, increased iodine signal-to-noise ratio, improved spatial resolution, and full-time multi-energy imaging. • Promising applications of PCD-CT involve imaging of anatomy where exquisite spatial resolution adds clinical value and applications requiring multi-energy data simultaneous with high spatial and/or temporal resolution. • Future applications of PCD-CT technology may include extremely high spatial resolution tasks, such as the detection of breast micro-calcifications, and quantitative imaging of native tissue types and novel contrast agents.

Virtual monochromatic images of dual-energy CT as an alternative to single-energy CT: performance comparison using a detectability index for different acquisition techniques

OBJECTIVES

To investigate the performance of virtual monochromatic (VM) images with the same dose and iodine contrast as those for single-energy (SE) images using five dual-energy (DE) scanners with DE techniques: two generations of fast kV switching (FKS), two generations of dual source (DS), and one split filter (SF).

METHODS

A water-bath phantom with a diameter of 300 mm, which contains one rod-shaped phantom made of a material equivalent to soft-tissue and two rod-shaped phantoms made of diluted iodine (2 and 12 mg/mL), was scanned using both SE (120, 100, and 80 kV) and DE techniques with the same CT dose index in each scanner. The VM energy at which the CT number of the iodine rod is closest to that of each SE tube voltage was determined as the equivalent energy (Eeq). A detectability index (d') was calculated from the noise power spectrum, the task transfer functions, and a task function corresponding to each rod. The percentage of the d' value of the VM image to that of the corresponding SE image was calculated for performance comparison.

RESULTS

The average percentages of d' of FKS1, FKS2, DS1, DS2, and SF were 84.6%, 96.2%, 94.3%, 107%, and 104% for 120 kV-Eeq; 75.9%, 91.2%, 88.2%, 99.2%, and 82.6% for 100 kV-Eeq; 71.6%, 88.9%, 82.6%, 85.2%, and 62.3% for 80 kV-Eeq, respectively.

CONCLUSION

The performance of VM images was on the whole inferior to that of SE images especially at low equivalent energy levels, depending on the DE techniques and their generations.

KEY POINTS

• This study evaluated the performance of VM images with the same dose and iodine contrast as those for SE images using five DE scanners. • The performance of VM images varied with the DE techniques and their generations and was mostly inferior at low equivalent energy levels. • The results highlight the importance of distribution of available dose over the two energy levels and spectral separation for the performance improvement of VM images.

Using density computed tomography images for photon dose calculations in radiation oncology: A patient study

Background and purpose

Conventional workflows for dose calculations require conversions between Hounsfield Units (HU) and the mass or electron density for Computed Tomography (CT) images in the Treatment Planning System (TPS). These conversions are scanner- and mostly kVp-dependent. A density representation or reconstruction at the CT level can potentially simplify the workflow. This study aimed to investigate the agreement between these two methods for patients and different calculation algorithms.

Materials and methods

Density conversions for conventional HU-density conversions were first established using two phantoms with appropriate inserts. Next, the differences in density and dose calculations between both methods were assessed using 95% Limits of Agreement (LOA) Bland-Altman analysis for 44 consecutive clinical patient cases. These cases represented a mix of indications, algorithms (collapsed cone, convolution superposition, ray tracing, finite-size pencil beam, and Monte Carlo), and scan kVp (80 to 140) in two different commercial TPS.

Results

No statistically significant bias in density or dose calculations was found between the two methods. Furthermore, 95% LOAs between both methods were ±0.05 g/cm³ and ±0.1 Gy for density and dose, respectively. Small but clinically irrelevant dose differences were found in high-density gradient regions for convolution superposition calculations or CT scans with non-delayed contrast agent injections with targets nearby vessels.

Conclusions

The in vivo density-reconstructed images at the CT level were assessed to be equivalent. Therefore, they can simplify and improve clinical workflows, allowing patient-specific acquisitions for contouring and density-reconstructed images for dose calculations.

Ultra-High-Resolution Photon-Counting Detector CT Arthrography of the Ankle: A Feasibility Study

This study was designed to investigate the image quality of ultra-high-resolution ankle arthrography employing a photon-counting detector CT. Bilateral arthrograms were acquired in four cadaveric specimens with full-dose (10 mGy) and low-dose (3 mGy) scan protocols. Three convolution kernels with different spatial frequencies were utilized for image reconstruction (ρ₅₀; Br98: 39.0, Br84: 22.6, Br76: 16.5 lp/cm). Seven radiologists subjectively assessed the image quality regarding the depiction of bone, hyaline cartilage, and ligaments. An additional quantitative assessment comprised the measurement of noise and the computation of contrast-to-noise ratios (CNR). While an optimal depiction of bone tissue was achieved with the ultra-sharp Br98 kernel (S ≤ 0.043), the visualization of cartilage improved with lower modulation transfer functions at each dose level (p ≤ 0.014). The interrater reliability ranged from good to excellent for all assessed tissues (intraclass correlation coefficient ≥ 0.805). The noise levels in subcutaneous fat decreased with reduced spatial frequency (p < 0.001). Notably, the low-dose Br76 matched the CNR of the full-dose Br84 (p > 0.999) and superseded Br98 (p < 0.001) in all tissues. Based on the reported results, a photon-counting detector CT arthrography of the ankle with an ultra-high-resolution collimation offers stellar image quality and tissue assessability, improving the evaluation of miniscule anatomical structures. While bone depiction was superior in combination with an ultra-sharp convolution kernel, soft tissue evaluation benefited from employing a lower spatial frequency.

Establishing a quality assurance program for photon counting detector (PCD) CT: Tips and caveats

PURPOSE

To determine the suitability of a quality assurance (QA) program based on the American College of Radiology's (ACR) CT quality control (QC) manual to fully evaluate the unique capabilities of a clinical photon-counting-detector (PCD) CT system.

METHODS

A daily QA program was established to evaluate CT number accuracy and artifacts for both standard and ultra-high-resolution (UHR) scan modes. A complete system performance evaluation was conducted in accordance with the ACR CT QC manual by scanning the CT Accreditation Phantom with routine clinical protocols and reconstructing low-energy-threshold (T3D) and virtual monoenergetic images (VMIs) between 40 and 120 keV. Spatial resolution was evaluated by computing the modulation transfer function (MTF) for the UHR mode, and multi-energy performance was evaluated by scanning a body phantom containing four iodine inserts with concentrations between 2 and 15 mg I/cc.

RESULTS

The daily QA program identified instances when the detector needed recalibration or replacement. CT number accuracy was impacted by image type: CT numbers at 70 keV VMI were within the acceptable range (defined for 120 kV). Other keV VMIs and the T3D reconstruction had at least one insert with CT number outside the acceptable range. The limiting resolution was nearly 40 lp/cm based on MTF measurements, which far exceeds the 12 lp/cm maximum capability of the ACR phantom. The CT numbers in the iodine inserts were accurate on all VMIs (3.8% average percentage error), while the iodine concentrations had an average root mean squared error of 0.3 mg I/cc.

CONCLUSION

Protocols and parameters must be properly selected on PCD-CT to meet current accreditation requirements with the ACR CT phantom. Use of the 70 keV VMI allowed passing all tests prescribed in the ACR CT manual. Additional evaluations such an MTF measurement and multi-energy phantom scans are also recommended to comprehensively evaluate PCD-CT scanner performance.

Dual imaging agent for magnetic particle imaging and computed tomography

Magnetic particle imaging (MPI) is a novel biomedical imaging modality that allows non-invasive, tomographic, and quantitative tracking of the distribution of superparamagnetic iron oxide nanoparticle (SPION) tracers. While MPI possesses high sensitivity, detecting nanograms of iron, it does not provide anatomical information. Computed tomography (CT) is a widely used biomedical imaging modality that yields anatomical information at high resolution. A multimodal imaging agent combining the benefits of MPI and CT imaging would be of interest. Here we combine MPI-tailored SPIONs with CT-contrast hafnium oxide (hafnia) nanoparticles using flash nanoprecipitation to obtain dual-imaging MPI/CT agents. Co-encapsulation of iron oxide and hafnia in the composite nanoparticles was confirmed via transmission electron microscopy and elemental mapping. Equilibrium and dynamic magnetic characterization show a reduction in effective magnetic diameter and changes in dynamic magnetic susceptibility spectra at high oscillating field frequencies, suggesting magnetic interactions within the composite dual imaging tracers. The MPI performance of the dual imaging agent was evaluated and compared to the commercial tracer ferucarbotran. The dual-imaging agent has MPI sensitivity that is ∼3× better than this commercial tracer. However, worsening of MPI resolution was observed in the composite tracer when compared to individually coated SPIONs. This worsening resolution could result from magnetic dipolar interactions within the composite dual imaging tracer. The CT performance of the dual imaging agent was evaluated in a pre-clinical animal scanner and a clinical scanner, revealing better contrast compared to a commercial iodine-based contrast agent. We demonstrate that the dual imaging agent can be differentiated from the commercial iodine contrast agent using dual energy CT (DECT) imaging. Furthermore, the dual imaging agent displayed energy-dependent CT contrast arising from the combination of SPION and hafnia, making it potentially suitable for virtual monochromatic imaging of the contrast agent distribution using DECT.

Dual-Source Photon-Counting Computed Tomography-Part I: Clinical Overview of Cardiac CT and Coronary CT Angiography Applications

The photon-counting detector (PCD) is a new computed tomography detector technology (photon-counting computed tomography, PCCT) that provides substantial benefits for cardiac and coronary artery imaging. Compared with conventional CT, PCCT has multi-energy capability, increased spatial resolution and soft tissue contrast with near-null electronic noise, reduced radiation exposure, and optimization of the use of contrast agents. This new technology promises to overcome several limitations of traditional cardiac and coronary CT angiography (CCT/CCTA) including reduction in blooming artifacts in heavy calcified coronary plaques or beam-hardening artifacts in patients with coronary stents, and a more precise assessment of the degree of stenosis and plaque characteristic thanks to its better spatial resolution. Another potential application of PCCT is the use of a double-contrast agent to characterize myocardial tissue. In this current overview of the existing PCCT literature, we describe the strengths, limitations, recent applications, and promising developments of employing PCCT technology in CCT.

Dual Source Photon-Counting Computed Tomography-Part II: Clinical Overview of Neurovascular Applications

Photon-counting detector (PCD) is a novel computed tomography detector technology (photon-counting computed tomography-PCCT) that presents many advantages in the neurovascular field, such as increased spatial resolution, reduced radiation exposure, and optimization of the use of contrast agents and material decomposition. In this overview of the existing literature on PCCT, we describe the physical principles, the advantages and the disadvantages of conventional energy integrating detectors and PCDs, and finally, we discuss the applications of the PCD, focusing specifically on its implementation in the neurovascular field.

Quantitative and qualitative image quality assessment in shoulder examinations with a first-generation photon-counting detector CT

Photon-counting detector (PCD) CT allows for ultra-high-resolution (UHR) examinations of the shoulder without requiring an additional post-patient comb filter to narrow the detector aperture. This study was designed to compare the PCD performance with a high-end energy-integrating detector (EID) CT. Sixteen cadaveric shoulders were examined with both scanners using dose-matched 120 kVp acquisition protocols (low-dose/full-dose: CTDI_vol = 5.0/10.0 mGy). Specimens were scanned in UHR mode with the PCD-CT, whereas EID-CT examinations were conducted in accordance with the clinical standard as "non-UHR". Reconstruction of EID data employed the sharpest kernel available for standard-resolution scans (ρ₅₀ = 12.3 lp/cm), while PCD data were reconstructed with both a comparable kernel (11.8 lp/cm) and a sharper dedicated bone kernel (16.5 lp/cm). Six radiologists with 2-9 years of experience in musculoskeletal imaging rated image quality subjectively. Interrater agreement was analyzed by calculation of the intraclass correlation coefficient in a two-way random effects model. Quantitative analyses comprised noise recording and calculating signal-to-noise ratios based on attenuation measurements in bone and soft tissue. Subjective image quality was higher in UHR-PCD-CT than in EID-CT and non-UHR-PCD-CT datasets (all p < 0.001). While low-dose UHR-PCD-CT was considered superior to full-dose non-UHR studies on either scanner (all p < 0.001), ratings of low-dose non-UHR-PCD-CT and full-dose EID-CT examinations did not differ (p > 0.99). Interrater reliability was moderate, indicated by a single measures intraclass correlation coefficient of 0.66 (95% confidence interval: 0.58-0.73; p < 0.001). Image noise was lowest and signal-to-noise ratios were highest in non-UHR-PCD-CT reconstructions at either dose level (p < 0.001). This investigation demonstrates that superior depiction of trabecular microstructure and considerable denoising can be realized without additional radiation dose by employing a PCD for shoulder CT imaging. Allowing for UHR scans without dose penalty, PCD-CT appears as a promising alternative to EID-CT for shoulder trauma assessment in clinical routine.

Ultrahigh-Resolution Photon-Counting CT in Cadaveric Fracture Models: Spatial Frequency Is Not Everything

In this study, the impact of reconstruction sharpness on the visualization of the appendicular skeleton in ultrahigh-resolution (UHR) photon-counting detector (PCD) CT was investigated. Sixteen cadaveric extremities (eight fractured) were examined with a standardized 120 kVp scan protocol (CTDI_vol 10 mGy). Images were reconstructed with the sharpest non-UHR kernel (Br76) and all available UHR kernels (Br80 to Br96). Seven radiologists evaluated image quality and fracture assessability. Interrater agreement was assessed with the intraclass correlation coefficient. For quantitative comparisons, signal-to-noise-ratios (SNRs) were calculated. Subjective image quality was best for Br84 (median 1, interquartile range 1-3; p ≤ 0.003). Regarding fracture assessability, no significant difference was ascertained between Br76, Br80 and Br84 (p > 0.999), with inferior ratings for all sharper kernels (p < 0.001). Interrater agreement for image quality (0.795, 0.732-0.848; p < 0.001) and fracture assessability (0.880; 0.842-0.911; p < 0.001) was good. SNR was highest for Br76 (3.4, 3.0-3.9) with no significant difference to Br80 and Br84 (p > 0.999). Br76 and Br80 produced higher SNRs than all kernels sharper than Br84 (p ≤ 0.026). In conclusion, PCD-CT reconstructions with a moderate UHR kernel offer superior image quality for visualizing the appendicular skeleton. Fracture assessability benefits from sharp non-UHR and moderate UHR kernels, while ultra-sharp reconstructions incur augmented image noise.

Impact of the automatic tube current modulation (ATCM) system on virtual monoenergetic image quality for dual-source CT: A phantom study

PURPOSE

To assess the impact of the automatic tube current modulation (ATCM) on virtual monoenergetic images (VMIs) quality in dual-source CT(DSCT).

MATERIALS AND METHODS

Acquisitions were performed on DSCT using the Mercury phantom. The acquisition parameters for an abdomen-pelvic examination with single-energy CT(SECT) and dual-energy CT(DECT) imaging were used. Acquisitions were performed for each imaging mode using fixed mAs and ATCM. The mAs value was set to obtain a volume CT dose index of 11 mGy in fixed mAs acquisitions. This value was used as the reference mAs in ATCM acquisitions. The noise power spectrum and task-based transfer function at 40,50,60 and 70 keV levels were computed on VMIs and SECT images. The detectability index (d') was calculated for a lesion with an iodine concentration of 10 mg/mL.

RESULTS

The noise magnitude on VMIs was higher with the ATCM system than with fixed mAs for all energy levels and section diameters of 21,26 and 31 cm. The noise texture and spatial resolution were similar between the fixed mAs and ATCM acquisitions for both imaging modes. The d' values were lower for all energy levels with ATCM than with fixed mAs acquisitions for 21 and 26 cm diameters by -39.82 ± 9.32%, similar at 31 cm diameter -4.13 ± 0.24% and higher at 36 cm diameter 10.40 ± 6.69%. It was higher on VMIs at all energy levels compared to SECT images.

CONCLUSIONS

The ATCM system could be used with DECT imaging to optimize patient exposure without changing the noise texture and spatial resolution of VMIs compared to fixed mAs and SECT.

Obesity-Related Pitfalls of Virtual versus True Non-Contrast Imaging-An Intraindividual Comparison in 253 Oncologic Patients

OBJECTIVES

Dual-source dual-energy CT (DECT) facilitates reconstruction of virtual non-contrast images from contrast-enhanced scans within a limited field of view. This study evaluates the replacement of true non-contrast acquisition with virtual non-contrast reconstructions and investigates the limitations of dual-source DECT in obese patients.

MATERIALS AND METHODS

A total of 253 oncologic patients (153 women; age 64.5 ± 16.2 years; BMI 26.6 ± 5.1 kg/m²) received both multi-phase single-energy CT (SECT) and DECT in sequential staging examinations with a third-generation dual-source scanner. Patients were allocated to one of three BMI clusters: non-obese: <25 kg/m² (n = 110), pre-obese: 25-29.9 kg/m² (n = 73), and obese: >30 kg/m² (n = 70). Radiation dose and image quality were compared for each scan. DECT examinations were evaluated regarding liver coverage within the dual-energy field of view.

RESULTS

While arterial contrast phases in DECT were associated with a higher CTDI_vol than in SECT (11.1 vs. 8.1 mGy; p < 0.001), replacement of true with virtual non-contrast imaging resulted in a considerably lower overall dose-length product (312.6 vs. 475.3 mGy·cm; p < 0.001). The proportion of DLP variance predictable from patient BMI was substantial in DECT (R² = 0.738) and SECT (R² = 0.620); however, DLP of SECT showed a stronger increase in obese patients (p < 0.001). Incomplete coverage of the liver within the dual-energy field of view was most common in the obese subgroup (17.1%) compared with non-obese (0%) and pre-obese patients (4.1%).

CONCLUSION

DECT facilitates a 30.8% dose reduction over SECT in abdominal oncologic staging examinations. Employing dual-source scanner architecture, the risk for incomplete liver coverage increases in obese patients.

Dual-energy CT-based stopping power prediction for dental materials in particle therapy

Radiotherapy with protons or light ions can offer accurate and precise treatment delivery. Accurate knowledge of the stopping power ratio (SPR) distribution of the tissues in the patient is crucial for improving dose prediction in patients during planning. However, materials of uncertain stoichiometric composition such as dental implant and restoration materials can substantially impair particle therapy treatment planning due to related SPR prediction uncertainties. This study investigated the impact of using dual-energy computed tomography (DECT) imaging for characterizing and compensating for commonly used dental implant and restoration materials during particle therapy treatment planning. Radiological material parameters of ten common dental materials were determined using two different DECT techniques: sequential acquisition CT (SACT) and dual-layer spectral CT (DLCT). DECT-based direct SPR predictions of dental materials via spectral image data were compared to conventional single-energy CT (SECT)-based SPR predictions obtained via indirect CT-number-to-SPR conversion. DECT techniques were found overall to reduce uncertainty in SPR predictions in dental implant and restoration materials compared to SECT, although DECT methods showed limitations for materials containing elements of a high atomic number. To assess the influence on treatment planning, an anthropomorphic head phantom with a removable tooth containing lithium disilicate as a dental material was used. The results indicated that both DECT techniques predicted similar ranges for beams unobstructed by dental material in the head phantom. When ion beams passed through the lithium disilicate restoration, DLCT-based SPR predictions using a projection-based method showed better agreement with measured reference SPR values (range deviation: 0.2 mm) compared to SECT-based predictions. DECT-based SPR prediction may improve the management of certain non-tissue dental implant and restoration materials and subsequently increase dose prediction accuracy.

Lung dual energy CT: Impact of different technological solutions on quantitative analysis

PURPOSE

To evaluated the accuracy of spectral parameters quantification of four different CT scanners in dual energy examinations of the lung using a dedicated phantom.

METHOD

Measurements were made with different technologies of the same vendor: one dual source CT scanner (DSCT), one TwinBeam (i.e. split filter) and two sequential acquisition single source scanners (SSCT). Angular separation of Calcium and Iodine signals were calculated from scatter plots of low-kVp versus high-kVp HUs. Electron density (ρe), effective atomic number (Zeff) and Iodine concentration (Iconc) were measured using Syngo.via software. Accuracy (A) of ρe, Zeff and Iconc was evaluated as the absolute percentage difference (D%) between reference values and measured ones, while precision (P) was evaluated as the variability σ obtained by repeating the measurement with different acquisition/reconstruction settings.

RESULTS

Angular separation was significantly larger for DSCT (α = 9.7°) and for sequential SSCT (α = 9.9°) systems. TwinBeam was less performing in material separation (α = 5.0°). The lowest average A was observed for TwinBeam (A_ρe = [4.7 ± 1.0], A_Z = [9.1 ± 3.1], A_Iconc = [19.4 ± 4.4]), while the best average A was obtained for Flash (A_ρe = [1.8 ± 0.4], A_Z = [3.5 ± 0.7], A_Iconc = [7.3 ± 1.8]). TwinBeam presented inferior average P (P_ρe = [0.6 ± 0.1], P_Z = [1.1 ± 0.2], P_Iconc = [10.9 ± 4.9]), while other technologies demonstrate a comparable average.

CONCLUSIONS

Different technologies performed material separation and spectral parameter quantification with different degrees of accuracy and precision. DSCT performed better while TwinBeam demonstrated not excellent performance. Iodine concentration measurements exhibited high variability due to low Iodine absolute content in lung nodules, thus limiting its clinical usefulness in pulmonary applications.

Assessment of epicardial adipose tissue on virtual non-contrast images derived from photon-counting detector coronary CTA datasets

OBJECTIVES

To assess epicardial adipose tissue (EAT) volume and attenuation of different virtual non-contrast (VNC) reconstructions derived from coronary CTA (CCTA) datasets of a photon-counting detector (PCD) CT-system to replace true non-contrast (TNC) series.

METHODS

Consecutive patients (n = 42) with clinically indicated CCTA and coronary TNC were included. Two VNC series were reconstructed, using a conventional (VNC_Conv) and a novel calcium-preserving (VNC_PC) algorithm. EAT was segmented on TNC, VNC_Conv, VNC_PC, and CCTA (CTA_-30) series using thresholds of -190 to -30 HU and an additional segmentation on the CCTA series with an upper threshold of 0 HU (CTA₀). EAT volumes and their histograms were assessed for each series. Linear regression was used to correlate EAT volumes and the Euclidian distance for histograms. The paired t-test and the Wilcoxon signed-rank test were used to assess differences for parametric and non-parametric data.

RESULTS

EAT volumes from VNC and CCTA series showed significant differences compared to TNC (all p < .05), but excellent correlation (all R² > 0.9). Measurements on the novel VNC_PC series showed the best correlation (R² = 0.99) and only minor absolute differences compared to TNC values. Mean volume differences were -12%, -3%, -13%, and +10% for VNC_Conv, VNC_PC, CTA_-30, and CTA₀ compared to TNC. Distribution of CT values on VNC_PC showed less difference to TNC than on VNC_Conv (mean attenuation difference +7% vs. +2%; Euclidean distance of histograms 0.029 vs. 0.016).

CONCLUSIONS

VNC_PC-reconstructions of PCD-CCTA datasets can be used to reliably assess EAT volume with a high accuracy and only minor differences in CT values compared to TNC. Substitution of TNC would significantly decrease patient's radiation dose.

KEY POINTS

• Measurement of epicardial adipose tissue (EAT) volume and attenuation are feasible on virtual non-contrast (VNC) series with excellent correlation to true non-contrast series (all R²>0.9). • Differences in VNC algorithms have a significant impact on EAT volume and CT attenuation values. • A novel VNC algorithm (VNC_PC) enables reliable assessment of EAT volume and attenuation with superior accuracy compared to measurements on conventional VNC- and CCTA-series.

Impact of CT Photon-Counting Virtual Monoenergetic Imaging on Visualization of Abdominal Arterial Vessels

PURPOSE

The novel photon-counting detector (PCD) technique acquires spectral data for virtual monoenergetic imaging (VMI) in every examination. The aim of this study was the evaluation of the impact of VMI of abdominal arterial vessels on quantitative and qualitative subjective image parameters.

METHODS

A total of 20 patients that underwent an arterial phase computed tomography (CT) scan of the abdomen with a novel PCD CT (Siemens NAEOTOM alpha) were analyzed regarding attenuation at different energy levels in virtual monoenergetic imaging. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were calculated and compared between the different virtual monoenergetic (VME) levels with correlation to vessel diameter. In addition, subjective image parameters (overall subjective image quality, subjective image noise and vessel contrast) were evaluated.

RESULTS

Our research showed decreasing attenuation levels with increasing energy levels in virtual monoenergetic imaging regardless of vessel diameter. CNR showed best overall results at 60 keV, and SNR at 70 keV with no significant difference to 60 keV (p = 0.294). Subjective image quality was rated best at 70 keV for overall image quality, vessel contrast and noise.

CONCLUSIONS

Our data suggest that VMI at 60-70 keV provides the best objective and subjective image quality concerning vessel contrast irrespective of vessel size.

Spectral CT in Emergency

Spectral CT technology is based on the acquisition of CT images with X-ray at 2 different energy levels which makes possible to distinguish between materials with different atomic numbers using their energy-dependent attenuation, even if those materials have similar density at conventional CT. This kind of technology has gained wide application due to the innumerable uses of their post-processing techniques, including virtual non-contrast images, iodine maps, virtual mono-chromatic images or mixed images without increasing radiation dose. There are several applications of spectral CT in Emergency Radiology that help in the detection, diagnosis and management of various pathologies such as differentiate haemorrhage from the underlaying causative lesion, diagnosis of pulmonary embolisms, demarcation of abscess, characterization of renal stones or reduction of artifacts. The purpose of this review is to provide the emergency radiologist a brief description of the main indications for spectral CT.

Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen

BACKGROUND

Iterative reconstruction (IR) has increasingly replaced traditional reconstruction methods in computed tomography (CT). The next paradigm shift in image reconstruction is likely to come from artificial intelligence, with deep learning reconstruction (DLR) solutions already entering the clinic. An enduring disadvantage to IR has been a change in noise texture, which can affect diagnostic confidence. DLR has demonstrated the potential to overcome this issue and has recently become available for dual-energy CT.

PURPOSE

To evaluate the spatial resolution, noise properties, and detectability index of a commercially available DLR algorithm for dual-energy CT of the abdomen and compare it to single-energy (SE) CT.

METHODS

An oval 25 cm x 35 cm custom-made phantom was scanned on a GE Revolution CT scanner (GE Healthcare, Waukesha, WI) at two dose levels (13 and 5 mGy) and two iodine concentrations (8 and 2 mg/mL), using three typical abdominal scan protocols: dual-energy (DE), SE 80 kV (SE-80 kV) and SE 120 kV (SE-120 kV). Reconstructions were performed with three strengths of IR (ASiR-V: AR0%, AR50%, AR100%) and three strengths of DLR (TrueFidelity: low, medium, high). The DE acquisitions were reconstructed as mono-energetic images between 40 and 80 keV. The noise power spectrum (NPS), task transfer function (TTF), and detectability index (d') were determined for the reconstructions following the recommendations of AAPM Task Group 233.

RESULTS

Noise magnitude reductions (relative to AR0%) for the SE protocols were on average (-29%, -21%) for (AR50%, TF-M), while for DE-70 keV were (-28%, -43%). There was less reduction in mean frequency (f_av ) for DLR than for IR, with similar results for SE and DE imaging. There was, however, a substantial change in the NPS shape when using DE with DLR, quantifiable by a marked reduction in the peak frequency (f_peak ) that was absent in SE mode. All protocols and reconstructions (including AR0%) exhibited slight to moderate shifts towards lower spatial frequencies at the lower dose (<12% in f_av ). Spatial resolution was consistently superior for DLR compared to IR for SE but not for DE. All protocols and reconstructions (including AR0%) showed decreased resolution with reduced dose and iodine concentration, with less decrease for DLR compared to IR. DLR displayed a higher d' than IR. The effect of energy was large: d' increased with lower keV, and SE-80 kV had higher d' than SE-120 kV. Using DE with DLR could provide higher d' than SE-80 kV at the higher dose but not at lower dose.

CONCLUSIONS

DE imaging with DLR maintained spatial resolution and reduced noise magnitude while displaying less change in noise texture than IR. The d' was also higher with DLR than IR, suggesting superiority in detectability of iodinated contrast. Despite these trends being consistent with those previously established for SE imaging, there were some noteworthy differences. For DE imaging there was no improvement in resolution compared to IR and a change in noise texture. DE imaging with low keV and DLR had superior detectability to SE DLR at the high dose but was not better than SE-80 kV at low dose.

Comparison of image quality of two versions of deep-learning image reconstruction algorithm on a rapid kV-switching CT: a phantom study

BACKGROUND

To assess the impact of the new version of a deep learning (DL) spectral reconstruction on image quality of virtual monoenergetic images (VMIs) for contrast-enhanced abdominal computed tomography in the rapid kV-switching platform.

METHODS

Two phantoms were scanned with a rapid kV-switching CT using abdomen-pelvic CT examination parameters at dose of 12.6 mGy. Images were reconstructed using two versions of DL spectral reconstruction algorithms (DLSR V1 and V2) for three reconstruction levels. The noise power spectrum (NSP) and task-based transfer function at 50% (TTF₅₀) were computed at 40/50/60/70 keV. A detectability index (d') was calculated for enhanced lesions at low iodine concentrations: 2, 1, and 0.5 mg/mL.

RESULTS

The noise magnitude was significantly lower with DLSR V2 compared to DLSR V1 for energy levels between 40 and 60 keV by -36.5% ± 1.4% (mean ± standard deviation) for the standard level. The average NPS frequencies increased significantly with DLSR V2 by 23.7% ± 4.2% for the standard level. The highest difference in TTF₅₀ was observed at the mild level with a significant increase of 61.7% ± 11.8% over 40-60 keV energy with DLSR V2. The d' values were significantly higher for DLSR V2 versus DLSR V1.

CONCLUSIONS

The DLSR V2 improves image quality and detectability of low iodine concentrations in VMIs compared to DLSR V1. This suggests a great potential of DLSR V2 to reduce iodined contrast doses.

Emerging Technology in Musculoskeletal MRI and CT

This article provides a focused overview of emerging technology in musculoskeletal MRI and CT. These technological advances have primarily focused on decreasing examination times, obtaining higher quality images, providing more convenient and economical imaging alternatives, and improving patient safety through lower radiation doses. New MRI acceleration methods using deep learning and novel reconstruction algorithms can reduce scanning times while maintaining high image quality. New synthetic techniques are now available that provide multiple tissue contrasts from a limited amount of MRI and CT data. Modern low-field-strength MRI scanners can provide a more convenient and economical imaging alternative in clinical practice, while clinical 7.0-T scanners have the potential to maximize image quality. Three-dimensional MRI curved planar reformation and cinematic rendering can provide improved methods for image representation. Photon-counting detector CT can provide lower radiation doses, higher spatial resolution, greater tissue contrast, and reduced noise in comparison with currently used energy-integrating detector CT scanners. Technological advances have also been made in challenging areas of musculoskeletal imaging, including MR neurography, imaging around metal, and dual-energy CT. While the preliminary results of these emerging technologies have been encouraging, whether they result in higher diagnostic performance requires further investigation.

A basic study for the molecular imaging of dual-energy CT in diagnosing anterior cruciate ligament injury of knee joint

Background

Anterior cruciate ligament (ACL) injury is a common disease in clinical practice that seriously affects the daily life of patients.

Purpose

To explore the molecular imaging basis of “diminution sign on dual-energy colour mapping” for the diagnosis of ACL injury by dual-energy computed tomography (DECT).

Material and Methods

The hydroxylysine and hydroxyproline reagents were prepared in different concentrations. The grouping was shown as follows: a simple concentration change group of an amino acid (group 1/2); a mixed solution group with the concentration increasing synchronously (group 3); a mixed solution group with the concentration reverse increasing and decreasing (group 4); and a mixed solution group that fix one amino acid with increasing concentration of the other (group 5/6). The samples were scanned by DECT. The solution CT value and image signal-to-noise ratio were analyzed.

Results

In group 1/2, the brightness of the dual-energy color mapping of each test tube solution and the CT value increased with increasing the concentration of amino acid. In group 6, there was no significant change in the brightness and brilliance of the dual-energy color mapping and the CT value. The remaining three groups showed an increase in the brightness and brilliance of the dual-energy color mapping and the CT value, and this increase was positively associated with the hydroxylysine concentration.

Conclusion

The dual-energy staining of the DECT imaging in “tendon” mode is related to hydroxylysine and hydroxyproline. Moreover, the degree of dual-energy color mapping is positively correlated with the change of CT value.

Contrast thresholds for detection of various iodine concentrations in subtraction CT and dual-energy CT systems

OBJECTIVE

To estimate the minimum iodine concentrations detectable in simulated vessels of various diameters for both subtraction computed tomography (CT) and dual-energy CT systems.

METHODS

Fillable tubes (diameters: 1, 3, and 5 mm) were filled with a variety of iodine concentrations (range: 0-20 mg/ml), placed in the center of 28-mm cylindrical rods and surrounded with water. Rods with and without fillable tubes were placed in a 20-cm cylindrical solid-water phantom to simulate administration of iodine in blood vessels. The phantom was scanned with clinical subtraction CT (SCT) and dual-energy CT (DECT) head protocols to assess the detection of minimum iodine concentrations in both systems. The SCT and DECT images were evaluated quantitatively with a MATLAB script to extract regions of interest (ROIs) of each simulated vessel. ROI measurements were used to calculate the limit of detectability (LOD) and signal-to-noise ratio of Rose criteria for the assessment of the contrast thresholds.

RESULTS

Both SNR_Rose and LOD methods agreed and determined the minimum detectable iodine concentration to be 0.4 mg/ml in the 5-mm diameter vessel for SCT. However, the minimum detectable concentration in the 5-mm vessel with DECT was 1 mg/ml. The 3-mm vessel had a minimum detectable concentration of 0.8 mg/ml for SCT and 2 mg/ml for DECT. Lastly, the minimum detectable iodine concentration for the 1-mm vessel was 10 mg/ml for SCT and 10 mg/ml for DECT.

CONCLUSION

In this phantom study, SCT showed the capability to detect lower iodine concentrations compared to DECT. Contrast thresholds varied for vessels of different diameters and the smaller vessels required a higher iodine concentration for detection. Based on this knowledge, radiologists can modify their protocols to increase contrast enhancement.