Virtual Monoenergetic Images Research Articles

Improved Diagnostic Performance Using Dual-Energy CT-Derived Slope Parameter Images in Crohn's Disease.

The objective of the study is to explore the image quality and diagnosis performance of the dual-energy CT-derived slope parameter images (SPI) generated by the algorithm based on the slope function in the diagnosis of Crohn's disease (CD). Seventy-six CD patients and 53 disease-free control group subjects who underwent dual-energy CT enterography were retrospectively collected. Portal venous phase 120kVp-like and virtual monoenergetic images at 40-100keV (VMI40-100) were reconstructed. SPIs corresponding to the spectral curve between 40 and 100keV (SPI40-100) were generated using Python. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of normal and abnormal intestinal walls were calculated. Image quality, noise, and contrast were independently scored by two radiologists using a 5-point scale. Four radiologists conducted CD diagnosis with three reading models (120kVp-like, 120kVp-like with optimal VMI, and 120kVp-like with SPI40-100). The diagnostic performances of the three reading models for diagnosing CD were evaluated using receiver operating characteristic (ROC) curves. The CNR in SPI40-100 was higher than in the other images (P < 0.05). The subjective evaluation showed that there was no statistical difference between the contrast of SPI40-100 and VMI40 (P > 0.05), but that of the two images was higher than the other images (P < 0.05). The scoring on the overall image quality of VMI50 was superior to that of other images (P < 0.05). The combined model of 120kVp-like with SPI40-100 had the strongest confidence (cases with high confidence: 36, 58, 49, 47 for radiologists 1, 2, 3, 4) and the highest efficiency in diagnosing CD (areas under the ROC curve: 0.973, 0.977, 0.982, 0.991 for radiologists 1, 2, 3, 4). SPI40-100 generated by the algorithm based on the slope function exhibited good image quality. The combined model of 120kVp-like with SPI40-100 could improve radiologists' diagnostic efficiency and confidence in diagnosing CD.

An experimental study on the diagnostic advantage of dual-energy computed tomography over single-energy scan to evaluate the treatment effect following transcatheter arterial chemoembolization.

We assessed the diagnostic advantage of dual-energy computed tomography (DECT) over single-energy computed tomography (SECT) to evaluate lipiodol accumulation in target lesions following transcatheter arterial chemoembolization (TACE). TACE was performed in 10 rabbits in whom the VX2 tumor was implanted in their left liver lobes. The miriplatin-lipiodol mixture was injected into the common hepatic artery. All rabbits were sacrificed 2 days after TACE, and the liver was harvested. CT was performed using both single-energy and dual-energy scan modes. The specimen was stained with Oil Red O to evaluate lipiodol accumulation; this was considered the reference standard. Mutual information (MI) was used to evaluate the significance of radiological-pathological correlation. Estimated iodine content values on iodine material density images were compared with actual values obtained using mass spectroscopy. Mean MI values were 0.69, 0.32, 0.83, 0.72, 0.65, and 0.58 for single-energy scan; iodine density images; and virtual monoenergetic images for energy levels of 40, 60, 80, and 100 keV, respectively. The MI value of the monochromatic image (40 keV) was the highest among all sequences. However, this was not significant compared with the single-energy scan (p = 0.81). A significant correlation was observed between the estimated and actual values of iodine content (Pearson's product moment coefficient = 0.70, p = 0.023). More accurate and quantitative lipiodol evaluation in targeted tumors after TACE can be achieved by applying DECT rather than SECT.

Assessment of photon-counting Computed Tomography for quantitative imaging in radiation therapy: PCCT for quantitative imaging in radiotherapy.

To test a first generation clinical PCCT scanner's capabilities to characterize materials in an anthropomorphic head phantom for radiation therapy purposes. A CIRS 731-HN head-and-neck phantom (CIRS/SunNuclear, Norfolk, USA) was scanned on a NAEOTOM Alpha photon-counting CT (PCCT) and a SOMATOM Definition AS+ with single-energy and dual-energy CT techniques (SECT and DECT, respectively), both scanners manufactured by Siemens (Siemens Healthineers, Forscheim, Germany). A method was developed to derive relative electron density (RED) and effective atomic number (EAN) from linear attenuation coefficients (LAC) of virtual mono-energetic images and applied for the PCCT and DECT data. For DECT, Siemens' syngo.via 'Rho/Z'-algorithm was also utilized. Proton stopping-power ratios (SPR) were calculated based on RED/EAN with the Bethe equation. For SECT, a stoichiometric calibration to SPR was used. Nine materials in the phantom were segmented, excluding border pixels. Distributions and root-mean-square deviations (RMSD) within the material regions were evaluated for LAC, RED/EAN and SPR, respectively. Two example ray-projections were also examined for LAC, SPR and water-equivalent thickness (WET), for illustrations of a more treatment-like scenario. There was a tendency towards narrower distributions for PCCT compared to both DECT methods for the investigated quantities, observed across all materials for RED only. Likewise the scored RMSDs showed overall superiority for PCCT with a few exceptions: for water-like materials, EAN and SPR were comparable between the modalities; for titanium the RED and SPR estimates were inferior for PCCT. The PCCT data gave the smallest deviations from theoretic along the more complex example ray profile whereas the more standard projection showed similar results between the modalities. This study shows promising results for tissue characterization in a human-like geometry for radiotherapy purposes using PCCT. The significance of improvements for clinical practice remains to be demonstrated.

Dual-energy computed tomography: pediatric considerations.

Multidetector computed tomography (CT) has revolutionized medicine and is now a fundamental aspect of modern radiology. Hardware and software advancements have significantly improved CT accessibility, image quality, and acquisition times. While considerable attention has been directed towards the potential risks of ionizing radiation from CT scans in children, recent concerns regarding the possible short- and long-term risks related to magnetic resonance imaging (MRI) conducted under general anesthesia have generated fresh interest in novel pediatric CT applications and techniques that allow imaging of awake patients at low radiation doses. Among these novel techniques, dual-energy CT (DECT) stands out for its ability to provide enhanced diagnostic information, reduce radiation doses further, and facilitate faster scans, making it a highly promising tool in pediatric radiology. This manuscript explores the current role of DECT in pediatric imaging, emphasizing its technical foundations, hardware configurations, and various reconstruction techniques. We discuss advanced post-processing techniques, such as material decomposition algorithms and virtual monoenergetic imaging, highlighting their clinical advantages in improving diagnostic accuracy and patient outcomes. Furthermore, the paper reviews the clinical applications of DECT in evaluating pulmonary perfusion, cardiovascular assessments, and oncologic imaging in pediatric patients.

Improved quantification of coronary stenosis in patients with high calcium scores using spectral reconstructions in coronary CT angiography

Abstract Background Coronary CT angiography has demonstrated reduced accuracy in patients with high calcium scores due to blooming artifacts, often resulting in overestimated stenosis. Purpose This study aimed to assess the impact of spectral reconstructions on coronary stenosis quantification in patients with high calcium scores (&amp;gt;500), utilizing invasive coronary angiography as the gold standard. Methods Twenty consecutive patients referred for coronary artery disease assessment underwent both coronary CT angiography and invasivecoronary angiography. Conventional 120 kV images, 100 keV virtual monoenergetic images, and iodine-no-water images were reconstructed. Two blinded operators independently reviewed the images. Lesions were qualitatively classified in both CCTA and invasive angiography as stenosis of 1-24%, 25-49%, 50-70%, and &amp;gt;70%. Results A total of 87 plaques were evaluated. Mean age was 72 years (SD=7.7) and 79% were male. Pre-test probability were low, medium and high in 15.8, 26.3% and 57.9% of patients respectively. Mean calcium score was 1662, with an average dose of 7.5 mSv (67.9% retrospective studies). Nitrates were administered in 89.5% of patients, and 47.4% received intravenous beta-blockers. Sensitivity, specificity, positive predictive value, and negative predictive value to detect lesions &amp;gt; 50% improved from 94%, 57%, 38%, 97% with conventional images to 94%, 82%, 59%, 98% with 100 keV virtual monoenergetic images. Conclusion Spectral reconstructions significantly enhance diagnostic accuracy in coronary CT angiography of severely calcified coronary arteries.

Ultra-High-Resolution Photon-Counting Detector CT Benefits Visualization of Abdominal Arteries: A Comparison to Standard-Reconstruction.

This study aimedto investigate the potential benefit of ultra-high-resolution (UHR) photon-counting detector CT (PCD-CT) angiography in visualization of abdominal arteries in comparison to standard-reconstruction (SR) images of virtual monoenergetic images (VMI) at low kiloelectron volt (keV).We prospectively included 47 and 47 participants to undergo contrast-enhanced abdominal CT scans within UHR mode on a PCD-CT systemusing full-dose (FD) and low-dose (LD) protocols, respectively. The data were reconstructed into six series of images: FD_UHR_Br48, FD_UHR_Bv56, FD_UHR_Bv60, FD_SR_Bv40, LD_UHR_Bv48, and LD_SR_Bv40. The UHR reconstructions were performed with three kernels (Bv48, Bv56, and Bv60) within 0.2mm. The SR were virtual monoenergetic imaging reconstruction with Bv40 kernel at 40-keV within 1mm. Each series of axial images were reconstructed into coronal and volume-rendered images. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of seven arteries were measured. Three radiologists assessed the image quality, and visibility of nine arteries on all the images.SNR and CNR values of SR images were significantly higher than those of UHR images (P < 0.001). The SR images have higher ratings in image noise (P < 0.001), but the FD_UHR_Bv56 and FD_UHR_Bv60 images has higher rating in vessel sharpness (P < 0.001). The overall quality was not significantly different among FD_VMI_40keV, LD_VMI_40keV, FD_UHR_Bv48, and LD_UHR_Bv48 images (P > 0.05) but higher than those of FD_UHR_Bv56 and FD_UHR_Bv60 images (P < 0.001). There is no significant difference of nine abdominal arteries among six series of images of axial, coronal and volume-rendered images (P > 0.05).To conclude, 1-mm SR image of VMI at 40-keV is superior to 0.2-mm UHR regardless of which kernel is used to visualize abdominal arteries, while 0.2-mm UHR image using a relatively smooth kernel may allow similar image quality and artery visibility when thinner slice image is warranted.

Optimal weighting strategies for maximizing contrast-to-noise ratio in photon counting CT images.

Photon counting detectors (PCDs) with energy discrimination capabilities have the potential to generate grayscale CT images with improved contrast-to-noise ratio (CNR) through optimal weighting of their spectral measurements. This study evaluates the CNR performance of grayscale CT projections and images generated from spectral measurements of PCDs using three energy-weighting strategies: pre-log weighting, post-log weighting, and material decomposition (MD) weighting. This study provides the expressions of optimal weights and maximum achievable CNR of these energy-weighting strategies, which only require the knowledge of detected bin counts and do not require information of PCD energy responses or imaging techniques. We defined and solved a generalized eigenvalue problem to obtain the maximum achievable CNR in the projection domain for low-contrast tasks using three energy-weighting strategies: pre-log weighting (weighted sum of energy bin counts), post-log weighting (weighted sum of line integrals), and MD weighting (weighted sum of basis material thicknesses, which is equivalent to virtual monoenergetic images [VMIs]). These expressions only contain energy bin counts from PCD measurements. We used a realistic PCD energy response model to simulate the detected bin counts and conducted Monte Carlo simulations of different contrast tasks and phantoms to evaluate the projection- and image-domain CNR performance of these energy-weighting strategies. Additionally, the total counts method (a special case of pre-log weighting with unity weights) was included for comparison. We also conducted Gammex head and body phantom scans on an edge-on-irradiated silicon PCCT prototype to evaluate the image-domain CNR performance of these energy-weighting strategies. The results show that pre-log, post-log, and MD weighting strategies generate approximately equal projection-domain maximum achievable CNR, with a difference of less than 2%, and outperform the total counts method. These three energy-weighting strategies also generate approximately equal image-domain maximum CNR when the contrast task is located at the center of a homogeneous phantom. Pre-log weighting generates the highest image-domain CNR for an off-center contrast task location or inhomogeneous phantoms while also outperforming the total counts method. We derived the expression of projection-domain maximum achievable CNR using three energy-weighting strategies. Our results suggest that using pre-log weighting strategies enables fast grayscale CT image generation with high CNR from spectral PCD measurements for inhomogeneous phantoms and off-center region of interests (ROIs).

Deep-learning reconstruction enhances image quality of Adamkiewicz Artery in low-keV dual-energy CT.

Low-keV virtual monoenergetic images (VMIs) of dual-energy computed tomography (CT) enhances iodine contrast for detecting small arteries like the Adamkiewicz artery (AKA), but image noise can be problematic. Deep-learning image reconstruction (DLIR) effectively reduces noise without sacrificing image quality. To evaluate whether DLIR on low-keV VMIs of dual-energy CT scans improves the visualization of the AKA. We enrolled 29 patients who underwent CT angiography before aortic repair. VMIs obtained at 70 and 40 keV were reconstructed using hybrid iterative reconstruction (HIR), and 40 keV VMIs were reconstructed using DLIR. The image noise of the spinal cord, the maximum CT values of the anterior spinal artery (ASA), and the contrast-to-noise ratio (CNR) of the ASA were compared. The overall image quality and the delineation of the AKA were evaluated on a 4-point score (1 = poor, 4 = excellent). The mean image noise of the spinal cord was significantly lower on 40-keV DLIR than on 40-keV HIR scans; they were significantly higher than on 70-keV HIR images. The CNR of the ASA was highest on the 40-keV DLIR images among the three reconstruction images. The mean image quality scores for 40-keV DLIR and 70-keV HIR scans were comparable, and higher than of 40-keV HIR images. The mean delineation scores for 40-keV HIR and 40-keV DLIR scans were significantly higher than for 70-keV HIR images. Visualization of the AKA was significantly better on low-keV VMIs subjected to DLIR than conventional HIR images.

Gadolinium-based coronary CT angiography on a clinical photon-counting-detector system: a dynamic circulating phantom study

BackgroundCoronary computed tomography angiography (CCTA) offers non-invasive diagnostics of the coronary arteries. Vessel evaluation requires the administration of intravenous contrast. The purpose of this study was to evaluate the utility of gadolinium-based contrast agent (GBCA) as an alternative to iodinated contrast for CCTA on a first-generation clinical dual-source photon-counting-detector (PCD)-CT system.MethodsA dynamic circulating phantom containing a three-dimensional-printed model of the thoracic aorta and the coronary arteries were used to evaluate injection protocols using gadopentetate dimeglumine at 50%, 100%, 150%, and 200% of the maximum approved clinical dose (0.3 mmol/kg). Virtual monoenergetic image (VMI) reconstructions ranging from 40 keV to 100 keV with 5 keV increments were generated on a PCD-CT. Contrast-to-noise ratio (CNR) was calculated from attenuations measured in the aorta and coronary arteries and noise measured in the background tissue. Attenuation of at least 350 HU was deemed as diagnostic.ResultsThe highest coronary attenuation (441 ± 23 HU, mean ± standard deviation) and CNR (29.5 ± 1.5) was achieved at 40 keV and at the highest GBCA dose (200%). There was a systematic decline of attenuation and CNR with higher keV reconstructions and lower GBCA doses. Only reconstructions at 40 and 45 keV at 200% and 40 keV at 150% GBCA dose demonstrated sufficient attenuation above 350 HU.ConclusionCurrent PCD-CT protocols and settings are unsuitable for the use of GBCA for CCTA at clinically approved doses. Future advances to the PCD-CT system including a 4-threshold mode, as well as multi-material decomposition may add new opportunities for k-edge imaging of GBCA.Relevance statementPatients allergic to iodine-based contrast media and the future of multicontrast CT examinations would benefit greatly from alternative contrast media, but the utility of GBCA for coronary photon-counting-dector-CT angiography remains limited without further optimization of protocols and scanner settings.Key PointsGBCA-enhanced coronary PCD-CT angiography is not feasible at clinically approved doses.GBCAs have potential applications for the visualization of larger vessels, such as the aorta, on PCD-CT angiography.Higher GBCA doses and lower keV reconstructions achieved higher attenuation values and CNR.Graphical

Evaluation of colorectal liver metastases using virtual monoenergetic images obtained from dual-layer spectral computed tomography.

To assess the potential of virtual monoenergetic images in assessing colorectal liver metastasis (CRLM) compared with conventional CT images. This single-center, retrospective study included 173 consecutive patients (mean age, 65.5 ± 10.6years; 106 men) who underwent dual-layer spectral CT (DLSCT) between November 2016 and April 2021. Portal venous phase images were reconstructed using hybrid iterative reconstruction (iDose) and virtual monoenergetic imaging at 50keV. Four radiologists independently and randomly reviewed the de-identified iDose and 50keV images. Lesion detection, CRLM conspicuity, and CRLM diagnosis were compared between these images using a generalized estimating equation analysis. The reference standards used were histopathology and follow-up imaging findings. The study included 797 focal liver lesions, including 463 CRLMs (median size, 18.1mm [interquartile range, 10.9-37.7mm]). Lesion detection was better with 50keV images than with iDose images (45.0% [95% confidence interval [CI]: 39-50] vs 40.0% [95% CI: 34-46], P = 0.003). CRLM conspicuity was higher in the 50keV images than in the iDose images (3.27 [95% CI: 3.09-3.46] vs 3.09 [95% CI: 2.90-3.28], P < 0.001). However, the specificity for diagnosing CRLM was lower with 50keV images than with iDose images (94.5% [95% CI: 91.6-96.4] vs 96.0% [95% CI: 93.2-98.1], P = 0.022), whereas sensitivity did not differ significantly (77.6% [95% CI: 70.3-83.5] vs 76.9% [95% CI: 70.0-82.7], P = 0.736). Indeterminate lesions were more frequently noted in 50keV images than in iDose images (13% [445/3188] vs 9% [313/3188], P = 0.005), and 56% (247/445) of the indeterminate lesions at 50keV were not CRLMs. The 50keV images obtained from DLSCT were better than the iDose images in terms of CRLM conspicuity and lesion detection. However, 50keV images did not improve CRLM diagnosis but slightly increased the reporting of indeterminate focal liver lesions associated with CRLMs.

UNet-based multi-organ segmentation in photon counting CT using virtual monoenergetic images.

Multi-organ segmentation aids in disease diagnosis, treatment, and radiotherapy. The recently emerged photon counting detector-based CT (PCCT) provides spectral information of the organs and the background tissue and may improve segmentationperformance. We propose UNet-based multi-organ segmentation in PCCT using virtual monoenergetic images (VMI) to exploit spectral informationeffectively. The proposed method consists of the following steps: Noise reduction in bin-wise images, image-based material decomposition, generating VMIs, and deep learning-based segmentation. VMIs are synthesized for various x-ray energies using basis images. The UNet-based networks (3D UNet, Swin UNETR) were used for segmentation, and dice similarity coefficients (DSC) and 3D visualization of the segmented result were evaluation indicators. We validated the proposed method for the liver, pancreas, and spleen segmentation using abdominal phantoms from 55 subjects for dual- and quad-energy bins. We compared it to the conventional PCCT-based segmentation, which uses only the (noise-reduced) bin-wise images. The experiments were conducted on two cases by adjusting the doselevels. The proposed method improved the training stability for most cases. With the proposed method, the average DSC for the three organs slightly increased from 0.933 to 0.95, and the standard deviation decreased from 0.066 to 0.047, for example, in the low dose case (using VMIs v.s. bin-wise images from dual-energy bins; 3D UNet). The proposed method using VMIs improves training stability for multi-organ segmentation in PCCT, particularly when the number of energy bins issmall.

Improved Discriminability of Severe Lung Injury and Atelectasis in Thoracic Trauma at Low keV Virtual Monoenergetic Images from Photon-Counting Detector CT

Objectives: To evaluate the value of virtual monoenergetic images (VMI) from photon-counting detector CT (PCD-CT) for discriminability of severe lung injury and atelectasis in polytraumatized patients. Materials &amp; Methods: Contrast-enhanced PCD-CT examinations of 20 polytraumatized patients with severe thoracic trauma were included in this retrospective study. Spectral PCD-CT data were reconstructed using a noise-optimized virtual monoenergetic imaging (VMI) algorithm with calculated VMIs ranging from 40 to 120 keV at 10 keV increments. Injury-to-atelectasis contrast-to-noise ratio (CNR) was calculated and compared at each energy level based on CT number measurements in severely injured as well as atelectatic lung areas. Three radiologists assessed subjective discriminability, noise perception, and overall image quality. Results: CT values for atelectasis decreased as photon energy increased from 40 keV to 120 keV (mean Hounsfield units (HU): 69 at 40 keV; 342 at 120 keV), whereas CT values for severe lung injury remained near-constant from 40 keV to 120 keV (mean HU: 42 at 40 keV; 44 at 120 keV) with significant differences at each keV level (p &lt; 0.001). The optimal injury-to-atelectasis CNR was observed at 40 keV in comparison with the remaining energy levels (p &lt; 0.001) except for 50 keV (p &gt; 0.05). In line with this, VMIs at 40 keV were rated best regarding subjective discriminability. VMIs at 60–70 keV, however, provided the highest subjective observer parameters regarding subjective image noise as well as image quality. Conclusions: Discriminability between severely injured and atelectatic lung areas after thoracic trauma can be substantially improved by virtual monoenergetic imaging from PCD-CT with superior contrast and visual discriminability at 40–50 keV.

Association of liver multi-parameter quantitative metrics determined by dual-layer spectral detector computed tomography (SDCT) with coronary plaque scores.

Hepatic steatosis is closely related to the occurrence and development of coronary plaques. Spectral detector computed tomography (SDCT) can provide more precise multiparameter quantitative parameters for hepatic steatosis. Hence, the purpose of this cross-sectional study was to explore the effect of quantitative liver metrics measured using SDCT on the extent and severity of coronary plaques. In patients who underwent upper abdomen unenhanced SDCT and coronary computed tomography angiography, plaque extent and severity were assessed using segmental involvement score (SIS) and segmental stenosis score (SSS). Liver fat quantification was evaluated by polychromatic and virtual mono-energetic images at 40 and 70 kev, spectral attenuation curve slope, and effective atomic number (CT40 keV, CT70 kev, λHU, and Zeff, respectively). A logistic regression model evaluated the factors influencing high SIS and SSS. Enrolled patients (n=644) were divided into groups: low SIS (<5) (n=451), high SIS (≥5) (n=193), low SSS (<5) (n=461), and high SSS (≥5) (n=183). Zeff was more closely correlated with SIS (standard partial regression coefficient =-0.422, P<0.001) and SSS (standard partial regression coefficient =-0.346, P<0.001). Zeff was divided into four groups using interquartile intervals. Compared with the patients in the lowest quartile, those in the second [odds ratio (OR) =2.116, 95% confidence interval (CI): 1.134-3.949, P=0.018], third (OR =2.832, 95% CI: 1.461-5.491, P=0.002), and fourth (OR =3.584, 95% CI: 1.857-6.918, P<0.001) quartiles showed higher risk for high SIS. And correspondingly, the second (OR =1.933, 95% CI: 1.040-3.592, P=0.037), third (OR =2.900, 95% CI: 1.499-5.609, P=0.002), and fourth (OR =3.368, 95% CI: 1.743-6.510, P<0.001) quartiles showed higher risk for high SSS, especially in those who were <60 years old, male and had visceral adipose tissue/subcutaneous adipose tissue <1.18. The SDCT-Zeff was an independent factor associated with high SIS and SSS. The quantification of liver fat may be useful for evaluating the risk and prognosis of coronary atherosclerosis.

Head and neck automatic multi-organ segmentation on Dual-Energy Computed Tomography

Background and purposeDeep-learning-based automatic segmentation is widely used in radiation oncology to delineate organs-at-risk. Dual-energy CT (DECT) allows the reconstruction of enhanced contrast images that could help with manual and auto-delineation. This paper presents a performance evaluation of a commercial auto-segmentation software on image series generated by a DECT. Material and methodsDifferent types of DECT images from seventy four head-and-neck (HN) patients were retrieved, including polyenergetic images at different voltages [80 kV reconstructed with a kernel corresponding to the commercial algorithm DirectDensity™ (PEI80-DD), 80 kV (PEI80), 120 kV-mixed (PEI120)] and a virtual-monoenergetic image at 40 keV (VMI40). Delineations used for treatment planning were considered as ground truth (GT) and were compared with the auto-segmentations performed on the 4 DECT images. A blinded qualitative evaluation of 3 structures (thyroid, left parotid, left nodes level II) was carried out. Performance metrics were calculated for thirteen HN structures to evaluate the auto-contours including dice similarity coefficient (DSC), 95th percentile Hausdorff distance (95HD) and mean surface distance (MSD). ResultsWe observed a high rate of low scores for PEI80-DD and VMI40 auto-segmentations on the thyroid and for GT and VMI40 contours on the nodes level II. All images received excellent scores for the parotid glands. The metrics comparison between GT and auto-segmented contours revealed that PEI80-DD had the highest DSC scores, significantly outperforming other reconstructed images for all organs (p < 0.05). ConclusionsThe results indicate that the auto-contouring system cannot generalize to images derived from DECT acquisition. It is therefore crucial to identify which organs benefit from these acquisitions to adapt the training datasets accordingly.

Photon-counting detector computed tomography: iodine density versus virtual monoenergetic imaging of pancreatic ductal adenocarcinoma.

Photon-counting detector computed tomography: iodine density versus virtual monoenergetic imaging of pancreatic ductal adenocarcinoma.

Carotid artery assessment in dual-source photon-counting CT: impact of low-energy virtual monoenergetic imaging on image quality, vascular contrast and diagnostic assessability.

Preliminary dual-energy CT studies have shown that low-energy virtual monoenergetic (VMI) + reconstructions can provide superior image quality compared to standard 120kV CTA series. The purpose of this study is to evaluate the impact of low-energy VMI reconstructions on quantitative and qualitative image quality, vascular contrast, and diagnostic assessability of the carotid artery in patients undergoing photon-counting CTA examinations. A total of 122 patients (67 male) who had undergone dual-source photon-counting CTA scans of the carotid artery were retrospectively analyzed in this study. Standard 120kV CT images and low-keV VMI series from 40 to 100keV with an interval of 15keV were reconstructed. Quantitative analyses included the evaluation of vascular CT numbers, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). CT number measurements were performed in the common, external, and internal carotid arteries. Qualitative analyses were performed by three board-certified radiologists independently using five-point scales to evaluate image quality, vascular contrast, and diagnostic assessability of the carotid artery. Mean attenuation, CNR and SNR values were highest in 40keV VMI reconstructions (HU, 1362.32 ± 457.81; CNR, 33.19 ± 12.86; SNR, 34.37 ± 12.89) followed by 55-keV VMI reconstructions (HU, 736.94 ± 150.09; CNR, 24.49 ± 7.11; SNR, 26.25 ± 7.34); all three mean values at these keV levels were significantly higher compared with the remaining VMI series and standard 120kV CT series (HU, 154.43 ± 23.69; CNR, 16.34 ± 5.47; SNR, 24.44 ± 7.14) (p < 0.0001). The qualitative analysis showed the highest rating scores for 55keV VMI reconstructions followed by 40keV and 70keV VMI series with a significant difference compared to standard 120kV CT images series regarding image quality, vascular contrast, and diagnostic assessability of the carotid artery (all comparisons, p < 0.01). Low-keV VMI reconstructions at a level of 40-55keV significantly improve image quality, vascular contrast, and the diagnostic assessability of the carotid artery compared with standard CT series in photon-counting CTA.

Eigenbin compression for reducing photon-counting CT data size.

Photon-counting CT (PCCT) systems acquire multiple spectral measurements at high spatial resolution, providing numerous image quality benefits while also increasing the amount of data that must be transferred through the gantry slip ring. This study proposes a lossy method to compress photon-counting CT data using eigenvector analysis, with the goal of providing image quality sufficient for applications that require a rapid initial reconstruction, such as to confirm anatomical coverage, scan quality, and to support automated advanced applications. The eigenbin compression method was experimentally evaluated on a clinical silicon PCCT prototype system. The proposed eigenbin method performs principal component analysis (PCA) on a set of PCCT calibration measurements. PCA finds the orthogonal axes or eigenvectors, which capture the maximum variance in the N dimensional photon-count data space, where N is the number of acquired energy bins. To reduce the dimensionality of the PCCT data, the data are linearly transformed into a lower dimensional space spanned by the M<N eigenvectors with highest eigenvalues (i.e., the vectors that account for most of the information in the data). Only M coefficients are then transferred per measurement, which we term eigenbin values. After transmission, the original N energy-bin measurements are estimated as a linear combination of the M eigenvectors. Two versions of the eigenbin method were investigated: pixel-specific and pixel-general. The pixel-specific eigenbin method determines eigenvectors for each individual detector pixel, while the more practically realizable pixel-general eigenbin method finds one set of eigenvectors for the entire detector array. The eigenbin method was experimentally evaluated by scanning a 20cm diameter Gammex Multienergy phantom with different material inserts on a clinical silicon-based PCCT prototype. The method was evaluated with the number of eigenbins varied between two and four. In each case, the eigenbins were used to estimate the original 8-bin data, after which material decomposition was performed. The mean, standard deviation, and contrast-to-noise ratio (CNR) of values in the reconstructed basis and virtual monoenergetic images (VMI) were compared for the original 8-bin data and for the eigenbin data. The pixel-specific eigenbin method reduced photon-counting CT data size by a factor of four with<5% change in mean values and a small noise penalty (mean change in noise of<12%, maximum change in noise of 20% for basis images). The pixel-general eigenbin compression method reduced data size by a factor of 2.67 with<5% change in mean values and a less than 10% noise penalty in the basis images (average noise penalty≤5%). The noise penalty and errors were less for the VMIs than for the basis images, resulting in<5% change in CNR in the VMIs. The eigenbin compression method reduced photon-counting CT data size by a factor of two to four with less than 5% change in mean values, noise penalty of less than 10%-20%, and change in CNR ranging from 15% decrease to 24% increase. Eigenbin compression reduces the data transfer time and storage space of photon-counting CT data for applications that require rapid initial reconstructions.

Role of Virtual Monoenergetic Images in the Assessment of Vessel Enhancement in Segmental Level in Third-Generation Dual-Source Dual-Energy CT Pulmonary Angiography—A Prospective Study

Abstract Introduction Pulmonary embolism is the third most common cause of cardiovascular death worldwide and imaging plays a pivotal role in establishing the diagnosis. Computed tomography pulmonary angiography (CTPA) scores over other modalities and is the current diagnostic investigation of choice. In this study, we assessed the main pulmonary artery and its corresponding segmental artery attenuation in reconstructed virtual monoenergetic (mono plus) images (VMI-MP) and linear blended images (spectral post processing, SPP) obtained from dual-energy CTPA. The values were compared using contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). Materials and Methods Forty patients (mean age group, 53.6 years; 26 females and 14 males) with segmental pulmonary thromboembolism were included in this prospective study. The patients underwent CTPA study using bolus tracking in the dual-source CT-SOMATOM Force, Siemens. Postcontrast datasets (90 kV, 150 kV, and SPP) were used to reconstruct the monoenergetic images using syngo.via software virtually. Comparison was done between bivariate samples using the paired sample t-test. Results The mean Hounsfield unit (HU) artery in the left lung for VMI-MP and SPP images were 886.9 ± 242 and 356.8 ± 121.3 HU, respectively. Similarly, for the right lung, it was 868.3 ± 243.5 and 336.1 ± 105.5 HU, respectively. The mean attenuation of the arteries in MP images was higher and statistically significant (p-value &lt;0.005). Likewise, the CNR) and SNR were found to have a statistically significant p-value (&lt;0.005). An acceptable increase in image noise was seen in VMI as compared with SPP images. Conclusion Low-keV VMIs perform more effectively than the conventional polyenergetic spectrum to evaluate vessel attenuation, which in turn increases thrombus detectability. The increased CNR in VMI enables improved lesion conspicuity.

Dual-energy CT Radiomics Combined with Quantitative Parameters for Differentiating Lung Adenocarcinoma From Squamous Cell Carcinoma: A Dual-center Study

Rationale and ObjectivesTo evaluate the ability of dual-energy CT(DECT)-based quantitative parameters and radiomics features to differentiate solid lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC). MethodsThis study included 213 patients diagnosed with ADC and SCC who underwent DECT scans at two centers from November 2022 to December 2023. Patients at center 1 were randomly divided into training(n=114) and internal test set (n=50) in a 7:3 ratio, with center 2 serving as the external test set (n=49). Radiologic and clinical data were combined to establish a clinical-radiologic model. Ten types of DECT energy images including conventional images, iodine density (ID), effective atomic number (Zeff), electron density, and virtual monoenergetic images (VMI) were reconstructed in both arterial phases (AP) and venous phases (VP). Quantitative parameters were measured at the uniform enhanced solid portion of the tumor and normalized to the aorta, used to develop a quantification model and calculate the quantitative score (quantscore). Radiologists manually delineated the tumor ROI at the largest level for extracting radiomics features in these 10 energy images. These features were used to establish ten uni-energy models from which the best-performing features were selected to construct the final radiomics model and calculate a radiomics score (radscore). Then, a combined model was developed using the akaike information criterion(AIC) and compared with the clinical-radiological model to test its diagnostic validity. ResultsThe independent predictors of the clinical-radiological model included age, gender, and central or peripheral location, and the AUCs for the training set, internal test set, and external test set were 0.808, 0.837, and 0.802. The quantification model incorporated 40keV CT values, Zeff, normalized Zeff, and the slope of the spectral attenuation curve (λHU) in the AP and normalized ID, Zeff, and λHU in the VP. Uni-energy models based on AP ID maps, AP Zeff maps, and VP VMI 65keV significantly outperformed AUC = 0.5, and 11 radiomics features were selected from these three models to construct the final radiomics model. The combined model, incorporating age, gender, quantscore, and radscore, significantly outperformed the clinical-radiological model in the training set (AUC = 0.952 vs 0.808, P < 0.001), and demonstrated higher performance in both the internal and external test sets, although these differences did not reach statistical significance (AUC = 0.870 vs 0.837, for the internal test set [P = 0.542], 0.888 vs 0.802 for the external test sets [P = 0.128]). The evaluation of the combined model demonstrated good discriminative ability and potential for generalization. ConclusionsThe combined model, integrating quantitative parameters and radiomics features from DECT multi-energy images with clinical-radiological characteristics, can be used as a non-invasive tool to differentiate ADC from SCC.

Head-to-head comparison of contrast-enhanced CT, dual-layer spectral-detector CT, and Gd-EOB-DTPA-enhanced MR in detecting neuroendocrine tumor liver metastases

PurposeTo explore the optimal of kiloelectron voltage (keV) of virtual monoenergetic imaging (VMI) of dual-layer spectral-detector CT (DLCT) in detecting neuroendocrine tumor liver metastases (NETLM) and to investigate diagnostic performance of polyenergetic images (PEI), DLCT, and Gd-EOB-DTPA-enhanced MR. MethodsSeventy-two patients with suspected NETLM who underwent DLCT and Gd-EOB-DTPA-enhanced MR were retrospectively enrolled. Tumor signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between PEI and VMI at 40–140 keV. Two radiologists read the CT examinations with and without VMI separately in consensus. Two other radiologists read the Gd-EOB-DTPA-enhanced MR in consensus. The diagnostic performance was evaluated. Reference standard was histopathology, follow-up, and interpretation of all available imaging. ResultsThe highest SNR and CNR were observed at VMI40keV, significantly higher than PEI in the arterial and venous phases (all P<0.01). A total of 477 lesions were identified (396 metastases, 81 benign lesions). Per-lesion AUC was 0.86, 0.91, and 0.97 (PEI, DLCT, and Gd-EOB-DTPA-enhanced MR, respectively). Sensitivity of PEI, DLCT, and Gd-EOB-DTPA-enhanced MRI were 0.76, 0.86, and 0.95, respectively. DLCT significantly improved sensitivity compared to PEI. MR had significantly higher sensitivity than DLCT and PEI. Subgroup analysis demonstrated that the difference in diagnostic performance was concentrated on lesions < 10 mm. ConclusionThe image quality of VMI40keV is higher than that of PEI. DLCT with VMI40keV provides better diagnostic sensitivity for NETLM detection than PEI. Gd-EOB-DTPA-enhanced MR yielded the best diagnostic performance for NETLM detection.