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

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

W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations. We theoretically and experimentally determined that combining the concepts of data augmentation optimized for GAN training (DAG) and Wasserstein GAN yields a considerably stable generation of synthetic images and effectively aligns their distribution with that of real images, thereby achieving a high degree of similarity. The classification model was trained using real and synthetic samples. Consequently, the GAN technique used in the diagnostic test had an improved F1 score of approximately 7.8% compared with CA. The final F1 score was 80.24%, and the recall and precision were 84.3% and 88.7%, respectively. The results obtained using the augmented samples outperformed those obtained using pure real samples without augmentation. In addition, we adopted explainable AI techniques that leverage a class activation map (CAM) and principal component analysis to facilitate visual analysis of the network's results. The framework was designed to suggest an attention map and scattering plot to visually explain the disease predictions of the network.

Breast density quantification in dual-energy mammography using virtual anthropomorphic phantoms.

PURPOSE: Breast density is a significant risk factor for breast cancer and can impact the sensitivity of screening mammography. Area-based breast density measurements may not provide an accurate representation of the tissue distribution, therefore volumetric breast density (VBD) measurements are preferred. Dual-energy mammography enables volumetric measurements without additional assumptions about breast shape. In this work we evaluated the performance of a dual-energy decomposition technique for determining VBD by applying it to virtual anthropomorphic phantoms. METHODS: The dual-energy decomposition formalism was used to quantify VBD on simulated dual-energy images of anthropomorphic virtual phantoms with known tissue distributions. We simulated 150 phantoms with volumes ranging from 50 to 709 mL and VBD ranging from 15% to 60%. Using these results, we validated a correction for the presence of skin and assessed the method's intrinsic bias and variability. As a proof of concept, the method was applied to 14 sets of clinical dual-energy images, and the resulting breast densities were compared to magnetic resonance imaging (MRI) measurements. RESULTS: Virtual phantom VBD measurements exhibited a strong correlation (Pearson's r > 0.95 $r > 0.95$ ) with nominal values. The proposed skin correction eliminated the variability due to breast size and reduced the bias in VBD to a constant value of -2%. Disagreement between clinical VBD measurements using MRI and dual-energy mammography was under 10%, and the difference in the distributions was statistically non-significant. VBD measurements in both modalities had a moderate correlation (Spearman's ρ $\rho \ $ = 0.68). CONCLUSIONS: Our results in virtual phantoms indicate that the material decomposition method can produce accurate VBD measurements if the presence of a third material (skin) is considered. The results from our proof of concept showed agreement between MRI and dual-energy mammography VBD. Assessment of VBD using dual-energy images could provide complementary information in dual-energy mammography and tomosynthesis examinations.

An adult and pediatric size-based contrast administration reduction phantom study for single and dual-energy CT through preservation of contrast-to-noise ratio.

BACKGROUND: Global shortages of iodinated contrast media (ICM) during COVID-19 pandemic forced the imaging community to use ICM more strategically in CT exams. PURPOSE: The purpose of this work is to provide a quantitative framework for preserving iodine CNR while reducing ICM dosage by either lowering kV in single-energy CT (SECT) or using lower energy virtual monochromatic images (VMI) from dual-energy CT (DECT) in a phantom study. MATERIALS AND METHODS: In SECT study, phantoms with effective diameters of 9.7, 15.9, 21.1, and 28.5 cm were scanned on SECT scanners of two different manufacturers at a range of tube voltages. Statistical based iterative reconstruction and deep learning reconstruction were used. In DECT study, phantoms with effective diameters of 20, 29.5, 34.6, and 39.7 cm were scanned on DECT scanners from three different manufacturers. VMIs were created from 40 to 140 keV. ICM reduction by lowering kV levels for SECT or switching from SECT to DECT was calculated based on the linear relationship between iodine CNR and its concentration under different scanning conditions. RESULTS: On SECT scanner A, while matching CNR at 120 kV, ICM reductions of 21%, 58%, and 72% were achieved at 100, 80, and 70 kV, respectively. On SECT scanner B, 27% and 80% ICM reduction was obtained at 80 and 100 kV. On the Fast-kV switch DECT, with CNR matched at 120 kV, ICM reductions were 35%, 30%, 23%, and 15% with VMIs at 40, 50, 60, and 68 keV, respectively. On the dual-source DECT, ICM reductions were 52%, 48%, 42%, 33%, and 22% with VMIs at 40, 50, 60, 70, and 80 keV. On the dual-layer DECT, ICM reductions were 74%, 62%, 45%, and 22% with VMIs at 40, 50, 60, and 70 keV. CONCLUSIONS: Our work provided a quantitative baseline for other institutions to further optimize their scanning protocols to reduce the use of ICM.

Robustness of radiomic features in healthy abdominal parenchyma of patients with repeated examinations on dual-layer dual-energy CT.

OBJECTIVES: Robustness of radiomic features in physiological tissue is an important prerequisite for quantitative analysis of tumor biology and response assessment. In contrast to previous studies which focused on different tumors with mostly short scan-re-scan intervals, this study aimed to evaluate the robustness of radiomic features in cancer-free patients and over a clinically encountered inter-scan interval. MATERIALS AND METHODS: Patients without visible tumor burden who underwent at least two portal-venous phase dual energy CT examinations of the abdomen between May 2016 and January 2020 were included, while macroscopic tumor burden was excluded based upon follow-up imaging for all patients (≥3 months). Further, patients were excluded if no follow-up imaging was available, or if the CT protocol showed deviations between repeated examinations. Circular regions of interest were placed and proofread by two board-certified radiologists (4 years and 5 years experience) within the liver (segments 3 and 6), the psoas muscle (left and right), the pancreatic head, and the spleen to obtain radiomic features from normal-appearing organ parenchyma using PyRadiomics. Radiomic feature robustness was tested using the concordance correlation coefficient with a threshold of 0.75 considered indicative for deeming a feature robust. RESULTS: In total, 160 patients with 480 repeated abdominal CT examinations (range: 2-4 per patient) were retrospectively included in this single-center, IRB-approved study. Considering all organs and feature categories, only 4.58 % (25/546) of all features were robust with the highest rate being found in the first order feature category (20.37 %, 22/108). Other feature categories (grey level co-occurrence matrix, grey level dependence matrix, grey level run length matrix, grey level size zone matrix, and neighborhood gray-tone difference matrix) yielded an overall low percentage of robust features (range: 0.00 %-1.19 %). A subgroup analysis revealed the reconstructed field of view and the X-ray tube current as determinants of feature robustness (significant differences in subgroups for all organs, p < 0.001) as well as the size of the region of interest (no significant difference for the pancreatic head with p = 0.135, significant difference with p < 0.001 for all other organs). CONCLUSION: Radiomic feature robustness obtained from cancer-free subjects with repeated examinations using a consistent protocol and CT scanner was limited, with first order features yielding the highest proportion of robust features.

Conversion of single-energy CT to parametric maps of dual-energy CT using convolutional neural network.

OBJECTIVES: We propose a deep learning (DL) multitask learning framework using convolutional neural network for a direct conversion of single-energy CT (SECT) to 3 different parametric maps of dual-energy CT (DECT): virtual-monochromatic image (VMI), effective atomic number (EAN), and relative electron density (RED). METHODS: We propose VMI-Net for conversion of SECT to 70, 120, and 200 keV VMIs. In addition, EAN-Net and RED-Net were also developed to convert SECT to EAN and RED. We trained and validated our model using 67 patients collected between 2019 and 2020. Single-layer CT images with 120 kVp acquired by the DECT (IQon spectral CT; Philips Healthcare, Amsterdam, Netherlands) were used as input, while the VMIs, EAN, and RED acquired by the same device were used as target. The performance of the DL framework was evaluated by absolute difference (AD) and relative difference (RD). RESULTS: The VMI-Net converted 120 kVp SECT to the VMIs with AD of 9.02 Hounsfield Unit, and RD of 0.41% compared to the ground truth VMIs. The ADs of the converted EAN and RED were 0.29 and 0.96, respectively, while the RDs were 1.99% and 0.50% for the converted EAN and RED, respectively. CONCLUSIONS: SECT images were directly converted to the 3 parametric maps of DECT (ie, VMIs, EAN, and RED). By using this model, one can generate the parametric information from SECT images without DECT device. Our model can help investigate the parametric information from SECT retrospectively. ADVANCES IN KNOWLEDGE: DL framework enables converting SECT to various high-quality parametric maps of DECT.

Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio, a theoretical study.

BACKGROUND: Dual-energy CT (DECT) systems provide valuable material-specific information by simultaneously acquiring two spectral measurements, resulting in superior image quality and contrast-to-noise ratio (CNR) while reducing radiation exposure and contrast agent usage. The selection of DECT scan parameters, including x-ray tube settings and fluence, is critical for the stability of the reconstruction process and hence the overall image quality. PURPOSE: The goal of this study is to propose a systematic theoretical method for determining the optimal DECT parameters for minimal noise and maximum CNR in virtual monochromatic images (VMIs) for fixed subject size and total radiation dose. METHODS: The noise propagation in the process of projection based material estimation from DECT measurements is analyzed. The main components of the study are the mean pixel variances for the sinogram and monochromatic image and the CNR, which were shown to depend on the Jacobian matrix of the sinograms-to-DECT measurements map. Analytic estimates for the mean sinogram and monochromatic image pixel variances and the CNR as functions of tube potentials, fluence, and VMI energy are derived, and then used in a virtual phantom experiment as an objective function for optimizing the tube settings and VMI energy to minimize the image noise and maximize the CNR. RESULTS: It was shown that DECT measurements corresponding to kV settings that maximize the square of Jacobian determinant values over a domain of interest lead to improved stability of basis material reconstructions. Instances of non-uniqueness in DECT were addressed, focusing on scenarios where the Jacobian determinant becomes zero within the domain of interest despite significant spectral separation. The presence of non-uniqueness can lead to singular solutions during the inversion of sinograms-to-DECT measurements, underscoring the importance of considering uniqueness properties in parameter selection. Additionally, the optimal VMI energy and tube potentials for maximal CNR was determined. When the x-ray beam filter material was fixed at 2 mm of aluminum and the photon fluence for low and high kV scans were considered equal, the tube potential pair of 60/120 kV led to the maximal iodine CNR in the VMI at 53 keV. CONCLUSIONS: Optimizing DECT scan parameters to maximize the CNR can be done in a systematic way. Also, choosing the parameters that maximize the Jacobian determinant over the set of expected line integrals leads to more stable reconstructions due to the reduced amplification of the measurement noise. Since the values of the Jacobian determinant depend strongly on the imaging task, careful consideration of all of the relevant factors is needed when implementing the proposed framework.

Pancreatic cancer detection with dual-energy CT: diagnostic performance of 40 keV and 70 keV virtual monoenergetic images.

PURPOSE: To compare the diagnostic performance of 40 keV and 70 keV virtual monoenergetic images (VMIs) generated from dual-energy CT in the detection of pancreatic cancer. METHODS: This retrospective study included patients who underwent pancreatic protocol dual-energy CT from January 2019 to August 2022. Four radiologists (1-11 years of experience), who were blinded to the final diagnosis, independently and randomly interpreted 40 keV and 70 keV VMIs and graded the presence or absence of pancreatic cancer. For each image set (40 keV and 70 keV VMIs), the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The diagnostic performance of each image set was compared using generalized estimating equations. RESULTS: Overall, 137 patients (median age, 71 years; interquartile range, 63-78 years; 77 men) were included. Among them, 62 patients (45%) had pathologically proven pancreatic cancer. The 40 keV VMIs had higher specificity (75% vs. 67%; P < .001), PPV (76% vs. 71%; P < .001), and accuracy (85% vs. 81%; P = .001) than the 70 keV VMIs. On the contrary, 40 keV VMIs had lower sensitivity (96% vs. 98%; P = .02) and NPV (96% vs. 98%; P = .004) than 70 keV VMIs. However, the diagnostic confidence in patients with (P < .001) and without (P = .001) pancreatic cancer was improved in 40 keV VMIs than in 70 keV VMIs. CONCLUSIONS: The 40 keV VMIs showed better diagnostic performance in diagnosing pancreatic cancer than the 70 keV VMIs, along with higher reader confidence.

Value of virtual non-contrast images to identify uncomplicated cystic renal lesions: photon-counting detector CT vs. dual-energy integrating detector CT.

PURPOSE: To investigate the value of photon-counting detector CT (PCD-CT) derived virtual non-contrast (VNC) reconstructions to identify renal cysts in comparison with conventional dual-energy integrating detector (DE EID) CT-derived VNC reconstructions. MATERIAL AND METHODS: We prospectively enrolled consecutive patients with simple renal cysts (Bosniak classification-Version 2019, density ≤ 20 HU and/or enhancement ≤ 20 HU) who underwent multiphase (non-contrast, arterial, portal venous phase) PCD-CT and for whom non-contrast and portal venous phase DE EID-CT was available. Subsequently, VNC reconstructions were calculated for all contrast phases and density as well as contrast enhancement within the cysts were measured and compared. MRI and/or ultrasound served as reference standards for lesion classification. RESULTS: 19 patients (1 cyst per patient; age 69.5 ± 10.7 years; 17 [89.5%] male) were included. Density measurements on PCD-CT non-contrast and VNC reconstructions (arterial and portal venous phase) revealed no significant effect on HU values (p = 0.301). In contrast, a significant difference between non-contrast vs. VNC images was found for DE EID-CT (p = 0.02). For PCD-CT, enhancement for VNC reconstructions was < 20 HU for all evaluated cysts. DE EID-CT measurements revealed an enhancement of > 20 HU in five lesions (26.3%) using the VNC reconstructions, which was not seen with the non-contrast images. CONCLUSION: PCD-CT-derived VNC images allow for reliable and accurate characterization of simple cystic renal lesions similar to non-contrast scans whereas VNC images calculated from DE EID-CT resulted in substantial false characterization. Thus, PCD-CT-derived VNC images may substitute for non-contrast images and reduce radiation dose and follow-up imaging.

Effect of X-ray tube on image quality and pancreatic ductal adenocarcinoma conspicuity in pancreatic protocol dual-energy CT.

AIM: To compare the radiation dose, image quality, and conspicuity of pancreatic ductal adenocarcinoma (PDAC) in pancreatic protocol dual-energy computed tomography (CT) between two X-ray tubes mounted in the same CT machine. MATERIAL AND METHODS: This retrospective study comprised 80 patients (median age, 73 years; 45 men) who underwent pancreatic protocol dual-energy CT from January 2019 to March 2022 using either old (Group A, n=41) or new (Group B, n=39) X-ray tubes mounted in the same CT machine. The imaging parameters were completely matched between the two groups, and CT data were reconstructed at 70 and 40 keV. The CT dose-index volume (CTDIvol); CT attenuation of the abdominal aorta, pancreas, and PDAC; background noise; and qualitative scores for the image noise, overall image quality, and PDAC conspicuity were compared between the two groups. RESULTS: The CTDIvol was lower in Group B than Group A (7.9 versus 9.2 mGy; p<0.001). The CT attenuation of all anatomical structures at 70 and 40 keV was comparable between the two groups (p=0.06-0.78). The background noise was lower in Group B than Group A (12 versus 14 HU at 70 keV, p=0.046; and 26 versus 30 HU at 40 keV, p<0.001). Qualitative scores for image noise and overall image quality at 70 and 40 keV and PDAC conspicuity at 40 keV were higher in Group B than Group A (p<0.001-0.045). CONCLUSION: The latest X-ray tube could reduce the radiation dose and improve image quality in pancreatic protocol dual-energy CT.

Imaging of intracranial hemorrhage in photon counting computed tomography using virtual monoenergetic images.

PURPOSE: To determine the optimal virtual monoenergetic image (VMI) for detecting and assessing intracranial hemorrhage in unenhanced photon counting CT of the head based on the evaluation of quantitative and qualitative image quality parameters. METHODS: Sixty-three patients with acute intracranial hemorrhage and unenhanced CT of the head were retrospectively included. In these patients, 35 intraparenchymal, 39 intraventricular, 30 subarachnoidal, and 43 subdural hemorrhages were selected. VMIs were reconstructed using all available monoenergetic reconstruction levels (40-190 keV). Multiple regions of interest measurements were used for evaluation of the overall image quality, and signal, noise, signal-to-noise-ratio (SNR), and contrast-to-noise-ratio (CNR) of intracranial hemorrhage. Based on the results of the quantitative analysis, specific VMIs were rated by five radiologists on a 5-point Likert scale. RESULTS: Signal, noise, SNR, and CNR differed significantly between different VMIs (p < 0.001). Maximum CNR for intracranial hemorrhage was reached in VMI with keV levels > 120 keV (intraparenchymal 143 keV, intraventricular 164 keV, subarachnoidal 124 keV, and subdural hemorrhage 133 keV). In reading, no relevant superiority in the detection of hemorrhage could be demonstrated using VMIs above 66 keV. CONCLUSION: For the detection of hemorrhage in unenhanced CT of the head, the quantitative analysis of the present study on photon counting CT is generally consistent with the findings from dual-energy CT, suggesting keV levels just above 120 keV and higher depending on the location of the hemorrhage. However, on the basis of the qualitative analyses, no reliable statement can yet be made as to whether an additional VMI with higher keV is truly beneficial in everyday clinical practice.

Dual-energy computed tomography for evaluating nodal staging in lung adenocarcinoma: correlation with surgical pathology.

PURPOSE: To ascertain the performance of dual-energy CT (DECT) with iodine quantification in differentiating malignant mediastinal and hilar lymph nodes (LNs) from benign ones, focusing on patients with lung adenocarcinoma. MATERIALS AND METHODS: In this study, patients with suspected lung cancer received a preoperative contrast-enhanced DECT scan from Jun 2018 to Dec 2020. Quantitative DECT parameters and the size were compared between metastatic and benign LNs. Their diagnostic performances were analyzed by the ROC curves and compared by using the two-sample t test. RESULTS: 72 patients (23 men, 49 women; mean age 62.5 ± 10.1 years) fulfilled the inclusion criteria. A total of 98 LNs (67 benign, 31 metastatic) were analyzed. The iodine concentration normalized by muscle (NICmuscle) was significantly higher (P < 0.001) in metastatic LNs (4.79 ± 1.70) than in benign ones (3.00 ± 1.45). The optimal threshold of NICmuscle was 3.44, which yielded AUC: 0.798, sensitivity: 83.9%, specificity: 73.1%, accuracy: 76.5%, respectively. Applying the established size parameters with 10 mm as the threshold yielded AUC: 0.600, sensitivity: 29.0%, specificity: 91.0%, accuracy: 71.4%, respectively. The diagnostic performance of NICmuscle was significantly better (P = 0.007) than the performance obtained using the established size parameters. CONCLUSIONS: For lung adenocarcinoma, the quantitative measurement of NICmuscle derived from DECT is useful for differentiating benign and metastatic mediastinal and hilar LNs before surgical intervention.

Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.

OBJECTIVES: To evaluate the performance of deep learning image reconstruction (DLIR) algorithm in dual-energy spectral CT (DEsCT) as a function of radiation dose and image energy level, in comparison with filtered-back-projection (FBP) and adaptive statistical iterative reconstruction-V (ASIR-V) algorithms. METHODS: An ACR464 phantom was scanned with DEsCT at four dose levels (3.5 mGy, 5 mGy, 7.5 mGy, and 10 mGy). Virtual monochromatic images were reconstructed at five energy levels (40 keV, 50 keV, 68 keV, 74 keV, and 140 keV) using FBP, 50% and 100% ASIR-V, DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) settings. The noise power spectrum (NPS), task-based transfer function (TTF) and detectability index (d') were computed and compared among reconstructions. RESULTS: NPS area and noise increased as keV decreased, with DLIR having slower increase than FBP and ASIR-V, and DLIR-H having the lowest values. DLIR had the best 40 keV/140 keV noise ratio at various energy levels, DLIR showed higher TTF (50%) than ASIR-V for all materials, especially for the soft tissue-like polystyrene insert, and DLIR-M and DLIR-H provided higher d' than DLIR-L, ASIR-V and FBP in all dose and energy levels. As keV increases, d' increased for acrylic insert, and d' of the 50 keV DLIR-M and DLIR-H images at 3.5 mGy (7.39 and 8.79, respectively) were higher than that (7.20) of the 50 keV ASIR-V50% images at 10 mGy. CONCLUSIONS: DLIR provides better noise containment for low keV images in DEsCT and higher TTF(50%) for the polystyrene insert over ASIR-V. DLIR-H has the lowest image noise and highest detectability in all dose and energy levels. DEsCT 50 keV images with DLIR-M and DLIR-H show potential for 65% dose reduction over ASIR-V50% withhigher d'.

Negative gallbladder stones: Diagnostic advantages of dual-energy CT and analysis of a unique case.

BACKGROUND: Gallbladder stones are a common digestive system disease, but their diagnosis can be limited in some cases, especially in identifying "negative" stones, which may be difficult to recognize with traditional CT scans. OBJECTIVE: This study aims to explore the advantages of dual-energy CT in diagnosing negative gallbladder stones through a unique case of gallbladder stones. METHODS AND RESULTS: A case of a 31-year-old female is described, who was diagnosed with gallbladder stones during a physical examination two years ago and occasionally experienced pain in the upper right abdomen. Dual-energy CT scanning revealed a mixed-density stone, approximately 2 cm in diameter, in the neck of the gallbladder, consisting of a calcified shell (positive stone) and a homogenous density nucleus (negative stone). Verified by dual-energy CT, single-energy images and spectral curves can very intuitively identify negative stones, demonstrating significantly superior performance compared to traditional CT. CONCLUSION: Dual-energy CT, through single-energy images and spectral curves, intuitively identifies negative gallbladder stones, showcasing significant advantages compared to traditional CT, and offers a valuable approach to enhancing the diagnostic accuracy of gallbladder stones.

Enhanced visualization in endoleak detection through iterative and AI-noise optimized spectral reconstructions.

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity - 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

Pancreatic cancer in photon-counting CT: Low keV virtual monoenergetic images improve tumor conspicuity.

PURPOSE OF THE STUDY: The aim of the study was to identify differences in the tumor conspicuity of pancreatic adenocarcinomas in different monoenergetic or polyenergetic reconstructions and contrast phases in photon-counting CT (PCCT). MATERIAL AND METHODS: 34 patients were retrospectively enrolled in this study. Quantitative image analysis was performed with region of interest (ROI) measurements in different monoenergetic levels ranging from 40 up to 70 keV (5-point steps) and polyenergetic series. Tumor-parenchyma attenuation differences and contrast-to-noise-ratio (CNR) were calculated. A qualitative image analysis was accomplished by 4 radiologists using a 5-point Likert scale (1 = "not recognizable" up to 5 = "easy recognizable"). Differences between groups were evaluated for statistical significance using the Friedman test and in case of significant differences pair-wise post-hoc testing with Bonferroni correction was applied. RESULTS: Tumor-parenchyma attenuation difference was significantly different between the different image reconstructions for both arterial- and portal-venous-phase-images (p < 0.001). Tumor-parenchyma attenuation difference was significantly higher on arterial-phase-images at mono40keV compared to polyenergetic images (p < 0.001) and mono55keV images or higher (p < 0.001). For portal-venous-phase-images tumor-parenchyma attenuation difference was significantly higher on mono40keV images compared to polyenergetic images (p < 0.001) and mono50keV images (p = 0.03) or higher (p < 0.001). The same trend was seen for CNR. Tumor conspicuity was rated best on mono40keV images with 4.3 ± 0.9 for arterial-phase-images and 4.3 ± 1.1 for portal-venous-phase-images. In contrast, overall image quality was rated best on polyenergetic-images with 4.8 ± 0.5 for arterial-phase-images and 4.7 ± 0.6 for portal-venous-phase-images. CONCLUSION: Low keV virtual monoenergetic images significantly improve the tumor conspicuity of pancreatic adenocarcinomas in PCCT based on quantitative and qualitative results. On the other hand, readers prefer polyenergetic images for overall image quality.

Dual-energy CT applications in musculoskeletal disorders.

Dual-energy CT (DECT) is an exciting application in CT technology conferring many advantages over conventional single-energy CT at no additional with comparable radiation dose to the patient. Various emerging and increasingly established clinical DECT applications in musculoskeletal (MSK) imaging such as bone marrow oedema detection, metal artefact reduction, monosodium urate analysis, and collagen analysis for ligamentous, meniscal, and disc injuries are made possible through its advanced DECT post-processing capabilities. These provide superior information on tissue composition, artefact reduction and image optimization. Newer DECT applications to evaluate fat fraction for sarcopenia, Rho/Z application for soft tissue calcification differentiation, 3D rendering, and AI integration are being assessed for future use. In this article, we will discuss the established and developing applications of DECT in the setting of MSK radiology as well as the basic principles of DECT which facilitate them.

The value of dual-layer spectral detector CT in preoperative T staging of laryngeal and hypopharyngeal squamous cell carcinoma.

PURPOSE: To explore the optimal kiloelectron voltage (keV) of virtual monochromatic images (VMIs) of dual-layer spectral detector computed tomography (DLSCT) to display laryngeal and hypopharyngeal squamous cell carcinoma (LHSCC) and its diagnostic performance for preoperative T staging of LHSCC. METHODS: A total of 67 LHSCC patients were included, and the contrast between the tumor and sternocleidomastoid muscle (SM), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image noise of 40-100 keV VMIs and conventional polyenergetic images (CIs) were evaluated. The image quality of the CI and 40-100 keV VMI was evaluated by a five-point method. The VMI with the best image quality was screened out, and the accuracy of the optimal keV VMI and CI for T staging was assessed using clinical T staging as the reference standard. RESULTS: The contrast between the tumor and SM, SNR, CNR and subjective image quality scores of LHSCC on 40-50 keV VMIs were higher than those on CIs (P < 0.05); the image noises of 40-100 keV VMIs were lower than those of CIs (P < 0.05). The 40 keV VMI had the highest SNR, CNR and subjective score of image quality. The accuracy rates of the 40 keV VMI and CI for T staging of LHSCC were 0.86 and 0.63 (P < 0.001), respectively. CONCLUSION: The image quality of 40-50 keV VMI is higher than that of CI, and the diagnostic accuracy of 40 keV VMI is better than that of CI, which is most suitable for preoperative T staging of LHSCC.

Saving Contrast Media in Coronary CT Angiography with Photon-Counting Detector CT.

RATIONALE AND OBJECTIVES: To determine the optimal virtual monoenergetic image (VMI) energy level and the potential of contrast-media (CM) reduction for coronary computed tomography angiography (CCTA) with photon-counting detector CT (PCD-CT). MATERIALS AND METHODS: In this institutional review board-approved study, patients who underwent CCTA with dual-source PCD-CT with an identical scan protocol and radiation dose were included. In group 1, CCTA was performed with our standard CM protocol (volume: 72-85.2 mL, 370 mg iodine/mL). VMIs were reconstructed from 40 to 60 keV at 5 keV increments. Objective image quality (IQ) (vascular attenuation, image noise, and contrast-to-noise ratio [CNR]) was measured. Two blinded, independent readers rated subjective IQ (overall IQ, subjective image contrast, and subjective noise using a five-point discrete visual scale). Results of group 1 served to determine the best VMI level for CCTA. In group 2, CM volume was reduced by 20%, and in group 3 by another 20%. RESULTS: A total of 100 patients were enrolled (45 females, mean age 54 ± 13 years). Inter-reader agreement was good-to-excellent for all comparisons (κ > 0.6). In group 1, the best VMI level regarding objective and subjective IQ was 45 keV, which was selected as the reference for groups 2 and 3. For group 2, mean vascular attenuation was 890 Hounsfield units (HU) and mean CNR was 26, with no differences compared to group 1, 45 keV for both objective and subjective IQ. For group 3, mean vascular attenuation was 676 HU and mean CNR was 21, and all patients were rated as diagnostic except one (severe motion artifacts). CONCLUSION: Increased IQ of PCD-CT can be used for considerable CM volume reduction while still maintaining a diagnostic IQ of CCTA.