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The purpose of this study was to determine if dual‐energy CT (DECT) vital iodine tumor burden (ViTB), a direct assessment of tumor vascularity, allows reliable response assessment in patients with GIST compared to established CT criteria such as RECIST1.1 and modified Choi (mChoi). From 03/2014 to 12/2019, 138 patients (64 years [32–94 years]) with biopsy proven GIST were entered in this prospective, multi‐center trial. All patients were treated with tyrosine kinase inhibitors (TKI) and underwent pre‐treatment and follow‐up DECT examinations for a minimum of 24 months. Response assessment was performed according to RECIST1.1, mChoi, vascular tumor burden (VTB) and DECT ViTB. A change in therapy management could be because of imaging (RECIST1.1 or mChoi) and/or clinical progression. The DECT ViTB criteria had the highest discrimination ability for progression‐free survival (PFS) of all criteria in both first line and second line and thereafter treatment, and was significantly superior to RECIST1.1 and mChoi (p <.034). Both, the mChoi and DECT ViTB criteria demonstrated a significantly early median time‐to‐progression (both delta 2.5 months; both p <.036). Multivariable analysis revealed 6 variables associated with shorter overall survival: secondary mutation (HR = 4.62), polymetastatic disease (HR = 3.02), metastatic second line and thereafter treatment (HR = 2.33), shorter PFS determined by the DECT ViTB criteria (HR = 1.72), multiple organ metastases (HR = 1.51) and lower age (HR = 1.04). DECT ViTB is a reliable response criteria and provides additional value for assessing TKI treatment in GIST patients. A significant superior response discrimination ability for median PFS was observed, including non‐responders at first follow‐up and patients developing resistance while on therapy. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This study aimed to determine the diagnostic efficacy of various indicators and models for the prediction of gastric cancer with liver metastasis. Methods: Clinical and spectral computed tomography (CT) data from 80 patients with gastric adenocarcinoma who underwent surgical resection were retrospectively analyzed. Patients were divided into metastatic and non-metastatic groups based on whether or not to occur liver metastasis, and the region of interest (ROI) was measured manually on each phase iodine map at the largest level of the tumor. Iodine concentration (IC), normalized iodine concentration (nIC), and clinical data of the primary gastric lesions were analyzed. Logistic regression analysis was used to construct the clinical indicator (CI) and clinical indicator-spectral CT iodine concentration (CI-Spectral CT-IC) Models, which contained all of the parameters with statistically significant differences between the groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the accuracy of the models. Results: The metastatic group showed significantly higher levels of Cancer antigen125 (CA125), carcinoembryonic antigen (CEA), IC, and nIC in the arterial phase, venous phase, and delayed phase than the non-metastatic group (all p < 0.05). Normalized iodine concentration Venous Phase (nICVP) exhibited a favorable performance among all IC and nIC parameters for forecasting gastric cancer with liver metastasis (area under the curve (AUC), 0.846). The combination model of clinical data with significant differences and nICVP showed the best diagnostic accuracy for predicting liver metastasis from gastric cancer, with an AUC of 0.897. Conclusion: nICVP showed the best diagnostic efficacy for predicting gastric cancer with liver metastasis. Clinical Indicators-normalized ICVP model can improve the prediction accuracy for this condition. [ABSTRACT FROM AUTHOR]

Purpose: To explore whether dual-energy CT (DECT) quantitative parameters could provide analytic value for the diagnosis of patients with occult peritoneal metastasis (OPM) in advanced gastric cancer preoperatively. Materials and methods: This retrospective study included 219 patients with advanced gastric cancer and DECT scans. The patient's clinical data and DECT related iodine concentration (IC) parameters and effective atomic number (Zeff) were collated and analyzed among noun-peritoneal metastasis (NPM), OPM and radiologically peritoneal metastasis (RPM) groups. The predictive performance of the DECT parameters was compared with that of the conventional CT features and clinical characteristics through evaluating area under curve of the precision-recall (AUC-PR), F1 score, balanced accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Results: Borrmann IV type diagnosed on CT and serum tumor indicator CA125 index were statistically different between the NPM and OPM groups. DECT parameters included IC, normalized IC (NIC), and Zeff of PM group were lower than the NPM group. The DECT predictive nomogram combined three independent DECT parameters produced a better diagnostic performance than the conventional CT feature Borrmann IV type and serum CA125 index in AUC-PR with 0.884 vs 0.368 vs 0.189, but similar to the combined indicator which was based on the DECT parameters, the conventional CT feature, and serum CA125 index in AUC-PR with 0.884 vs 0.918. Conclusion: The lower quantitative NIC, IC ratio, and Zeff on DECT was associated with peritoneal metastasis in advanced gastric cancer and was promising to identify patients with OPM noninvasively. [ABSTRACT FROM AUTHOR]

To develop a clinical-radiomics nomogram based on spectral CT multi-parameter images for predicting lymph node metastasis in colorectal cancer. A total of 76 patients with colorectal cancer and 156 lymph nodes were included. The clinical data of the patients were collected, including gender, age, tumor location and size, preoperative tumor markers, etc. Three sets of conventional images in the arterial, venous, and delayed phases were obtained, and six sets of spectral images were reconstructed using the arterial phase spectral data, including virtual monoenergetic images (40 keV, 70 keV, 100 keV), iodine density maps, iodine no water maps, and virtual non-contrast images. Radiomics features of lymph nodes were extracted from the above images, respectively. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select features. A clinical model was constructed based on age and carcinoembryonic antigen (CEA) levels. The radiomics features selected were used to generate a composed radiomics signature (Com-RS). A nomogram was developed using age, CEA, and the Com-RS. The models' prediction efficiency, calibration, and clinical application value were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis, respectively. The nomogram outperforms the clinical model and the Com-RS (AUC = 0.879, 0.824). It is well calibrated and has great clinical application value. This study developed a clinical-radiomics nomogram based on spectral CT multi-parameter images, which can be used as an effective tool for preoperative personalized prediction of lymph node metastasis in colorectal cancer. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to explore the value of monoenergetic imaging using dual-layer spectral detector computed tomography (CT) for dacryocystography. Methods: Dacryocystography was performed in patients with clinically suspected lacrimal duct obstruction using a Philips IQon dual-layer spectral detector CT. The objective evaluation included the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of each monoenergetic image, subjective rating of image quality, and the site and degree of lacrimal duct obstruction. Results: The SNR and CNR of 50 keV images was 9.76±13.44 and 10.24±13.88, respectively, which were significantly lower than that of 70~200 keV images. The subjective scores for 50 keV and conventional image quality were (5, 5) and (4, 5), respectively, indicating good consistency in subjective scores among radiologists. Conclusion: The SNR and CNR of CT dacryocystography increased significantly at 50 keV.

Objective: This study aimed to explore the feasibility of applying dual-layer detector spectral computed tomography (DLCT) virtual non-contrast (VNC) technology in pediatric enhanced chest CT examinations. Methods: Retrospective analysis was conducted on the enhanced chest CT imaging data and clinical information of 51 children who underwent DLCT scanning at Wuhan Union Hospital. The same processing protocol was applied to each original image. VNC images were obtained using projection space spectral reconstruction from the venous phase spectral base images (SBI) and compared with true non-contrast (TNC) images regarding image quality and radiation dose. Two radiologists measured the CT and SD values of the pulmonary artery trunk, aorta trunk, erector spinae, lung tissue, and subcutaneous fat of the chest wall on image sets, calculating the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Non-contrast, arterial, and venous phase radiation doses were recorded. Subjective image quality scores were assigned using a 5-point Likert scale, and inter-observer consistency was assessed with the Kappa test. The Mann–Whitney U test compared subjective scores, while the paired t-test was used for objective image quality and radiation dose analyses. Results: Objective evaluation of image quality showed no significant statistical differences between the SNR, CNR, and subjective scores of both groups. The median subjective image scores for TNC and VNC were 4 points. The DLP and ED values of the VNC group (298.14±119.40) mGy·cm, (4.27±1.34) mSv were significantly lower than those of the TNC group (437.31±178.28) mGy·cm, (6.26±2.00) mSv. The DLP and ED of the VNC group reduced by 31.82％ and 31.79％, respectively, compared to the TNC group. Conclusion: Spectral CT virtual non-contrast (VNC) images as a substitute for conventional non-contrast (TNC) images in pediatric enhanced chest scans offers satisfactory diagnostic image quality and can significantly reduce radiation dose.

Computed tomography (CT) is one of the most commonly used examination methods in clinical practice, but its resolution for soft-tissue is not high enough, making it difficult to detect and identify some lesions, and it is insufficient in quantitative assessment. The advantages of spectral CT imaging are multi-parameter, multi-dimensional and quantitative imaging, which reflects the compositional characteristics of tissues and the characteristics of blood supply. It can simulate images of a single KeV energy, through two sets of high-energy and low-energy data, namely virtual monoenergetic images (VMI). In this review, the current problems in the use of VMI and iodine maps in lesion detection and differentiating diagnosis of abdominal diseases were concluded. The progress in detection of liver and pancreas lesion were pointed out. For example, the confidence level was raised from 52.4 to 68.8 in detection of small hepatocellular carcinoma by using VMI. In the pancreas, compared with normal CT, detection rate of VMI for endocrine tumor in pancreas was raised from 84.6% to 92.3%. What’s more, in terms of differential diagnosis, there have been many advances in the research on VMI and iodine density maps, and higher efficiency in differentiating diagnosis has been obtained in the classification of kidney cyst, differentiation between small hepatocellular carcinoma and small hepatic hemangioma, and differentiation of serous and mucinous cystadenoma in pancreas. Based on current progress, the future of VMI and iodine maps in abdominal diseases is prospected.

Objective. Multi-energy CT conducted by photon-counting detector has a wide range of applications, especially in multiple contrast agents imaging. However, static multi-energy (SME) CT imaging suffers from higher statistical noise because of increased energy bins with static energy thresholds. Our team has proposed a dynamic dual-energy (DDE) CT detector model and the corresponding iterative reconstruction algorithm to solve this problem. However, rigorous and detailed analysis of the statistical noise characterization in this DDE CT was lacked. Approach. Starting from the properties of the Poisson random variable, this paper analyzes the noise characterization of the DDE CT and compares it with the SME CT. It is proved that the multi-energy CT projections and reconstruction images calculated from the proposed DDE CT algorithm have less statistical noise than that of the SME CT. Main results. Simulations and experiments verify that the expectations of the multi-energy CT projections calculated from DDE CT are the same as those of the SME projections. Still, the variance of the former is smaller. We further analyze the convergence of the iterative DDE CT algorithm through simulations and prove that the derived noise characterization can be realized under different CT imaging configurations. Significance. The low statistical noise characteristics demonstrate the value of DDE CT imaging technology. [ABSTRACT FROM AUTHOR]

Background/Objectives: Fragility fractures of the pelvis (FFP) are characterized by inadequate trauma to a structurally compromised bone, primarily in osteoporosis. Conventional CT studies can be inadequate in identifying FFPs. An MRI of the pelvis is considered the gold standard in diagnosing FFPs. Spectral CT or Dual-Energy CT may have comparable diagnostic accuracy. It provides additional insights into associated bone marrow edema. The aim of this prospective monocentric study is to evaluate the diagnostic accuracy of Spectral CT compared to the gold standard MRI in diagnosing FFP. Methods: Over a 2-year period, patients presenting in the emergency department with clinical suspicion of an FFP were consecutively included. They underwent Spectral CT (GE Revolution 16 cm GSI) upon admission, followed by an MRI. The gold standard for diagnosing FFP is pelvic MRI, showing sensitivity and specificity ranging from 97% to 100%. The acquired images were evaluated and classified using the osteoporotic fractures of the pelvis (OFP) classification. Results: Compared to the reference test, which was the MRI pelvis, the sensitivity of the CT pelvis was determined to be 86.8 (95% confidence interval (CI) 71.9–95.6%) with a specificity of 84.6% (95% CI: 54.6–98.1%, p = 0.453). Spectral CT could identify an additional FFP correctly, exhibiting a sensitivity of 89.5% (95% CI: 75.2–97.1%, p = 0.688), while maintaining the same specificity as the conventional CT. The inter-rater reliability assessment for Spectral CT, conducted by four independent raters, resulted in a Fleiss' Kappa value of 0.516 (95% CI: 0.450–0.582, p < 0.001). Conclusion: The sensitivity of Spectral CT in the detection of pelvic ring fragility fractures shows a slightly lower sensitivity compared to MRI. There were no statistically significant differences observed when compared to conventional CT or MRI. In conclusion, Spectral CT may be beneficial in distinguishing FFP, particularly in cases where a definitive diagnosis is uncertain. Level of Evidence: II. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer. Methods: Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40–70 keV), spectral curve slope (K(HU)), effective atomic number (Zeff), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test. Results: The preoperative spectral CT parameters (40–70 keV attenuation, K(HU), Zeff, and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%. Conclusion: Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency. [ABSTRACT FROM AUTHOR]

Objective The evaluation of lymph node properties before lung cancer surgery has a great impact with the choice of surgical methods. Although there are various examination methods, many methods have invasive or accuracy problems. In order to improve the accuracy of diagnosis, we mainly discuss the value of wide- body spectral CT in the differential diagnosis of mediastinal metastatic lymph nodes, non-metastatic lymph nodes in lung cancer patients and reactive hyperplastic lymph nodes. Methods The clinical and imaging data of 64 patients with lung cancer and 28 patients with pulmonary inflammatory lesions were retrospectively analyzed. All patients underwent plain scan and enhanced dual-phase spectral CT scan. The size, density, three-phase IC, NIC, and λHU of lymph nodes in metastatic, non-metastatic and inflammatory reactive hyperplasia groups were measured on 70 keV single-energy images and iodine-based images, respectively. The single-factor variance and Kruskal-Wallis H rank sum test were used to analyze and compare the differences. Results The short diameter of metastatic lymph nodes was larger than that of non-metastatic lymph nodes and reactive hyperplastic lymph nodes (P < 0.001) . The plain scan density of reactive hyperplastic lymph nodes was higher than that of metastatic lymph nodes (P < 0.001), but there was no significant difference between non-metastatic lymph nodes (P = 0.325) . The CT values of reactive hyperplastic lymph nodes in arterial phase and venous phase were higher than those of metastatic and non-metastatic lymph nodes (P < 0.05) . Except for NIC in arterial phase, IC, NIC and λHU in plain scan, IC and λHU in arterial phase, IC, NIC and λHU in venous phase of reactive hyperplastic lymph nodes and metastatic lymph nodes were statistically significant (all P < 0.05) . There was no significant difference in IC, NIC and λHU between reactive hyperplastic lymph nodes and non-metastatic lymph nodes in plain scan, arterial phase and venous phase (all P > 0.05) . Conclusion The quantitative and spectral curve slope of iodine in mediastinal metastatic lymph nodes of lung cancer were basically lower than those in reactive hyperplastic lymph nodes. The quantitative parameters of spectral CT had certain diagnostic efficacy in differentiating metastatic lymph nodes and reactive hyperplastic lymph nodes, while the spectral parameters of non-metastatic lymph nodes and reactive hyperplastic lymph nodes were not statistically significant. [ABSTRACT FROM AUTHOR]

With the development of photon counting detectors (PCDs), spectral CT achieves higher performance than in the past, thus has better application prospects. In this paper, an adaptive dual effect decomposition (ADED) algorithm is proposed to perform the quantitative estimation of effective atomic number and electron density. The algorithm is an empirical material identification algorithm that achieves calibration by combining polynomials on the projection data and constraining in the image domain. In addition, we also propose an innovative effective atomic number estimation model with an accurate physical model that adaptively adjusts the parameters according to the x‐ray energy and material type. The performance of the algorithm was verified by experiment and simulation in this paper. Compared to the conventional basis material decomposition algorithm, the proposed ADED algorithm reduces the relative error in the quantification of the effective atomic number from 4.5%–12.0% to 0.3%–4.5%. Overall, the results demonstrate that the proposed algorithm significantly improves the accuracy of quantifying the effective atomic number. [ABSTRACT FROM AUTHOR]

Objective：To establish the applicability of a predictive model based on spectral computed tomography（CT）parameters and radiomics features through machine learning for differentiating benign and malignant thyroid nodules. Methods：A retrospective analysis was conducted on imaging and clinical data from 118 patients with thyroid nodules who underwent spectral CT enhancement scanning（143 nodules, comprise of 46 benign and 97 malignant nodules). These patients were randomly divided into a training set （n=100）and a validation set（n=43）in a 7∶3 ratio. Discriminative testing, intraclass correlation coefficient（ICC), and the least absolute shrinkage and selection operator（LASSO）were employed to select features and calculate a radiomics score（Radscore). Six machine learning algorithms including decision tree（DT), random forest（RF), extreme gradient boosting（XGBoost), support vector machine（SVM), K ⁃ nearest neighbors（KNN), and logistic regression（LR）were utilized to develop models. The optimal model was selected to construct nomograms. Results：The XGBoost model was demonstrated to be the best in the validation set（AUC：0.938； accuracy：86.05%；sensitivity：89.29%；specificity：80.00%), with normalized iodine concentration（NIC), Radscore, and age identified as significant predictive factors. The ensuing nomograms exhibited robust performance. Conclusion：The machine learning model that combines spectral CT and radiomics features with the nomograms provides a highly accurate reference for non ⁃invasive prediction of the benignity or malignancy of thyroid nodules. [ABSTRACT FROM AUTHOR]

Abstract Background Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. Purpose To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. Methods Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. Results The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson’s and Spearman’s correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p

Objective: To investigate the value of radiomics features based on spectral CT iodine map for diagnosing metastatic cervical lymph nodes in patients with papillary thyroid carcinoma. Methods: Seventy-eight patients with papillary thyroid carcinoma who underwent cervical energy spectrum CT within two weeks before surgery were retrospectively analyzed. We included 117 metastatic, 176 non-metastatic lymph nodes, which were then randomly divided into a training set and a validation set in a 3:1 ratio. Radiomics features were extracted and screened from venous phase iodine maps. Logistic regression model was used to construct diagnostic models based on CT image features, radiomics signature, and a combination of the two, respectively; a nomogram was then drawn to visualize the combined model. The diagnostic performance, calibration ability and clinical practicability of each model were evaluated by ROC curve, calibration curve and decision curve analysis, respectively. Results: The combined model showed optimal diagnostic performance in both the training and validation sets, followed by radiomics model. These two models outperformed the CT image features model in both the training and validation sets. All models showed good calibration, and decision curve analysis demonstrated the superiority of the nomogram over the other two models in terms of clinical usefulness. Conclusion: The radiomics signature of spectral CT showed good performance in diagnosing lymph node metastasis of papillary thyroid carcinoma. The diagnostic performance was further improved when combined with CT image features, which can be a useful tool to assist in clinical decision-making.

Objective To assess the value of dual-layer detector spectral CT (DLCT) multi-parameter imaging in the differential diagnosis of gastrointestinal heterotopic pancreas (HP) and gastrointestinal stromal tumor (GIST). Methods The clinical and DLCT imaging data of 47 patients (21 men and 26 women) with pathologically confirmed gastrointestinal HP and GIST were analyzed retrospectively. The iodine concentration (IC) and effective atomic number (Zeff) of gastrointestinal HP and GIST were measured in the arterial and venous phases. The predictive factors were screened out by univariate and binary logistic regression analysis of all spectral CT parameters, and the diagnostic efficiency was analyzed by receiver operating characteristic (ROC) curve. Results There were 22 gastrointestinal HP patients with median age of 43 years (range: 38.7-50.5 years) and 25 GIST patients with median age of 57 years. Univariate analysis showed that the values of IC and Zeff in arteriovenous phase were statistically different (P<0.05). Binary logistic regression analysis showed that venous phase IC value (P=0.008) and arterial phase Zeff value (P= 0.013) were independent predictors of HP. The area under the ROC curve (AUC) of 2.065 mg/mL venous phase IC threshold value was 0.792 with 72.7% sensitivity and 88.0% specificity compared to AUC of 0.786 of 8.295 arterial phase Zeff threshold value with 68.2% sensitivity and 92.0% specificity for distinguishing HP from GIST. Conclusion The venous phase IC value and Zeff value of DLCT can effectively distinguish HP from GIST. [ABSTRACT FROM AUTHOR]

Background: Abdominal CT scans are vital for diagnosing abdominal diseases but have limitations in tissue analysis and soft tissue detection. Dual-energy CT (DECT) can improve these issues by offering low keV virtual monoenergetic images (VMI), enhancing lesion detection and tissue characterization. However, its cost limits widespread use. Purpose: To develop a model that converts conventional images (CI) into generative virtual monoenergetic images at 40 keV (Gen-VMI40keV) of the upper abdomen CT scan. Methods: Totally 444 patients who underwent upper abdominal spectral contrast-enhanced CT were enrolled and assigned to the training and validation datasets (7:3). Then, 40-keV portal-vein virtual monoenergetic (VMI40keV) and CI, generated from spectral CT scans, served as target and source images. These images were employed to build and train a CI-VMI40keV model. Indexes such as Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity (SSIM) were utilized to determine the best generator mode. An additional 198 cases were divided into three test groups, including Group 1 (58 cases with visible abnormalities), Group 2 (40 cases with hepatocellular carcinoma [HCC]) and Group 3 (100 cases from a publicly available HCC dataset). Both subjective and objective evaluations were performed. Comparisons, correlation analyses and Bland-Altman plot analyses were performed. Results: The 192nd iteration produced the best generator mode (lower MAE and highest PSNR and SSIM). In the Test groups (1 and 2), both VMI40keV and Gen-VMI40keV significantly improved CT values, as well as SNR and CNR, for all organs compared to CI. Significant positive correlations for objective indexes were found between Gen-VMI40keV and VMI40keV in various organs and lesions. Bland-Altman analysis showed that the differences between both imaging types mostly fell within the 95% confidence interval. Pearson's and Spearman's correlation coefficients for objective scores between Gen-VMI40keV and VMI40keV in Groups 1 and 2 ranged from 0.645 to 0.980. In Group 3, Gen-VMI40keV yielded significantly higher CT values for HCC (220.5HU vs. 109.1HU) and liver (220.0HU vs. 112.8HU) compared to CI (p < 0.01). The CNR for HCC/liver was also significantly higher in Gen-VMI40keV (2.0 vs. 1.2) than in CI (p < 0.01). Additionally, Gen-VMI40keV was subjectively evaluated to have a higher image quality compared to CI. Conclusion: CI-VMI40keV model can generate Gen-VMI40keV from conventional CT scan, closely resembling VMI40keV. [ABSTRACT FROM AUTHOR]

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

The extension of conventional computed tomography known as spectral computed tomography involves utilizing the variations in X-ray attenuation, driven by spectral and material dependencies. This technique enables the virtual decomposition of scanned objects, revealing their elemental constituents. The resultant images provide quantitative information, such as material concentration within the scanned volume. Enhancements in results are achievable through methods that capitalize on the strong correlation among decomposed images, effectively minimizing noise and artifacts. The Rigaku nano3DX submicron tomograph uses a dual-target source, which allows the generation of two distinct X-ray spectra through different target materials. This configuration holds promise for high-resolution applications in spectral tomography, particularly for low-Z materials, where it offers high contrast in the acquired images. The potential of this setup in the context of spectral computed tomography is explored in this contribution, delving into its applications for materials characterized by low atomic numbers. [ABSTRACT FROM AUTHOR]

Simple Summary: The incidence of renal cell carcinoma (RCC) is increasing due to the expansion of cross-sectional imaging and advanced imaging techniques. They allow for the detection of tumors at an earlier stage, but there are often overlapping similarities in the appearance of benign and malignant renal tumors. This review presents and discusses the ever-evolving landscape of imaging techniques that can be used to detect and diagnose renal cell carcinoma, including its major histologic subtypes. It also provides insight into recently proposed or updated imaging algorithms and guidelines for the diagnosis of RCC. The review considers the major advances in spectral CT, photo- counting CT, multiparametric MRI, contrast-enhanced ultrasound, sestamibi SPECT/CT, PSMA PET/CT, radiomics, artificial intelligence, Bosniak classification version 2019, clear cell likelihood score, and AUA guidelines. The goal for radiologists is to be better equipped to guide the diagnosis and management of these patients. This review highlights recent advances in renal cell carcinoma (RCC) imaging. It begins with dual-energy computed tomography (DECT), which has demonstrated a high diagnostic accuracy in the evaluation of renal masses. Several studies have suggested the potential benefits of iodine quantification, particularly for distinguishing low-attenuation, true enhancing solid masses from hyperdense cysts. By determining whether or not a renal mass is present, DECT could avoid the need for additional imaging studies, thereby reducing healthcare costs. DECT can also provide virtual unenhanced images, helping to reduce radiation exposure. The review then provides an update focusing on the advantages of multiparametric magnetic resonance (MR) imaging performance in the histological subtyping of RCC and in the differentiation of benign from malignant renal masses. A proposed standardized stepwise reading of images helps to identify clear cell RCC and papillary RCC with a high accuracy. Contrast-enhanced ultrasound may represent a promising diagnostic tool for the characterization of solid and cystic renal masses. Several combined pharmaceutical imaging strategies using both sestamibi and PSMA offer new opportunities in the diagnosis and staging of RCC, but their role in risk stratification needs to be evaluated. Although radiomics and tumor texture analysis are hampered by poor reproducibility and need standardization, they show promise in identifying new biomarkers for predicting tumor histology, clinical outcomes, overall survival, and the response to therapy. They have a wide range of potential applications but are still in the research phase. Artificial intelligence (AI) has shown encouraging results in tumor classification, grade, and prognosis. It is expected to play an important role in assessing the treatment response and advancing personalized medicine. The review then focuses on recently updated algorithms and guidelines. The Bosniak classification version 2019 incorporates MRI, precisely defines previously vague imaging terms, and allows a greater proportion of masses to be placed in lower-risk classes. Recent studies have reported an improved specificity of the higher-risk categories and better inter-reader agreement. The clear cell likelihood score, which adds standardization to the characterization of solid renal masses on MRI, has been validated in recent studies with high interobserver agreement. Finally, the review discusses the key imaging implications of the 2017 AUA guidelines for renal masses and localized renal cancer. [ABSTRACT FROM AUTHOR]

Background: This study addresses the technical gap between clinical radiation therapy (RT) and preclinical small‐animal RT, hindering the comprehensive validation of innovative clinical RT approaches in small‐animal models of cancer and the translation of preclinical RT studies into clinical practices. Purpose: The main aim was to explore the feasibility of biologically guided RT implemented within a small‐animal radiation therapy (SART) platform, with integrated quad‐modal on‐board positron emission tomography (PET), single‐photon emission computed tomography, photon‐counting spectral CT, and cone‐beam CT (CBCT) imaging, in a Monte Carlo model as a proof‐of‐concept. Methods: We developed a SART workflow employing quad‐modal imaging guidance, integrating multimodal image‐guided RT and emission‐guided RT (EGRT). The EGRT algorithm was outlined using positron signals from a PET radiotracer, enabling near real‐time adjustments to radiation treatment beams for precise targeting in the presence of a 2‐mm setup error. Molecular image‐guided RT, incorporating a dose escalation/de‐escalation scheme, was demonstrated using a simulated phantom with a dose painting plan. The plan involved delivering a low dose to the CBCT‐delineated planning target volume (PTV) and a high dose boosted to the highly active biological target volume (hBTV) identified by the 18F‐PET image. Additionally, the Bayesian eigentissue decomposition method illustrated the quantitative decomposition of radiotherapy‐related parameters, specifically iodine uptake fraction and virtual noncontrast (VNC) electron density, using a simulated phantom with Kidney1 and Liver2 inserts mixed with an iodine contrast agent at electron fractions of 0.01–0.02. Results: EGRT simulations generated over 4,000 beamlet responses in dose slice deliveries and illustrated superior dose coverage and distribution with significantly lower doses delivered to normal tissues, even with a 2‐mm setup error introduced, demonstrating the robustness of the novel EGRT scheme compared to conventional image‐guided RT. In the dose‐painting plan, doubling the dose to the hBTV while maintaining a low dose for the PTV resulted in an organ‐at‐risk (OAR) dose comparable to the low‐dose treatment for the PTV alone. Furthermore, the decomposition of radiotherapy‐related parameters in Kidney1 and Liver2 inserts, including iodine uptake fractions and VNC electron densities, exhibited average relative errors of less than 1.0% and 2.5%, respectively. Conclusions: The results demonstrated the successful implementation of biologically guided RT within the proposed quad‐model image‐guided SART platform, with potential applications in preclinical RT and adaptive RT studies. [ABSTRACT FROM AUTHOR]

Background: CT reconstruction is of essential importance in medical imaging. In 2022, the American Association of Physicists in Medicine (AAPM) sponsored a Grand Challenge to investigate the challenging inverse problem of spectral CT reconstruction, with the aim of achieving the most accurate reconstruction results. The authors of this paper participated in the challenge and won as a runner‐up team. Purpose: This paper reports details of our PROSPECT algorithm (Prior‐based Restricted‐variable Optimization for SPEctral CT) and follow‐up studies regarding the algorithm's accuracy and enhancement of its convergence speed. Methods: We formulated the reconstruction task as an optimization problem. PROSPECT employed a one‐step backward iterative scheme to solve this optimization problem by allowing estimation of and correction for the difference between the actual polychromatic projection model and the monochromatic model used in the optimization problem. PROSPECT incorporated various forms of prior information derived by analyzing training data provided by the Grand Challenge to reduce the number of unknown variables. We investigated the impact of projection data precision on the resulting solution accuracy and improved convergence speed of the PROSPECT algorithm by incorporating a beam‐hardening correction (BHC) step in the iterative process. We also studied the algorithm's performance under noisy projection data. Results: Prior knowledge allowed a reduction of the number of unknown variables by 85.9%$85.9\%$. PROSPECT algorithm achieved the average root of mean square error (RMSE) of 3.3×10−6$3.3\,\times \,10^{-6}$ in the test data set provided by the Grand Challenge. Performing the reconstruction with the same algorithm but using double‐precision projection data reduced RMSE to 1.2×10−11$1.2\,\times \,10^{-11}$. Including the BHC step in the PROSPECT algorithm accelerated the iteration process with a 40% reduction in computation time. Conclusions: PROSPECT algorithm achieved a high degree of accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: In spite of the tremendous potential of game‐changing biological image‐ and/or biologically guided radiation therapy (RT) and adaptive radiation therapy for cancer treatment, existing limited strategies for integrating molecular imaging and/or biological information with RT have impeded the translation of preclinical research findings to clinical applications. Additionally, there is an urgent need for a highly integrated small‐animal radiation therapy (SART) platform that can seamlessly combine therapeutic and diagnostic capabilities to comprehensively enhance RT for cancer treatment. Purpose: We investigated a highly integrated quad‐modal on‐board imaging configuration combining positron emission tomography (PET), single‐photon emission computed tomography (SPECT), photon‐counting spectral CT, and cone‐beam computed tomography (CBCT) in a SART platform using a Monte Carlo model as a proof‐of‐concept. Methods: The quad‐modal on‐board imaging configuration of the SART platform was designed and evaluated by using the GATE Monte Carlo code. A partial‐ring on‐board PET imaging subsystem, utilizing advanced semiconductor thallium bromide detector technology, was designed to achieve high sensitivity and spatial resolution. On‐board SPECT, photon‐counting spectral‐CT, and CBCT imaging were performed using a single cadmium zinc telluride flat detector panel. The absolute peak sensitivity and scatter fraction of the PET subsystem were estimated by using simulated phantoms described in the NEMA NU‐4 standard. The spatial resolution of the PET image of the platform was evaluated by imaging a simulated micro‐Derenzo hot‐rod phantom. To evaluate the quantitative imaging capability of the system's spectral CT, the Bayesian eigentissue decomposition (ETD) method was utilized to quantitatively decompose the virtual noncontrast (VNC) electron densities and iodine contrast agent fractions in the Kidney1 inserts mixed with the iodine contrast agent within the simulated phantoms. The performance of the proposed quad‐model imaging in the platform was validated by imaging a simulated phantom with multiple imaging probes, including an iodine contrast agent and radioisotopes of 18F and 99mTc. Results: The PET subsystem demonstrated an absolute peak sensitivity of 18.5% at the scanner center, with an energy window of 175–560 KeV, and a scatter fraction of only 3.5% for the mouse phantom, with a default energy window of 480–540 KeV. The spatial resolution of PET on‐board imaging exceeded 1.2 mm. All imaging probes were identified clearly within the phantom. The PET and SPECT images agreed well with the actual spatial distributions of the tracers within the phantom. Average relative errors on electron density and iodine contrast agent fraction in the Kidney1 inserts were less than 3%. High‐quality PET images, SPECT images, spectral‐CT images (including iodine contrast agent fraction images and VNC electron density images), and CBCT images of the simulated phantom demonstrated the comprehensive multimodal imaging capability of the system. Conclusions: The results demonstrated the feasibility of the proposed quad‐modal imaging configuration in a SART platform. The design incorporates anatomical, molecular, and functional information about tumors, thereby facilitating successful translation of preclinical studies into clinical practices. [ABSTRACT FROM AUTHOR]

Background: The goal of this study was to create a prediction model for brain metastasis (BrMs) in patients with lung cancer using unenhanced spectral computed tomography (CT) and radiomics. Materials and Methods: This study comprised 162 patients with lung cancer who underwent spectral CT from 2019-2021. Patients were split into training and test sets and into BrMs and BrMs-free groups. Spectral and radiomics parameters were obtained from the spectral CT images before pathological confirmation. Prediction models in the training and test sets were created using logistic regression. The receiver operating characteristic curve was used to evaluate each quantitative parameter for predicting BrMs. The diagnostic effectiveness of several parameters was analyzed and compared using the area under the curve (AUC) calculation. The final model was obtained using the Delong test. Results: There were statistically significant differences in the iodine concentrations and the slope of the energy spectrum attenuation curve of the two groups <(p0.05). The AUC of the combined radiomics model was greater than that of the 70 keV and 120 keV sequence models. The joint parameters of radiomics and spectral CT constructed an integrated model. In the training set, test set, and overall set, the AUCs of the integrated model were 0.875, 0.879, and 0.724, respectively. In the training and overall sets, the prediction performance of the integrated model outperformed the spectral and radiomics models (p<0.05). Conclusions: This integrated model may predict the BrMs in lung cancer patients. [ABSTRACT FROM AUTHOR]

In spectral CT imaging, the coefficient image of the basis material obtained by the material decomposition technique can estimate the tissue composition, and its accuracy directly affects the disease diagnosis. Although the precision of material decomposition is increased by employing convolutional neural networks (CNN), extracting the non-local features from the CT image is restricted using the traditional CNN convolution operator. A graph model built by multi-scale non-local self-similar patterns is introduced into multi-material decomposition (MMD). We proposed a novel MMD method based on graph edge–conditioned convolution U-net (GECCU-net) to enhance material image quality. The GECCU-net focuses on developing a multi-scale encoder. At the network coding stage, three paths are applied to capture comprehensive image features. The local and non-local feature aggregation (LNFA) blocks are designed to integrate the local and non-local features from different paths. The graph edge–conditioned convolution based on non-Euclidean space excavates the non-local features. A hybrid loss function is defined to accommodate multi-scale input images and avoid over-smoothing of results. The proposed network is compared quantitatively with base CNN models on the simulated and real datasets. The material images generated by GECCU-net have less noise and artifacts while retaining more information on tissue. The Structural SIMilarity (SSIM) of the obtained abdomen and chest water maps reaches 0.9976 and 0.9990, respectively, and the RMSE reduces to 0.1218 and 0.4903 g/cm3. The proposed method can improve MMD performance and has potential applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Copyright of CT Theory & Applications is the property of Editorial Department of CT Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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. [ABSTRACT FROM AUTHOR]

ObjectiveDelayed enhancement cardiac computed tomography (CT) empowers the diagnosis of left atrial appendage thrombus while limited to scanning heterogeneity. We optimized the spectral CT scan and post-process protocols, incorporating delayed enhancement and spectral iodine analysis to discriminate left atrial appendage (LAA) thrombus with better morphological relationships between the left atrium, pulmonary vein, and esophagus.MethodsA total of 278 consecutive patients were retrieved from January 2019 to June 2023. All patients underwent transesophageal echocardiography (TEE) and spectral CT scan of the left atrial and pulmonary vein, with a complete period including the pulmonary venous phase and three delay phases. TEE diagnosis was used as the standard reference. For patients exhibiting LAA filling defects during the pulmonary venous phase, a delayed scan of 30 s (phase I) was performed. If the filling defects persisted, a further delayed scan of 1 min (phase II) was conducted. In cases where the filling defects persisted, an additional delayed scan of 2 min (phase III) was carried out. Iodine concentration in the filled defect area of LAA and the left atrium was measured in phase III. Moreover, 30 patients were randomly selected for water-swallowing and the other 30 for calm breathing. The image quality and esophageal dilation of the two groups were assessed by two experienced surgeons specializing in radiofrequency ablation.ResultsIn total, 14 patients were diagnosed with thrombi by TEE. The sensitivity, specificity, positive predictive values, negative predictive values, and AUC of phase III delayed combined with iodine quantification for thrombi diagnosis were all 100%. The water-swallowing group exhibited significantly greater esophageal filling and expansion than the calm-breathing group, contributing to a better morphology assessment with no significant difference in image quality.ConclusionCombined with iodine quantification, delayed enhancement of spectral CT imaging presents a promising diagnostic potency for LAA thrombus. Incorporating water swallowing into the CT scan process further enables anatomical visualization of the esophagus, left atrium, and pulmonary vein, thereby providing more objective and authentic imaging evidence to assess the esophageal morphology and positional relationships.

A 24-year-old male patient complained of mild knee pain after jogging. The subsequent knee MRI demonstrated bilateral lateral thickened tibiofemoral cartilages, evidenced by deformities of the bilateral subchondral bone beneath the lateral femoral condyle cartilage. The corresponding dislocations of almost all the left lateral meniscus and part of the right lateral meniscus to the center of the joint were detected. After excluding diagnoses of congenital ring-shaped meniscus, bucket handle tear of the C-shaped lateral meniscus, and central tear of the discoid meniscus, the displacement of all or part of the lateral meniscus into the intercondylar notch was considered a consequence of congenital thickening of the lateral superior and inferior cartilage. This case may report a new variant of knee joint pathology.

Follicular lymphoma (FL) is one of the frequent varieties of non-Hodgkin's lymphoma (NHL). Secondary skeletal muscle FL is an uncommon extra-nodal lymphoma that is more challenging to identify. Spectral CT imaging can aid in the diagnosis of it. A 52-year-old man who had a bulge in his left inguinal area for two months in 2021. Following a mass aspiration biopsy, cervical lymph node dissection and a bone marrow aspiration biopsy, the patient was diagnosed as follicular lymphoma grade I with bone marrow involvement. Later, the patient had progressive worsening of low back pain for 2 month and underwent a lumbar spectral enhanced-CT scan, which demonstrated the right psoas major muscle and the retroperitoneal swollen lymph nodes were discovered to be homologous lesions, indicating the lymphoma infiltration of the right psoas major muscle. Spectral enhanced-CT is a non-invasive, practical, and reliable alternative technique. By reconstructing and comparing the energy spectrum curves of different tissues to shed light on the nature of the mass, it can aid in the diagnosis of extra-nodal lymphoma, which is of great value in determining the extent of the lesion and guiding the clinical treatment plan.

Purpose: To investigate the quantitative assessment of carotid plaque by each parameter of dual-layer detector spectral CT and its diagnostic value in patients with acute cerebral infarction. Patients and Methods: Eighty-three patients with carotid atherosclerotic plaques who underwent spectral CT scanning were retrospectively included. Forty-two patients with acute ischaemic stroke (AIS) were included in the study group, and 41 patients without AIS were included in the control group. We compared the detection of carotid plaques in the two groups and the differences in the spectral quantitative parameters of the plaques in the two groups, and their diagnostic efficacy was obtained. Results: The detection rate of carotid plaques in the AIS group was higher than that in the non-AIS group (p< 0.05); the carotid plaques in the AIS group mainly consisted of non-calcified plaques, while those in the non-AIS group mainly consisted of calcified plaques. The effective atomic number (Zeff), slope of the energy spectrum curve (λH), electron density (ED), and iodine-no-water value of the carotid plaques in the AIS group were lower than those in the non-AIS (p< 0.05). For the differentiation of the carotid plaques in the AIS group from those in the non-AIS group, the area under the curve (AUC) of Zeff amounted to 0.637 (cut-off value: 11.865; sensitivity: 72.5%; specificity: 56.2%), the AUC of λH amounted to 0.628 (cut-off value: 19.56; sensitivity: 76.3%; specificity: 51.6%), and that for ED amounted to 0.624 (cut-off value: 110.45; sensitivity: 60.0%; specificity: 64.1%), AUC of iodine-no-water value amounted to 0.645 (cut-off value: 9.125; sensitivity: 61.3%; specificity: 65.6%). Conclusion: In summary, the quantitative parameters of dual-layer detector spectral CT can be used to assess plaque stability and have certain value in the diagnosis of AIS. The quantitative parameters can effectively differentiate carotid plaques in AIS and non-AIS patients. [ABSTRACT FROM AUTHOR]

To demonstrate the feasibility and potential of using a second-generation prototype photon-counting computed tomography (CT) system to provide simultaneous high spatial resolution images and high spectral resolution material information across a range of routine imaging tasks using clinical patient exposure levels. The photon-counting system employs an innovative silicon-based photon-counting detector to provide a balanced approach to ultra-high-resolution spectral CT imaging. An initial cohort of volunteer subjects was imaged using the prototype photon-counting system. Acquisition technique parameters and radiation dose exposures were guided by routine clinical exposure levels used at the institution. Images were reconstructed in native slice thickness using an early version of a spectral CT reconstruction algorithm Samples of images across a range of clinical tasks were selected and presented for review. Clinical cases are presented across inner ear, carotid angiography, chest, and musculoskeletal imaging tasks. Initial reconstructed images illustrate ultra-high spatial resolution imaging. The fine detail of small structures and pathologies is clearly visualized, and structural boundaries are well delineated. The prototype system additionally provides concomitant spectral information with high spatial resolution. This initial study demonstrates that routine imaging at clinically appropriate patient exposure levels is feasible using a novel deep-silicon photon-counting detector CT system. Furthermore, a deep-silicon detector may provide a balanced approach to photon-counting CT, providing high spatial resolution imaging with simultaneous high-fidelity spectral information. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The authors applied Anatomique Benoist Girard II (ABG II) stems for total hip arthroplasty in some Dorr type C femurs as early attempts. Here, the authors compared the long-term follow-up results between ABG II stems and the 'well-performing' Corail stems and their monochromatic images. Methods: Among 3214 primary total hip arthroplasty records, 43 short ABG II stems and 67 standard-length Corail stems implanted in Dorr type C femurs were eligible and enrolled in this retrospective cohort study, with a mean follow-up of 10.3 years. Revision rates, Harris hip scores, and radiologic signs were compared. Spectral CT scans from a representative sample were obtained, and monochromatic images were reconstructed. A quantitative method was developed to measure the volume of the gap around stems. Patient-specific finite element analysis was conducted to investigate the strains. Results: The revision rate of ABG II stems was significantly higher than that of Corail stems (21 vs. 3%, P< 0.05). In the monochromatic images, fewer spot-weld signs (2.2 vs. 3.4, P <0.05) and wider gaps around stems (1.64 cm3 vs. 0.13 cm3, P<0.05) were observed on average in the ABG II group. The mean maximum principal strains of the proximal femurs in the ABG II group were close to the yield strains and significantly larger than those in the Corail group (0.0052 vs. 0.0011, P< 0.05). Conclusions: There was a high risk of postoperative periprosthetic femoral fracture for ABG II stems in Dorr type C femurs. Monochromatic images provided some insight into the failure mechanism. Level of Evidence: III. [ABSTRACT FROM AUTHOR]

Computed tomography aortic valve calcium (AVC) score has accepted value for diagnosing and predicting outcomes in aortic stenosis (AS). Multi-energy CT (MECT) allows virtual non-contrast (VNC) reconstructions from contrast scans. We aim to compare the VNC-AVC score to the true non-contrast (TNC)-AVC score for assessing AS severity. We prospectively included patients undergoing a MECT for transcatheter aortic valve replacement (TAVR) planning. TNC-AVC was acquired before contrast, and VNC-AVC was derived from a retrospectively gated contrast-enhanced scan. The Agatston scoring method was used for quantification, and linear regression analysis to derive adjusted-VNC values. Among 109 patients (55% female) included, 43% had concordant severe and 14% concordant moderate AS. TNC scan median dose-length product was 116 ​mGy∗cm. The median TNC-AVC was 2,107 AU (1,093–3,372), while VNC-AVC was 1,835 AU (1293-2,972) after applying the coefficient (1.46) and constant (743) terms. A strong correlation was demonstrated between methods (r ​= ​0.93; p ​< ​0.001). Using accepted thresholds (>1,300 AU for women and >2,000 AU for men), 65% (n ​= ​71) of patients had severe AS by TNC-AVC and 67% (n ​= ​73) by adjusted-VNC-AVC. After estimating thresholds for adjusted-VNC (>1,564 AU for women and >2,375 AU for men), 56% (n ​= ​61) had severe AS, demonstrating substantial agreement with TNC-AVC ( κ ​= ​0.77). MECT-derived VNC-AVC showed a strong correlation with TNC-AVC. After adjustment, VNC-AVC demonstrated substantial agreement with TNC-AVC, potentially eliminating the requirement for an additional scan and enabling reductions in both radiation exposure and acquisition time. We studied 109 prospectively included patients undergoing multi-energy CT (MECT) for transcatheter aortic valve replacement (TAVR). True non-contrast (TNC)- aortic valve calcium (AVC) score was prospectively acquired and virtual non-contrast (VNC)-AVC score derived from a contrast-enhanced scan. Agatston method was used and a correction proportionality constant was applied to VNC values. A total of 43% had concordant severe and 14% concordant moderate aortic stenosis (AS). MECT-derived VNC-AVC showed a strong correlation with TNC-AVC. After adjustment, VNC-AVC demonstrated substantial agreement with TNC-AVC, potentially eliminating the requirement for an additional scan and enabling reductions in both radiation exposure and acquisition time. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate advantages of spectral dual-layer computed tomography (CT) in diagnosing pulmonary embolism (PE). To compare D-dimer values in patients with PE and concomitant COVID-19 pneumonia to those in patients without PE and COVID-19 pneumonia. To compare D-dimer values in cases of minor versus extensive PE. Methods: A monocentric retrospective study of 1500 CT pulmonary angiographies (CTPAs). Three groups of 500 consecutive examinations: 1) using conventional multidetector CT (CTC), 2) using spectral dual-layer CT (CTS), and 3) of COVID-19 pneumonia patients using spectral dual-layer CT (COV). Only patients with known D-dimer levels were enrolled in the study. Results: Prevalence of inconclusive PE findings differed significantly between CTS and CTC (0.8 % vs. 5.4 %, p < 0.001). In all groups, D-dimer levels were significantly higher in PE positive patients than in patients without PE (CTC, 8.04 vs. 3.05 mg/L; CTS, 6.92 vs. 2.57 mg/L; COV, 10.26 vs. 2.72 mg/L, p < 0.001). There were also statistically significant differences in D-dimer values between minor and extensive PE in the groups negative for COVID-19 (CTC, 5.16 vs. 8.98 mg/L; CTS 3.52 vs. 9.27 mg/L, p < 0.001). The lowest recorded D-dimer value for proven PE in patients with COVID-19 pneumonia was 1.19 mg/L. Conclusion: CTPAs using spectral dual-layer CT reduce the number of inconclusive PE findings. Plasma D-dimer concentration increases with extent of PE. Cut-off value of D-dimer with 100 % sensitivity for patients with COVID-19 pneumonia could be doubled to 1.0 mg/L. This threshold would have saved 110 (22 %) examinations in our cohort.

Objective To investigate the effect of different radiation doses on the accuracy of iodine quantification and CT value of virtual monochromatic image with a muti-spectral CT iodine phantom using second-generation dual-layer detector spectral CT (DLCT). Methods The second-generation DLCT scan abdominal phantom was used, which consisted of six different concentrations of iodine rods at 4, 8 and 12 g/L. The scan parameters were set at 100, 120 and 140 kVp tube voltages, and six radiation exposure sequences were scanned at each tube voltage: 5, 10, 15, 20, 25 and 30 mGy. Measure the iodine concentration value and the CT value at the single level of 50, 70 and 100 keV, and calculate the absolute error value of iodine concentration and CT value under different radiation exposure. SPSS 22.0 software single factor analysis of variance was used to compare the difference between the iodine concentration and the absolute error of CT value under different Radiation exposure, and LSD method was used to compare the two groups. Results The absolute error of CT value of 4 g/L iodine rod with 10~30 mGy scanning at 100 keV energy level was significantly higher than that of 12 g/L and 8 g/L(P＜0.05), and the absolute error of iodine concentration measurement of other iodine rods with different concentrations at different Radiation exposure had no statistical difference. Conclusions The second-generation double-layer detector spectral CT has high measurement stability for iodine concentration quantification and CT value accuracy under conditions of radiation dose greater than 5 mGy.

BackgroundTo explore the value of dual-energy spectral CT in distinguishing solitary pulmonary tuberculosis (SP-TB) from solitary lung adenocarcinoma (S-LUAD).MethodsA total of 246 patients confirmed SP-TB (n = 86) or S-LUAD (n = 160) were retrospectively included. Spectral CT parameters include CT40keV value, CT70keV value, iodine concentration (IC), water concentration (WC), effective atomic number (Zeff), and spectral curve slope (λ70keV). Data were measured during the arterial phase (AP) and venous phase (VP). Chi-square test was used to compare categorical variables, Wilcoxon rank-sum test was used to compare continuous variables, and a two-sample t-test was used to compare spectral CT parameters. ROC curves were used to calculate diagnostic efficiency.ResultsThere were significant differences in spectral CT quantitative parameters (including CT40keV value [all P< 0.001] , CT70keV value [all P< 0.001], λ70keV [P< 0.001, and P = 0.027], Zeff [P =0.015, and P = 0.001], and IC [P =0.002, and P = 0.028]) between the two groups during the AP and VP. However, WC (P = 0.930, and P = 0.823) was not statistically different between the two groups. The ROC curve analysis showed that the AUC in the AP and VP was 90.9% (95% CI, 0.873-0.945) and 83.4% (95% CI, 0.780-0.887), respectively. The highest diagnostic performance (AUC, 97.6%; 95% CI, 0.961-0.991) was achieved when all spectral CT parameters were combined with clinical variables.ConclusionDual-energy spectral CT has a significant value in distinguishing SP-TB from S-LUAD.