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Background: Low-kiloelectron volt (keV) virtual monochromatic images (VMIs) from low-dose (LD) dual-energy computed tomography (DECT) can enhance lesion contrast but suffer from high image noise. Recently, a deep learning image reconstruction (DLIR) algorithm has been developed and shown significant potential in suppressing image noise and improving image quality. To date, the capacity of LD low-keV thoracic-abdominal-pelvic DECT with DLIR to detect various types of tumor lesions have not been assessed. Hence, this study aimed to evaluate the image quality and lesion detection capabilities of LD VMIs using DLIR with thoracic-abdominal-pelvic DECT versus standard-dose (SD) iterative reconstruction (IR) in oncology patients. Methods: This prospective intraindividual study included 56 oncology patients who received a SD (13.86 mGy) and a consecutive LD (7.15 mGy) thoracic-abdominal-pelvic DECT from April 2022 to July 2023 at The First Affiliated hospital of Zhengzhou University. SD VMIs were reconstructed using IR at 50 keV (SD-IR50 keV), while LD VMIs were processed using DLIR at 50 keV (LD-DL50 keV) and 40 keV (LD-DL40 keV), respectively. Quantitative image parameters [computed tomography (CT) values, image noise, and contrast-to-noise ratios (CNRs)], qualitative metrics (image noise, vessel conspicuity, image contrast, artificial sensation, and overall image quality), and lesion CNRs and conspicuity were compared. The lesion detection rates in the SD-IR50 keV, LD-DL50 keV, and LD-DL40 keV VMIs were assessed according to lesion location (lung, liver, and lymph), type, and size. Repeated measures analysis of variance and the Friedman test were applied for comparing quantitative and qualitative measures, respectively. The Cochran Q test was used for comparing lesion detection rates. Results: Compared to SD-IR50 keV VMIs, LD-DL50 keV VMIs showed similar CT values and image noise (P>0.05), similar (P>0.05) or higher(P<0.05) CNRs, similar (P>0.05) or superior (P<0.05) perceptual image quality, and similar (P>0.05) or higher (P<0.001) lesion CNR and conspicuity. LD-DL40 keV VMIs exhibited higher CT values (by 40.4-47.1%) and CNRs (by 21.8-39.8%) (P<0.001), equivalent image noise, similar (P>0.05) or superior (P<0.05) perceptual image quality except for artificial sensation, and similar (P>0.05) or higher (P<0.001) lesion CNRs (by 16.5-46.3%) and conspicuity. The VMIs of LD-DL50 keV and LD-DL40 keV were consistent with those of SD-IR50 keV in terms of lesion detection capability in pulmonary nodules [SD-IR50 keV vs. LD-DL50 keV vs. LD-DL40 keV: 88/88 (100.0%) vs. 88/88 (100.0%) vs. 88/88 (100.0%); P>0.99], for lymph nodes [125/126 (99.2%) vs. 123/126 (97.6%) vs. 124/126 (98.4%); P>0.05], and high-contrast liver lesions [12/12 (100.0%) vs. 12/12 (100.0%) vs. 12/12 (100.0%); P>0.05], but not for small liver lesions (≤0.5 cm) [63/65 (96.9%) vs. 43/65 (66.2%) vs. 51/65 (78.5%); P<0.05] or low-contrast liver lesions [198/200 (99.0%) vs. 174/200 (87.0%) vs. 183/200 (91.5%); P<0.05]. Conclusions: VMIs at 40 keV with DLIR enables a 50% decrease in the radiation dose while largely maintaining diagnostic capabilities for multidetection of pulmonary nodules, lymph nodes, and liver lesions in oncology patients.

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OBJECTIVES: To compare the diagnostic performance of conventional non-contrast CT, dual-energy spectral CT, and chemical-shift MRI (CS-MRI) in discriminating lipid-poor adenomas (> 10-HU on non-contrast CT) from non-adenomas. METHODS: A total of 110 patients (69 men; 41 women; mean age 66.5 ± 13.4 years) with 80 lipid-poor adenomas and 30 non-adenomas who underwent non-contrast dual-layer spectral CT and CS-MRI were retrospectively identified. For each lesion, non-contrast attenuation on conventional 120-kVp images, ΔHU-index ([attenuation difference between virtual monoenergetic 140-keV and 40-keV images]/conventional attenuation × 100), and signal intensity index (SI-index) were quantified. Each parameter was compared between adenomas and non-adenomas using the Mann-Whitney U-test. The area under the receiver operating characteristic curve (AUC) and sensitivity to achieve > 95% specificity for adenoma diagnosis were determined. RESULTS: Conventional non-contrast attenuation was lower in adenomas than in non-adenomas (22.4 ± 8.6 HU vs 32.8 ± 48.5 HU), whereas ΔHU-index (148.0 ± 103.2 vs 19.4 ± 25.8) and SI-index (41.6 ± 19.6 vs 4.2 ± 10.2) were higher in adenomas (all, p < 0.001). ΔHU-index showed superior performance to conventional non-contrast attenuation (AUC: 0.919 [95% CI: 0.852-0.963] vs 0.791 [95% CI: 0.703-0.863]; sensitivity: 75.0% [60/80] vs 27.5% [22/80], both p < 0.001), and near equivalent to SI-index (AUC: 0.952 [95% CI: 0.894-0.984], sensitivity 85.0% [68/80], both p > 0.05). Both the ΔHU-index and SI-index provided a sensitivity of 96.0% (48/50) for hypoattenuating adenomas (≤ 25 HU). For hyperattenuating (> 25 HU) adenomas, SI-index showed higher sensitivity than ΔHU-index (66.7% [20/30] vs 40.0% [12/30], p = 0.022). CONCLUSIONS: Non-contrast spectral CT and CS-MRI outperformed conventional non-contrast CT in distinguishing lipid-poor adenomas from non-adenomas. While CS-MRI demonstrated superior sensitivity for adenomas measuring > 25 HU, non-contrast spectral CT provided high discriminative values for adenomas measuring ≤ 25 HU. CLINICAL RELEVANCE STATEMENT: Spectral attenuation analysis improves the diagnostic performance of non-contrast CT in discriminating lipid-poor adrenal adenomas, potentially serving as an alternative to CS-MRI and obviating the necessity for additional diagnostic workup in indeterminate adrenal incidentalomas, particularly for lesions measuring ≤ 25 HU. KEY POINTS: Incidental adrenal lesion detection has increased as abdominal CT use has become more frequent. Non-contrast spectral CT and CS-MRI differentiated lipid-poor adenomas from non-adenomas better than conventional non-contrast CT. For lesions measuring ≤ 25 HU, spectral CT may obviate the need for additional evaluation.

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OBJECTIVES: This study investigated the diagnostic performance of dual-energy computed tomography (CT) and deep learning for the preoperative classification of equivocal lymph nodes (LNs) on CT images in thyroid cancer patients. METHODS: In this prospective study, from October 2020 to March 2021, 375 patients with thyroid disease underwent thin-section dual-energy thyroid CT at a small field of view (FOV) and thyroid surgery. The data of 183 patients with 281 LNs were analyzed. The targeted LNs were negative or equivocal on small FOV CT images. Six deep-learning models were used to classify the LNs on conventional CT images. The performance of all models was compared with pathology reports. RESULTS: Of the 281 LNs, 65.5% had a short diameter of less than 4 mm. Multiple quantitative dual-energy CT parameters significantly differed between benign and malignant LNs. Multivariable logistic regression analyses showed that the best combination of parameters had an area under the curve (AUC) of 0.857, with excellent consistency and discrimination, and its diagnostic accuracy and sensitivity were 74.4% and 84.2%, respectively (p < 0.001). The visual geometry group 16 (VGG16) based model achieved the best accuracy (86%) and sensitivity (88%) in differentiating between benign and malignant LNs, with an AUC of 0.89. CONCLUSIONS: The VGG16 model based on small FOV CT images showed better diagnostic accuracy and sensitivity than the spectral parameter model. Our study presents a noninvasive and convenient imaging biomarker to predict malignant LNs without suspicious CT features in thyroid cancer patients. CLINICAL RELEVANCE STATEMENT: Our study presents a deep-learning-based model to predict malignant lymph nodes in thyroid cancer without suspicious features on conventional CT images, which shows better diagnostic accuracy and sensitivity than the regression model based on spectral parameters. KEY POINTS: Many cervical lymph nodes (LNs) do not express suspicious features on conventional computed tomography (CT). Dual-energy CT parameters can distinguish between benign and malignant LNs. Visual geometry group 16 model shows superior diagnostic accuracy and sensitivity for malignant LNs.

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

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PURPOSE: To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9). METHODS: This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test. RESULTS: The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076). CONCLUSIONS: Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

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OBJECTIVES: To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). METHODS: In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. RESULTS: Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. CONCLUSION: DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. CLINICAL RELEVANCE STATEMENT: Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. KEY POINTS: • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

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The liver, spleen, and kidneys are the commonest injured solid organs in blunt and penetrating trauma. The American Association for the Surgery of Trauma (AAST) Organ Injury Scale (OIS) is the most widely accepted system for categorizing traumatic injuries. Grading systems allow clear communication of findings between clinical teams and assign a measurable severity of injury, which directly correlates with morbidity and mortality. The 2018 revised AAST OIS emphasizes reliance on CT for accurate grading; in particular regarding vascular injuries. Dual-Energy CT (DECT) has emerged as a promising tool with multiple clinical applications already demonstrated. In this review article, we summarize the basic principles of CT attenuation to refresh the minds of our readers and we scrutinize DECT's technology as opposed to conventional Single-Energy CT (SECT). This is followed by outlining the benefits of various DECT postprocessing techniques, which authors of this article refer to as the 3Ms (Mapping of Iodine, Material decomposition, and Monoenergetic virtual imaging), in aiding radiologists to confidently assign an OIS as well as problem solve complex injury patterns. In addition, a thorough discussion of changes to the revised AAST OIS focusing on definitions of key terms used in reporting injuries is described.

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OBJECTIVES: The purpose of this study was to evaluate a three-material decomposition algorithm for hepatic fat quantification using a dual-layer computed tomography (DL-CT) and MRI as reference standard on a large patient cohort. METHOD: A total of 104 patients were retrospectively included in our study, i.e., each patient had an MRI exam and a DL-CT exam in our institution within a maximum of 31 days. Four regions of interest (ROIs) were positioned blindly and similarly in the liver, by two independent readers on DL-CT and MRI images. For DL-CT exams, all imaging phases were included. Fat fraction agreement between CT and MRI was performed using intraclass correlation coefficients (ICC), determination coefficients R2, and Bland-Altman plots. Diagnostic performance was determined using sensitivity, specificity, and positive and negative predictive values. The cutoff for steatosis was 5%. RESULTS: Correlation between MRI and CT data was excellent for all perfusion phases with ICC calculated at 0.99 for each phase. Determination coefficients R2 were also good for all perfusion phases (about 0.95 for all phases). Performance of DL-CT in the diagnosis of hepatic steatosis was good with sensitivity between 89 and 91% and specificity ranging from 75 to 80%, depending on the perfusion phase. The positive predictive value was ranging from 78 to 93% and the negative predictive value from 82 to 86%. CONCLUSION: Multi-material decomposition in DL-CT allows quantification of hepatic fat fraction with a good correlation to MRI data. CLINICAL RELEVANCE STATEMENT: The use of DL-CT allows for detection of hepatic steatosis. This is especially interesting as an opportunistic finding CT performed for other reasons, as early detection can help prevent or slowdown the development of liver metabolic disease. KEY POINTS: • Hepatic fat fractions provided by the dual-layer CT algorithm is strongly correlated with that measured on MRI. • Dual-layer CT is accurate to detect hepatic steatosis ≥ 5%. • Dual-layer CT allows opportunistic detection of steatosis, on CT scan performed for various indications.

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OBJECTIVES: To determine whether dual-energy CT (DECT) can be used to accurately and reliably detect anterior cruciate ligament (ACL) rupture. MATERIALS AND METHODS: Participants with unilateral ACL rupture were prospectively enrolled, and the bilateral knees were scanned by DECT. A tissue-specific mapping algorithm was applied to improve the visualization of the ACLs. The 80-keV CT value, mixed-keV CT value, electron density (Rho), and effective atomic number (Zeff) were measured to quantitatively differentiate torn ACLs from normal ACLs. MRI and arthroscopy served as the reference standards. RESULTS: Fifty-one participants (mean age, 27.0 ± 8.7 years; 31 men) were enrolled. Intact and torn ACLs were explicitly differentiated on color-coded DECT images. The 80-keV CT value, mixed-keV CT value, and Rho were significantly lower for the torn ACLs than for the intact ACLs (p < 0.001). The optimal cutoff values were an 80-keV CT value of 61.8 HU, a mixed-keV CT value of 60.9 HU, and a Rho of 51.8 HU, with AUCs of 98.0% (95% CI: 97.0-98.9%), 99.2% (95% CI: 98.6-99.7%), and 99.8% (95% CI: 99.6-100.0%), respectively. Overall, DECT had almost perfect reliability and validity in detecting ACL integrity (sensitivity = 97.1% [95% CI: 88.1-99.8%]; specificity = 98.0% [95% CI: 89.5-99.9%]; PPV = 98.0% [95% CI: 93.0-99.8%]; NPV = 97.1% [95% CI: 91.7-99.4%]; accuracy = 97.5% [95% CI: 94.3-99.2%]). There was no evidence of a difference between MRI and DECT in the diagnostic performance (p > 0.99). CONCLUSION: DECT has excellent diagnostic accuracy and reliability in qualitatively and quantitatively diagnosing ACL rupture. CLINICAL RELEVANCE STATEMENT: DECT could validly and reliably diagnose ACL rupture using both qualitative and quantitative methods, which may become a promising substitute for MRI to evaluate the integrity of injured ACLs and the maturity of postoperative ACL autografts. KEY POINTS: • On color-coded DECT images, an uncolored ACL was a reliable sign for qualitatively diagnosing ACL rupture. • The 80-keV CT value, mixed-keV CT value, and Rho were significantly lower for the torn ACLs than for the intact ACLs, which contributed to the quantitative diagnosis of ACL rupture. • DECT had an almost perfect diagnostic performance for ACL rupture, and diagnostic capability was comparable between MRI and DECT.

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OBJECTIVES: To evaluate the quality of virtual monochromatic images (VMIs) from spectral photon-counting CT (SPCCT) and two energy-integrating detector dual-energy CT (EID-DECT) scanners from the same manufacturer, for the coronary lumen. METHODS: A 21-cm section of the Mercury v4.0 phantom was scanned using a cardiac CT protocol. VMIs from 40 to 90 keV were reconstructed using high-resolution (HR) parameters for EID-DECT and SPCCT (CB and HRB kernels at 0.67 mm slice thickness, respectively). Ultra-high-resolution (UHR) parameters were used in addition to SPCCT (detailed-2 kernel, 0.43 mm slice thickness). Noise-power-spectrum (NPS), task-based transfer function (TTF), and detectability index (d') were computed for 2-mm-diameter lumen detection. In consensus, two radiologists analyzed the quality of the images from 8 patients who underwent coronary CTA on both CT systems. RESULTS: For all keV images, fpeak, f50, and d' were higher with SPCCT. The fpeak and f50 were higher with UHR-SPCCT with greater noise and lower d' compared to those of the HR-SPCCT images. Noise magnitude was constant for all energy levels (keV) with both systems, and lower with HR images, and d' decreased as keV decreased. Subjective analysis showed greater lumen sharpness and overall quality for HR and UHR-SPCCT images using all keV, with a greater difference at low keV compared to HR-EID-DECT images. CONCLUSION: HR and UHR-SPCCT images gave greater detectability of the coronary lumen for 40 to 90 keV VMIs compared to two EID-DECT systems, with benefits of higher lumen sharpness and overall quality. KEY POINTS: • Compared with 2 dual-energy CT systems, spectral photon-counting CT (SPCCT) improved spatial resolution, noise texture, noise magnitude, and detectability of the coronary lumen. • Use of ultra-high-resolution parameters with SPCCT improved spatial resolution and noise texture and provided high detectability of the coronary lumen, despite an increase in noise magnitude. • In eight patients, radiologists found greater overall image quality with SPCCT for all virtual monochromatic images with a greater difference at low keV, compared with dual-energy CT systems.

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OBJECTIVES: To evaluate the performance of extreme gradient boosting (XGBoost) combined with multiparameters from dual-energy computed tomography (mpDECT) to differentiate between multiple myeloma (MM) of the spine and vertebral osteolytic metastases (VOM). METHODS: For this retrospective study, 28 patients (83 lesions) with MM of the spine and 23 patients (54 lesions) with VOM who underwent DECT were included. The mpDECT for each lesion, including normalized effective atomic number, slope of the spectral Hounsfield unit curve, CT attenuation, and virtual noncalcium (VNCa), was obtained. Boruta was used to select the key parameters, and then subsequently merged with XGBoost to yield a prediction model. The lesions were divided into the training and testing group in a 3:1 ratio. The highest performance of the univariate analysis was compared with XGBoost using the Delong test. RESULTS: The mpDECT of MM was significantly lower than that of VOM (all p < 0.05). In univariate analysis, VNCa had the highest area under the receiver operating characteristic curve (AUC) in the training group (0.81) and testing group (0.87). Based on Boruta, 6 parameters of DECT were selected for XGBoost model construction. The XGBoost model achieved an excellent and stable diagnostic performance, as shown in the training group (AUC of 1.0) and testing group (AUC of 0.97), with a sensitivity of 80%, a specificity of 95%, and an accuracy of 88%, which was superior to VNCa (p < 0.05). CONCLUSIONS: XGBoost combined with mpDECT yielded promising performance in differentiating between MM of the spine and VOM. KEY POINTS: • The multiparameters obtained from dual-energy CT of multiple myeloma differed significantly from those of vertebral osteolytic metastases. • The virtual noncalcium offered the highest AUC in the univariate analysis to distinguish multiple myeloma from vertebral osteolytic metastases. • Extreme gradient boosting combined with multiparameters from dual-energy CT had a promising performance to distinguish multiple myeloma from vertebral osteolytic metastases.

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BACKGROUND: Iodine concentrations measured using dual-energy spectral CT (DESCT) have been recently proposed as providing good performance for examining tissues hemodynamics. PURPOSE: To evaluate the diagnostic efficacy of DESCT-derived parameters in evaluating portal venous pressure in patients with liver cirrhosis. MATERIAL AND METHODS: A total of 71 patients with liver cirrhosis who underwent percutaneous transhepatic portal vein puncture procedures were included in this study. All participants underwent DESCT and gastrointestinal endoscopy within one month before the operation. The direct portal venous pressure of each participant was measured preoperatively. RESULTS: Stepwise multivariate linear regression analysis showed that the iodine concentrations in the portal vein and hepatic parenchyma during the portal venous phase and the platelet count were independently correlated with the direct portal venous pressure (P < 0.001, P < 0.001, and P = 0.030, respectively). Receiver operating characteristic analysis revealed that the normalized iodine concentration of the hepatic parenchyma had the best performance for identifying clinically significant portal hypertension (≥10 mmHg), esophageal varices, and high-risk esophageal varices (the area under the curve values were 0.951, 0.932, and 0.960, respectively). CONCLUSION: The normalized iodine concentration of the hepatic parenchyma is a reliable parameter to non-invasively assess portal venous pressure in patients with liver cirrhosis.

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BACKGROUND: Early and accurate diagnosis is vital for avoiding the development of nondisplaced fractures to displaced fractures. Dual-energy CT (Computed Tomography) can detect bone marrow edema (BME), which may help to detect non-displaced fractures. AIM: To evaluate the value of DECT (Dual-Energy Computed Tomography) VNCa (Virtual noncalcium) images for improving diagnostic performance and confidence in acute non-displaced knee fractures. METHODS: 125 patients with clinical suspicion of knee fractures underwent both DECT and MR. Conventional linear-blended CT and VNCa images were obtained from DECT. First, five readers with varying levels of experience evaluated the presence of fractures on conventional linear-blended CT and graded their diagnostic confidence on a scale of 1 to 10. Then BME with VNCa images was evaluated and compared with MR. Finally, the VNCa images combined with conventional linear-blended CT images were used to reassess the presence of fractures and diagnostic confidence. Diagnostic performance and matched pair analyses were performed. RESULTS: 20 non-displaced knee fractures were detected. The consistency test of VNCa images and MR by five radiologists showed Kappa values are 0.76, 0.79, 0.81,0.85,and 0.90,respectively. The diagnostic performance of all readers was improved when using VNCa images combined with conventional linear-blended CT compared with that with conventional linear-blended CT alone. Diagnostic confidence was improved with combined conventional linear-blended CT and VNCa images (median score:8,8,9,9, and 10, respectively) compared with conventional linear-blended CT alone (median score:7,7,8,9, and 9). CONCLUSION: DECT VNCa images could improve the radiologists' diagnostic performance and confidence with varying levels of experience in the detection of non-displaced knee fractures.

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Purpose: To assess the usefulness of various metal artifact reduction (MAR) methods in patients with hip prostheses. Materials and Methods: This retrospective study included 47 consecutive patients who underwent hip arthroplasty and dual-energy CT. Conventional polyenergetic image (CI), orthopedic-MAR (O-MAR), and virtual monoenergetic image (VMI, 50-200 keV) were tested for MAR. Quantitative analysis was performed in seven regions around the prostheses. Qualitative assessments included evaluation of the degree of artifacts and the presence of secondary artifacts. Results: The lowest amount of image noise was observed in the O-MAR, followed by the VMI. O-MAR also showed the lowest artifact index, followed by high-keV VMI in the range of 120-200 keV (soft tissue) or 200 keV (bone). O-MAR had the highest contrast-to-noise ratio (CNR) in regions with severe hypodense artifacts, while VMI had the highest CNR in other regions, including the periprosthetic bone. On assessment of the CI of pelvic soft tissues, VMI showed a higher structural similarity than O-MAR. Upon qualitative analysis, metal artifacts were significantly reduced in O-MAR, followed by that in VMI, while secondary artifacts were the most frequently found in the O-MAR (p < 0.001). Conclusion: O-MAR is the best technique for severe MAR, but it can generate secondary artifacts. VMI at high keV can be advantageous for evaluating periprosthetic bone.

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Purpose: We compared the radiation dose and image quality between the 2nd generation and the 3rd generation dual-source single-energy (DSSE) and dual-source dual-energy (DSDE) CT of the abdomen. Materials and Methods: We included patients undergoing follow-up abdominal CT after partial or radical nephrectomy in the first 10 months of 2019 (2nd generation DS CT) and the first 10 months of 2020 (3rd generation DS CT). We divided the 320 patients into 4 groups (A, 2nd generation DSSE CT; B, 2nd generation DSDE CT; C, 3rd generation DSSE CT; and D, 3rd generation DSDE CT) (n = 80 each) matched by sex and body mass index. Radiation dose and image quality (objective and subjective qualities) were compared between the groups. Results: The mean size-specific dose estimation of 3rd generation DSDE CT group was significantly lower than that of the 2nd generation DSSE CT (42.5%, p = 0.013) and 2nd generation DSDE CT (46.9%, p = 0.015) groups. Interobserver agreement was excellent for the overall image quality (intraclass correlation coefficient [ICC]: 0.8867) and image artifacts (ICC: 0.9423). Conclusion: Our results showed a considerable reduction in the radiation dose while maintaining high image quality with 3rd generation DSDE CT as compared to the 2nd generation DSDE CT and 2nd generation DSSE CT.

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Objective: To explore the value of dual-energy computed tomography (DECT) radiomics of the regional largest short-axis lymph nodes for evaluating lymph node metastasis in patients with rectal cancer. Materials and Methods: One hundred forty-one patients with rectal cancer (58 in LNM+ group, 83 in LNM- group) who underwent preoperative total abdominal DECT were divided into a training group and testing group (7:3 ratio). After post-processing DECT venous phase images, 120kVp-like images and iodine (water) images were obtained. The highest-risk lymph nodes were identified, and their long-axis and short-axis diameter and DECT quantitative parameters were measured manually by two experienced radiologists who were blind to the postoperative pathological results. Four DECT parameters were analyzed: arterial phase (AP) normalized iodine concentration, AP normalized effective atomic number, the venous phase (VP) normalized iodine concentration, and the venous phase normalized effective atomic number. The carcinoembryonic antigen (CEA) levels were recorded one week before surgery. Radiomics features of the largest lymph nodes were extracted, standardized, and reduced before modeling. Radomics signatures of 120kVp-like images (Rad-signature120kVp) and iodine map (Rad-signatureImap) were built based on Logistic Regression via Least Absolute Shrinkage and Selection Operator (LASSO). Results: Eight hundred thirty-three features were extracted from 120kVp-like and iodine images, respectively. In testing group, the radiomics features based on 120kVp-like images showed the best diagnostic performance (AUC=0.922) compared to other predictors [CT morphological indicators (short-axis diameter (AUC=0.779, IDI=0.262) and long-axis diameter alone (AUC=0.714, IDI=0.329)), CEA alone (AUC=0.540, IDI=0.414), and normalized DECT parameters alone (AUC=0.504-0.718, IDI=0.290-0.476)](P<0.05 in Delong test). Contrary, DECT iodine map-based radiomic signatures showed similar performance in predicting lymph node metastasis (AUC=0.866). The decision curve showed that the 120kVp-like-based radiomics signature has the highest net income. Conclusion: Predictive model based on DECT and the largest short-axis diameter lymph nodes has the highest diagnostic value in predicting lymph node metastasis in patients with rectal cancer.

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OBJECTIVES: To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing hypoenhancing hepatic metastases. METHODS: This retrospective study included 131 patients who underwent contrast-enhanced DECT (80-kVp and 150-kVp with a tin filter) in the portal venous phase for hepatic metastasis surveillance. Linearly blended images simulating 100-kVp images (100-kVp), standard 40-keV VMI images (40-keV VMI), and post-processed 40-keV VMI using a vendor-agnostic DLM (i.e., DLM 40-keV VMI) were reconstructed. Lesion conspicuity and diagnostic acceptability were assessed by three independent reviewers and compared using the Wilcoxon signed-rank test. The contrast-to-noise ratios (CNRs) were also measured placing ROIs in metastatic lesions and liver parenchyma. The detection performance of hepatic metastases was assessed by using a jackknife alternative free-response ROC method. The consensus by two independent radiologists was used as the reference standard. RESULTS: DLM 40-keV VMI, compared to 40-keV VMI and 100-kVp, showed a higher lesion-to-liver CNR (8.25 ± 3.23 vs. 6.05 ± 2.38 vs. 5.99 ± 2.00), better lesion conspicuity (4.3 (4.0-4.7) vs. 3.7 (3.7-4.0) vs. 3.7 (3.3-4.0)), and better diagnostic acceptability (4.3 (4.0-4.3) vs. 3.0 (2.7-3.3) vs. 4.0 (4.0-4.3)) (p < 0.001 for all). For lesion detection (246 hepatic metastases in 68 patients), the figure of merit was significantly higher with DLM 40-keV VMI than with 40-keV VMI (0.852 vs. 0.822, p = 0.012), whereas no significant difference existed between DLM 40-keV VMI and 100-kVp (0.852 vs. 0.842, p = 0.31). CONCLUSIONS: DLM 40-keV VMI provided better image quality and comparable diagnostic performance for detecting hypoenhancing hepatic metastases compared to linearly blended images. KEY POINTS: • DLM 40-keV VMI provides a superior image quality compared with 40-keV or 100-kVp for assessing hypoenhancing hepatic metastasis. • DLM 40-keV VMI has the highest CNR and lesion conspicuity score for hypoenhancing hepatic metastasis due to noise reduction and structural preservation. • DLM 40-keV VMI provides higher lesion detectability than standard 40-keV VMI (p = 0.012).

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BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) patients with a high tumor grade, lymphovascular invasion (LVI), or perineural invasion (PNI) tend to demonstrate a poor prognosis in clinical series. Thus, the identification of histopathological features, including tumor grade, LVI, and PNI, before treatment could be used to stratify the prognosis of patients with HNSCC. This study aimed to assess whether quantitative parameters derived from pretreatment dual-energy computed tomography (DECT) can predict the histopathological features of patients with HNSCC. METHODS: In this study, 72 consecutive patients with pathologically confirmed HNSCC were enrolled and underwent dual-phase (noncontrast-enhanced phase and contrast-enhanced phase) DECT examinations. Normalized iodine concentration (NIC), the slope of the spectral Hounsfield unit curve (λHU), and normalized effective atomic number (NZeff) were calculated. The attenuation values on 40-140 keV noise-optimized virtual monoenergetic images [VMIs (+)] in the contrast-enhanced phase were recorded. The diagnostic performance of the quantitative parameters for predicting histopathological features, including tumor grade, LVI, and PNI, was assessed by receiver operating characteristic curves. RESULTS: The NIC, λHU, NZeff, and attenuation value on the VMIs (+) at 40 keV (A40) in the grade III group, LVI-positive group, and PNI-positive group were significantly higher than those in the grade I and II groups, the LVI-negative group, and the PNI-negative group (all P values <0.05). A multivariate logistic regression model combining these 4 quantitative parameters improved the diagnostic performance of the model in predicting tumor grade, LVI, and PNI (areas under the curve: 0.969, 0.944, and 0.931, respectively). CONCLUSIONS: Quantitative parameters derived from pretreatment DECT, including NIC, λHU, NZeff, and A4,0 were found to be imaging markers for predicting the histopathological characteristics of HNSCC. Combining all these characteristics improved the predictive performance of the model.

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To evaluate the feasibility of spectral imaging with dual-layer spectral detector computed tomography (CT) for the diagnosis of acute coronary syndrome. We identified 30 consecutive patients who underwent cardiac CT using dual-layer spectral detector CT and were diagnosed with acute ischemic syndrome by an invasive coronary angiography. We reconstructed 120 kVp images and generated virtual monochromatic images (VMIs; 40-200 keV in 10 keV increments), iodine concentration maps, and effective atomic number (Z) maps. We calculated the contrast and contrast-to-noise ratio (CNR) between myocardial normal and hypo-perfusion and chose the VMIs with the best CNR for quantitative analysis. We compared the image noise, contrast, and CNR of 120 kVp images and the best VMIs, CT value, iodine concentration, and effective Z between myocardial normal and hypo-perfusion with the paired t test. As the X-ray energy decreased, venous attenuation, contrast, and CNR gradually increased. The 40 keV image yielded the best CNR. The contrast and CNR between myocardial normal and hypo-perfusion were significantly higher in 40 keV images than those in 120 kVp images. The iodine concentration and the effective Z were significantly higher in normal myocardium than those in hypo-perfused myocardium. Spectral imaging with dual-layer spectral detector CT is a feasible technique to detect the hypo-perfused area of acute ischemic syndrome.

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Background: Preoperative stereotactic planning of deep brain stimulation (DBS) using computed tomography (CT) imaging in patients with Parkinson's disease (PD) is of clinical interest. However, frame-induced metal artifacts are common in clinical practice, which can be challenging for neurosurgeons to visualize brain structures. Objectives: To evaluate the image quality and radiation exposure of patients with stereotactic frame brain CT acquired using a dual-source CT (DSCT) system in single- and dual-energy modes. Materials and Methods: We included 60 consecutive patients with Parkinson's disease (PD) and randomized them into two groups. CT images of the brain were performed using DSCT (Group A, an 80/Sn150 kVp dual-energy mode; Group B, a 120 kVp single-energy mode). One set of single-energy images (120 kVp) and 10 sets of virtual monochromatic images (50-140 keV) were obtained. Subjective image analysis of overall image quality was performed using a five-point Likert scale. For objective image quality evaluation, CT values, image noise, signal-to-noise ratio (SNR), and contrast-to-noise (CNR) were calculated. The radiation dose was recorded for each patient. Results: The mean effective radiation dose was reduced in the dual-energy mode (1.73 mSv ± 0.45 mSv) compared to the single-energy mode (3.16 mSv ± 0.64 mSv) (p < 0.001). Image noise was reduced by 46-52% for 120-140 keV VMI compared to 120 kVp images (both p < 0.01). CT values were higher at 100-140 keV than at 120 kVp images. At 120-140 keV, CT values of brain tissue showed significant differences at the level of the most severe metal artifacts (all p < 0.05). SNR was also higher in the dual-energy mode 90-140 keV compared to 120 kVp images, showing a significant difference between the two groups at 120-140 keV (all p < 0.01). The CNR was significantly better in Group A for 60-140 keV VMI compared to Group B (both p < 0.001). The highest subjective image scores were found in the 120 keV images, while 110-140 keV images had significantly higher scores than 120 kVp images (all p < 0.05). Conclusion: DSCT images using dual-energy modes provide better objective and subjective image quality for patients with PD at lower radiation doses compared to single-energy modes and facilitate brain tissue visualization with stereotactic frame DBS procedures.