Differentiation of Hamartomas and Malignant Lung Tumors in Single-Phased Dual-Energy Computed Tomography.

This study investigated the efficacy of single-phase dual-energy CT (DECT) in differentiating pulmonary hamartomas from malignant lung lesions using virtual non-contrast (VNC), iodine, and fat quantification. Forty-six patients with 47 pulmonary lesions (mean age: 65.2 ± 12.1 years; hamartomas-to-malignant lesions = 22:25; male: 67%) underwent portal venous DECT using histology, PET-CT and follow-up CTs as a reference. Quantitative parameters such as VNC, fat fraction, iodine density and CT mixed values were statistically analyzed. Significant differences were found in fat fractions (hamartomas: 48.9%; malignancies: 22.9%; p ≤ 0.0001) and VNC HU values (hamartomas: -20.5 HU; malignancies: 17.8 HU; p ≤ 0.0001), with hamartomas having higher fat content and lower VNC HU values than malignancies. CT mixed values also differed significantly (p ≤ 0.0001), but iodine density showed no significant differences. ROC analysis favored the fat fraction (AUC = 96.4%; sensitivity: 100%) over the VNC, CT mixed value and iodine density for differentiation. The study concludes that the DECT-based fat fraction is superior to the single-energy CT in differentiating between incidental pulmonary hamartomas and malignant lesions, while post-contrast iodine density is ineffective for differentiation.

A novel fully automated method for measuring ASPECTS to improve stroke diagnosis: Comparison to traditional ASPECTS.

BACKGROUND AND PURPOSE: To compare the accuracy of subjective Alberta Stroke Program Early CT Score (sASPECTS) evaluation and that of an automated prototype software (aASPECTS) on nonenhanced CT (NECT) in patients with early anterior territory stroke and controls using side-to-side quantification of hypoattenuated brain areas. METHODS: We retrospectively analyzed the NECT scans of 42 consecutive patients with ischemic stroke before reperfusion and 42 controls using first sASPECTS and subsequently aASPECTS. We assessed the differences in Alberta Stroke Program Early CT Score (ASPECTS) and calculated the sensitivity and specificity of NECT with CT perfusion, whereas cerebral blood volume (CBV) served as the reference standard for brain infarction. RESULTS: The clot was located in the middle cerebral artery (MCA) in 47.6% of cases and the internal carotid artery (ICA) in 28.6% of cases. Ten cases presented combined ICA and MCA occlusions. The stroke was right sided in 52.4% of cases and left sided in 47.6%. Reader-based NECT analysis yielded a median sASPECTS of 10. The median CBV-based ASPECTS was 7. Compared to the area of decreased CBV, sASPECTS yielded a sensitivity of 12.5% and specificity of 86.8%. The software prototype (aASPECTS) yielded an overall sensitivity of 65.5% and a specificity of 92.2%. The interreader agreement for ASPECTS evaluation of admission NECT and follow-up CT was almost perfect (κ = .93). The interreader agreement of the CBV color map evaluation was substantial (κ = .77). CONCLUSIONS: aASPECTS of NECT can outperform sASPECTS for stroke detection.

Dual-Energy CT for Accurate Discrimination of Intraperitoneal Hematoma and Intestinal Structures.

The purpose of this study was to evaluate the potential of dual-energy CT (DECT) with virtual unenhanced imaging (VNC) and iodine maps (IM) to differentiate between intraperitoneal hematomas (IH) and bowel structures (BS) compared to linearly blended DECT (DE-LB) images (equivalent to single-energy CT). This retrospective study included the DECT of 30 patients (mean age: 64.5 ± 15.1 years, 19 men) with intraperitoneal hematomas and 30 negative controls. VNC, IM, and DE-LB were calculated. Imaging follow-up and surgical reports were used as references. Three readers assessed diagnostic performance and confidence in distinguishing IH and BS for DE-LB, VNC, and IM. Diagnostic confidence was assessed on a five-point Likert scale. The mean values of VNC, IM, and DE-LB were compared with nonparametric tests. Diagnostic accuracy was assessed by calculating receiver operating characteristics (ROC). The results are reported as medians with interquartile ranges. Subjective image analysis showed higher diagnostic performance (sensitivity: 96.7−100% vs. 88.2−96.7%; specificity: 100% vs. 96.7−100%; p < 0.0001; ICC: 0.96−0.99) and confidence (Likert: 5; IRQ [5−5] vs. 4, IRQ [3−4; 4−5]; p < 0.0001; ICC: 0.80−0.96) for DECT compared to DE-LB. On objective image analysis, IM values for DECT showed significant differences between IH (3.9 HU; IQR [1.6, 8.0]) and BS (39.5 HU; IQR [29.2, 43.3]; p ≤ 0.0001). VNC analysis revealed a significantly higher attenuation of hematomas (50.5 HU; IQR [44.4, 59.4]) than BS (26.6 HU; IQR [22.8, 32.4]; p ≤ 0.0001). DE-LB revealed no significant differences between hematomas (60.5 HU, IQR [52.7, 63.9]) and BS (63.9 HU, IQR [58.0, 68.8]; p > 0.05). ROC analysis revealed the highest AUC values and sensitivity for IM (AUC = 100%; threshold by Youden-Index ≤ 19 HU) and VNC (0.93; ≥34.1 HU) compared to DE-LB (0.64; ≤63.8; p < 0.001). DECT is suitable for accurate discrimination between IH and BS by calculating iodine maps and VNC images.

Case Report: MRI, CEUS, and CT Imaging Features of Metanephric Adenoma with Histopathological Correlation and Literature Review.

The metanephric adenoma is an extremely rare, benign, embryonal-epithelial neoplasm of the kidney and has a good prognosis with appropriate treatment. It can present at any age and is often asymptomatic. Histologically, the lesion is well established; however, there have been only a few cases described with available detailed imaging findings, most of them with large renal masses typically depicted by computed tomography (CT). This case report includes imaging of contrast-enhanced MRI, contrast-enhanced ultrasound (CEUS), and CT, and thus adds to the information available, potentially promoting a nephron-sparing clinical pathway. We report on the clinical presentation, imaging, histopathological diagnosis, and treatment data of a 27-year-old female, in whom an incidental, symptomatic kidney tumor was detected. CT, CEUS, and MRI showed a suspicious unifocal renal lesion with inhomogeneous enhancement, which was indistinguishable from renal cell carcinoma. After laparoscopic resection, a metanephric adenoma with microscopically partially glandular, partially nest-like solid growth and without distinctive atypia was diagnosed pathohistologically. Immunohistochemistry results were positive for Wilms Tumor 1 and CD57 and negative for EMA and CK7: 2-3% positive cells in MIB1 coloring. At 3-month and 1-year follow-up, the patient was asymptomatic and imaging showed no recurrence of renal masses or metastases.

Effect of acquisition techniques, latest kernels, and advanced monoenergetic post-processing for stent visualization with third-generation dual-source CT.

PURPOSE The purpose of this study is to systematically evaluate the effect of tube voltage, current kernels, and monoenergetic post-processing on stent visualization. METHODS A 6 mm chrome-cobalt peripheral stent was placed in a dedicated phantom and scanned with the available tube voltage settings of a third-generation dual-source scanner in single-energy (SE) and dual-energy (DE) mode. Images were reconstructed using the latest convolution kernels and monoenergetic reconstructions (40-190 keV) for DE. The sharpness of stent struts (S), struts width (SW), contrast-to-noise-ratios (CNR), and pseudoenhancement (PE) between the vessel with and without stent were analyzed using an in-house built automatic analysis tool. Measurements were standardized through calculated z-scores. Z-scores were combined for stent (SQ), luminal (LQ), and overall depiction quality (OQ) by adding S and SW, CNR and SW and PE, and S and SW and CNR and PE. Two readers rated overall stent depiction on a 5-point Likert-scale. Agreement was calculated using linear-weighted kappa. Correlations were calculated using Spearman correlation coefficient. RESULTS Maximum values of S and CNR were 169.1 HU/pixel for [DE; 100/ Sn 150 kV; Qr59; 40 keV] and 50.0 for [SE; 70 kV; Bv36]. Minimum values of SW and PE were 2.615 mm for [DE; 80 to 90/ Sn 150 kV; Qr59; 140 to 190 keV] and 0.12 HU for [DE; 80/ Sn 150 kV; Qr36; 190 keV]. Best combined z-scores of SQ, LQ, and OQ were 4.53 for [DE; 100/ Sn 150 kV; Qr 59; 40 keV], 1.23 for [DE; 100/ Sn 150 kV; Qr59; 140 keV] and 2.95 for [DE; 90/ Sn 150 kV; Qr59; 50 keV]. Best OQ of SE was ranked third with 2.89 for [SE; 90 kV; Bv59]. Subjective agreement was excellent (kappa=0.86; P < .001) and correlated well with OQ (rs=0.94, P < .001). CONCLUSION Combining DE computed tomography (CT) acquisition with the latest kernels and monoenergetic post-processing allows for improved stent visualization as compared with SECT. The best overall results were obtained for monoenergetic reconstructions with 50 keV from DECT 90/Sn 150 kV acquisitions using kernel Qr59.

Beware of gastric tube in esophagectomy after gastric radiotherapy: A case report.

BACKGROUND: Gastric tube formation and pull-up is the most common technique of reconstruction following esophagectomy for esophageal cancer. If previous treatment with radiotherapy for gastric mucosa-associated lymphoid tissue (MALT)-lymphoma restricts suitability of the stomach for anastomosis to the esophagus is unknown. CASE SUMMARY: A 57-year-old man underwent sequential chemotherapy and radiotherapy for gastric MALT-lymphoma seven years prior to diagnosis of esophageal adenocarcinoma. Esophagectomy without neoadjuvant treatment was recommended by the multidisciplinary tumor board due to early tumor stage [uT1 (sm2) uN+ cM0 according to TNM-classification of malignant tumors, 8th edition] without lymph node involvement. Minimal invasive esophageal resection with esophagogastrostomy was performed. Due to gastric tube necrosis with anastomotic leakage on the twelfth postoperative day, diverting resection with construction of a cervical salivary fistula was necessary. Rapid recovery facilitated colonic interposition without any complications six months afterwards. CONCLUSION: This case report may represent the start for further investigation to know if it is reasonable to refrain from esophagogastrostomy in patients with a long interval between gastric radiotherapy and surgery.

Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications.

The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p > 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC ≥ 0.961; 95% CI: 0.940−0.974), and between the readers was excellent (ICC ≥ 0.879; 95% CI: 0.818−0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology.

Dual-Energy Computed Tomography-Based Quantitative Bone Marrow Imaging in Non-Hematooncological Subjects: Associations with Age, Gender and Other Variables.

Background: Our aim is to assess the utility and associations of quantitative bone marrow attenuation (BMA) values measured on clinical dual-energy computed tomography (DECT) exams in non-hematooncologic subjects with skeletal regions, patient age, gender, and other clinical variables. Methods: Our local ethics committee approved this retrospective image data analysis. Between July 2019 and July 2021, 332 eligible patients (mean age, 64 ± 18 years; female, 135) were identified. Inclusion criteria were the availability of a standardized abdominopelvic DECT data set acquired on the same scanner with identical protocol. Eleven regions-of-interest were placed in the T11-L5 vertebral bodies, dorsal iliac crests, and femur necks. Patient age, gender, weight, clinical, habitual variables, inflammation markers, and anemia were documented in all cases. Results: Multi-regression analyses (all, p < 0.05) identified age as the strongest predictor of lumbar BMA (standardized coefficient: ß = −0.74), followed by CRP (ß = 0.11), LDH (ß = 0.11), and gender (ß = −0.10). In the lower thoracic spine, age was the strongest predictor (ß = −0.58) of BMA, followed by gender (ß = −0.09) and LDH (ß = 0.12). In femoral bones, age was negatively predictive of BMA (ß = −0.12), whereas LDH and anemia were positively predictive (ß = 0.16 both). Heart insufficiency significantly decreased (ß = 0.12, p = 0.034) a BMA value gradient from higher to lower HU values along the vertebrae T11 and L5, whereas age significantly increased this gradient (ß = −0.2, p ≤ 0.001). Conclusions: DECT-based BMA measurements can be obtained from clinical CT exams. BMA values are negatively associated with patient age and influenced by gender, anemia, and inflammatory markers.

Differentiation of adrenal adenomas from adrenal metastases in single-phased staging dual-energy CT and radiomics.

PURPOSE Differentiation of incidental adrenal lesions remains a challenge in diagnostic imaging, especially on single-phase portal venous computed tomography (CT) in the oncological setting. The aim of the study was to explore the ability of dual-energy CT (DECT)-based iodine quantification and virtual non-contrast (VNC) imaging and advanced radiomic analysis of DECT for differentiation of adrenal adenomas from metastases. METHODS A total of 46 patients with 49 adrenal lesions underwent clinically indicated staging DECT and magnetic resonance imaging. Median values of quantitative parameters such as VNC, fat frac- tion, and iodine density in DECT images were collected and compared between adenomas and metastases using non-parametric tests. Magnetic resonance imaging, washout CT, and clinical follow-up were used as a reference standard. Diagnostic accuracy was assessed by calculat- ing receiver operating characteristics. A DECT tumor analysis prototype software was used for semiautomatic segmentation of adrenal lesions and extraction of radiomic features. A radiomics prototype was used to analyze the data with multiple logistic regression and random forest clas- sification to determine the area under the curve (AUC). RESULTS The study cohort (60.87% women; mean age: 66.91 ± 12.93 years) consisted of 32 adenomas and 17 metastases. DECT-based VNC imaging (AUC=0.89) and fat quantification (AUC=0.86) differentiate between adrenal adenomas and metastases with high diagnostic accuracy (P < .001). Analysis of radiomic features revealed that DECT features such as VNC imaging and fat fraction (AUC = 0.87-0.89; < .001) and radiomic features such as 90th percentile and total energy (AUC = 0.88-0.93; P < .001) differentiate with high diagnostic accuracy between adrenal adeno- mas and metastases. Random forest classification revealed an AUC of 0.83 for separating adrenal adenomas from metastases. CONCLUSION Virtual non-contrast imaging and fat quantification as well as extraction of radiomic features accurately differentiate between adrenal adenomas and metastases on single-phase oncologic staging DECT.

AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

(1) To investigate whether interventional cone-beam computed tomography (cbCT) could benefit from AI denoising, particularly with respect to patient body mass index (BMI); (2) From 1 January 2016 to 1 January 2022, 100 patients with liver-directed interventions and peri-procedural cbCT were included. The unenhanced mask run and the contrast-enhanced fill run of the cbCT were reconstructed using weighted filtered back projection. Additionally, each dataset was post-processed using a novel denoising software solution. Place-consistent regions of interest measured signal-to-noise ratio (SNR) per dataset. Corrected mixed-effects analysis with BMI subgroup analyses compared objective image quality. Multiple linear regression measured the contribution of "Radiation Dose", "Body-Mass-Index", and "Mode" to SNR. Two radiologists independently rated diagnostic confidence. Inter-rater agreement was measured using Spearman correlation (r); (3) SNR was significantly higher in the denoised datasets than in the regular datasets (p < 0.001). Furthermore, BMI subgroup analysis showed significant SNR deteriorations in the regular datasets for higher patient BMI (p < 0.001), but stable results for denoising (p > 0.999). In regression, only denoising contributed positively towards SNR (0.6191; 95%CI 0.6096 to 0.6286; p < 0.001). The denoised datasets received overall significantly higher diagnostic confidence grades (p = 0.010), with good inter-rater agreement (r ≥ 0.795, p < 0.001). In a subgroup analysis, diagnostic confidence deteriorated significantly for higher patient BMI (p < 0.001) in the regular datasets but was stable in the denoised datasets (p ≥ 0.103).; (4) AI denoising can significantly enhance image quality in interventional cone-beam CT and effectively mitigate diagnostic confidence deterioration for rising patient BMI.

AI Denoising Significantly Improves Image Quality in Whole-Body Low-Dose Computed Tomography Staging.

(1) Background: To evaluate the effects of an AI-based denoising post-processing software solution in low-dose whole-body computer tomography (WBCT) stagings; (2) Methods: From 1 January 2019 to 1 January 2021, we retrospectively included biometrically matching melanoma patients with clinically indicated WBCT staging from two scanners. The scans were reconstructed using weighted filtered back-projection (wFBP) and Advanced Modeled Iterative Reconstruction strength 2 (ADMIRE 2) at 100% and simulated 50%, 40%, and 30% radiation doses. Each dataset was post-processed using a novel denoising software solution. Five blinded radiologists independently scored subjective image quality twice with 6 weeks between readings. Inter-rater agreement and intra-rater reliability were determined with an intraclass correlation coefficient (ICC). An adequately corrected mixed-effects analysis was used to compare objective and subjective image quality. Multiple linear regression measured the contribution of "Radiation Dose", "Scanner", "Mode", "Rater", and "Timepoint" to image quality. Consistent regions of interest (ROI) measured noise for objective image quality; (3) Results: With good-excellent inter-rater agreement and intra-rater reliability (Timepoint 1: ICC ≥ 0.82, 95% CI 0.74-0.88; Timepoint 2: ICC ≥ 0.86, 95% CI 0.80-0.91; Timepoint 1 vs. 2: ICC ≥ 0.84, 95% CI 0.78-0.90; all p ≤ 0.001), subjective image quality deteriorated significantly below 100% for wFBP and ADMIRE 2 but remained good-excellent for the post-processed images, regardless of input (p ≤ 0.002). In regression analysis, significant increases in subjective image quality were only observed for higher radiation doses (≥0.78, 95%CI 0.63-0.93; p < 0.001), as well as for the post-processed images (≥2.88, 95%CI 2.72-3.03, p < 0.001). All post-processed images had significantly lower image noise than their standard counterparts (p < 0.001), with no differences between the post-processed images themselves. (4) Conclusions: The investigated AI post-processing software solution produces diagnostic images as low as 30% of the initial radiation dose (3.13 ± 0.75 mSv), regardless of scanner type or reconstruction method. Therefore, it might help limit patient radiation exposure, especially in the setting of repeated whole-body staging examinations.

Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening.

PURPOSE: Evaluation of machine learning-based fully automated artery-specific coronary artery calcium (CAC) scoring software, using semi-automated software as a reference. METHODS: A total of 505 patients underwent non-contrast-enhanced calcium scoring computed tomography (CSCT). Automated, machine learning-based software quantified the Agatston score (AS), volume score (VS), and mass score (MS) of each coronary artery [right coronary artery (RCA), left main (LM), circumflex (CX) and left anterior descending (LAD)]. Identified CAC of readers who annotated the data with semi-automated software served as a reference standard. Statistics included comparisons of evaluation time, agreement of identified CAC, and comparisons of the AS, VS, and MS of the reference standard and the fully automated algorithm. RESULTS: The machine learning-based software correlated strongly with the reference standard for the AS, VS, and MS (Spearman's rho > 0.969) (p < 0.001), with excellent agreement (ICC > 0.919) (p < 0.001). The mean assessment time of the reference standard was 59 seconds (IQR 39-140) and that of the automated algorithm was 5.9 seconds (IQR 3.9-16) (p < 0.001). The Bland-Altman plots mean difference and 1.96 upper and lower limits of agreement for all arteries combined were: AS 0.996 (1.33 to 0.74), VS 0.995 (1.40 to 0.71), and MS 0.995 (1.35 to 0.74). The mean bias was minimal: 0.964-1.0429. Risk class assignment showed high accuracy for the AS in total (weighed κ = 0.99) and for each individual artery (κ = 0.96-0.99) with corresponding correct risk group assignment in 497 of 505 patients (98.4 %). CONCLUSION: The fully automated artery-specific coronary calcium scoring algorithm is a time-saving procedure and shows excellent correlation and agreement compared with the clinically established semi-automated approach. KEY POINTS: · Very high correlation and agreement between fully automatic and semi-automatic calcium scoring software.. · Less time-consuming than conventional semi-automatic methods.. · Excellent tool for artery-specific calcium scoring in a clinical setting.. CITATION FORMAT: · Winkelmann MT, Jacoby J, Schwemmer C et al. Fully Automated Artery-Specific Calcium Scoring Based on Machine Learning in Low-Dose Computed Tomography Screening. Fortschr Röntgenstr 2022; 194: 763 - 770.

Interindividual Comparison of Frequency-Selective Nonlinear Blending to Conventional CT for Detection of Focal Liver Lesions Using MRI as the Reference Standard.

BACKGROUND. Diagnosing liver lesions is challenging. CT is used for primary diagnosis, but its contrast resolution is limited. Investigating methods to improve detection of liver lesions is important. OBJECTIVE. The purpose of this study was to evaluate the effect of frequency-selective nonlinear blending on the detectability of liver lesions on CT. METHODS. A retrospective search yielded 109 patients with 356 malignant and benign liver lesions (191 principally diagnosed, 165 incidental findings) who underwent contrast-enhanced CT (CECT) in the portal venous phase and liver MRI between January 2012 and December 2017. Nonlinear blending was applied to CECT examinations, and three blinded readers independently rated the quality (5-point Likert scale) of randomly presented images. Focal lesions (n = 356) were evaluated for lesion identification and categorization to assess sensitivity. For 191 lesions (primary diagnosis), two readers evaluated CECT and nonlinear blending CT to compare lesion size and the accuracy of subjective measurements. A fourth reader performed ROI measurements for calculation of contrast-to-noise ratio (CNR), and a fifth reader reviewed MRI as the standard of reference. Statistics included interobserver agreement, quantitative comparisons of CNR, lesion size, and subjective image analyses of image quality and sensitivity for detecting liver lesions. RESULTS. Three readers rated the image quality of nonlinear blending CT (rating, 4; 10th-90th percentiles, 4-5) higher than that of CECT (rating, 2; 10th-90th percentiles, 1-3) (p < .001). CECT had good interreader agreement (interclass correlation coefficient [ICC], 0.81; 95% CI, 0.76-0.85), as did nonlinear blending CT (ICC, 0.75; 95% CI, 0.69-0.79). The median CNR of liver lesions increased with nonlinear blending (CECT, 4.18 [10th-90th percentiles, 1.67-9.06]; nonlinear blending CT, 12.49 [10th-90th percentiles, 6.18-23.39]; p < .001). Bland-Altman analysis of lesion size showed a reduction in underestimation from 2.5 (SD, 9.2) mm (95% CI, 1.2-3.9 mm) with CECT to 0.1 (SD, 3.9) mm (95% CI, -0.68 to 0.46 mm) for nonlinear blending CT (concordance correlation coefficient, 0.99). Sensitivity for detecting liver lesions increased to 86% for nonlinear blending CT. The sensitivity of CECT was 76%. CONCLUSION. Frequency-selective nonlinear blending in CECT increases image quality and CNR, increases the precision of size measurement, and increases sensitivity for detecting liver lesions. CLINICAL IMPACT. Use of nonlinear blending CT improves liver lesion detection and increases the accuracy of lesion size measurement, which is important when local ablation or liver transplant is being considered.

Quantification of liver and muscular fat using contrast-enhanced Dual Source Dual Energy Computed Tomography compared to an established multi-echo Dixon MRI sequence.

PURPOSE: To investigate the feasibility of liver fat quantification in contrast-enhanced dual source dual energy computed tomography (DECT) using multi-echo Dixon magnetic resonance imaging (MRI) as reference standard. METHOD: Patients who underwent MRI of the liver including a multi-echo Dixon sequence for estimation of proton density fat fraction in 2017 as well as contrast-enhanced DECT imaging of the abdomen were included in this retrospective, monocentric IRB approved study. Furthermore, patients with a hepatic fat amount >5% who were examined in 2018 with MRI and DECT were included. The final study group consisted of 81 patients with 90 pairs of examinations. Analysis of parameter maps was performed manually using congruent regions of interest which were placed in the liver parenchyma, in the erector spinae muscles, and psoas major muscles. RESULTS: Mean patient age was 61 ± 13 years. Median time between MRI and DECT was 48 days. MRI liver fat quantification resulted in a median of 3.8% (IQR: 2.2-8.2%) compared to 1.8% (IQR: 0-6.3%) in DECT (p < 0.001), with a Spearman correlation of 0.73. Bland-Altman analysis resulted in a systematic underestimation of liver fat in DECT, with a mean difference of -1.7%. Fat quantification in the erector spinae muscles (p = 0.257) and the psoas major muscles (p = 0.208) was not significantly different in DECT compared to MRI. CONCLUSIONS: Liver and muscular fat quantification in portal-venous phase DECT is feasible with good to excellent correlation compared to a multi-echo Dixon MRI sequence analysis. While there is an underestimation of the liver fat content in DECT, there are no significant differences between DECT and MRI fat quantification of the erector spinae and psoas major muscles.

Simulated Radiation Dose Reduction in Whole-Body CT on a 3rd Generation Dual-Source Scanner: An Intraindividual Comparison.

To evaluate the effect of radiation dose reduction on image quality and diagnostic confidence in contrast-enhanced whole-body computed tomography (WBCT) staging. We randomly selected March 2016 for retrospective inclusion of 18 consecutive patients (14 female, 60 ± 15 years) with clinically indicated WBCT staging on the same 3rd generation dual-source CT. Using low-dose simulations, we created data sets with 100, 80, 60, 40, and 20% of the original radiation dose. Each set was reconstructed using filtered back projection (FBP) and Advanced Modeled Iterative Reconstruction (ADMIRE®, Siemens Healthineers, Forchheim, Germany) strength 1-5, resulting in 540 datasets total. ADMIRE 2 was the reference standard for intraindividual comparison. The effective radiation dose was calculated using commercially available software. For comparison of objective image quality, noise assessments of subcutaneous adipose tissue regions were performed automatically using the software. Three radiologists blinded to the study evaluated image quality and diagnostic confidence independently on an equidistant 5-point Likert scale (1 = poor to 5 = excellent). At 100%, the effective radiation dose in our population was 13.3 ± 9.1 mSv. At 20% radiation dose, it was possible to obtain comparably low noise levels when using ADMIRE 5 (p = 1.000, r = 0.29). We identified ADMIRE 3 at 40% radiation dose (5.3 ± 3.6 mSv) as the lowest achievable radiation dose with image quality and diagnostic confidence equal to our reference standard (p = 1.000, r > 0.4). The inter-rater agreement for this result was almost perfect (ICC ≥ 0.958, 95% CI 0.909-0.983). On a 3rd generation scanner, it is feasible to maintain good subjective image quality, diagnostic confidence, and image noise in single-energy WBCT staging at dose levels as low as 40% of the original dose (5.3 ± 3.6 mSv), when using ADMIRE 3.

Mechanical circulatory support for cardiovascular complications in a young COVID-19 patient.

BACKGROUND: The current coronavirus (COVID-19) pandemic is associated with severe pulmonary and cardiovascular complications. CASE PRESENTATION: This report describes a young patient with COVID-19 without any comorbidity presenting with severe cardiovascular complications, manifesting with pulmonary embolism, embolic stroke, and right heart failure. CONCLUSION: Management with short-term mechanical circulatory support, including different cannulation strategies, resulted in a successful outcome despite his critical cardiovascular status.

Development of a multivariable prediction model for patient-adjusted aortic risk morphology.

OBJECTIVES: Preventing type A aortic dissection requires reliable prediction. We developed and validated a multivariable prediction model based on anthropometry to define patient-adjusted thresholds for aortic diameter and length. METHODS: We analysed computed tomography angiographies and clinical data from 510 control patients, 143 subjects for model validation, 125 individuals with ascending aorta ectasia (45-54 mm), 58 patients with aneurysm (≥55 mm), 206 patients with type A aortic dissection and 19 patients who had received a computed tomography angiography ≤2 years before they suffered from a type A aortic dissection. Computed tomography angiographies were analysed using curved planar reformations. RESULTS: In the control group, the mean ascending aortic diameter was 33.8 mm [standard deviation (SD) ±5.2 mm], and the length, measured from the aortic valve to the brachiocephalic trunk, was 91.9 mm (SD ±12.2 mm); both diameter and length were correlated with anthropometric parameters and were smaller than the respective values in all pathological groups (P < 0.001). Multivariable linear regression analysis of the control group revealed that age, sex and body surface area were predictors of ascending aorta diameter (R2 = 0.40) and length (R2 = 0.26). Bicuspidity of the aortic valve was not included in the model; its prevalence was only 3.2% in the control group but >25% in the ectasia and aneurysm groups. CONCLUSIONS: The regression model provides a patient-adjusted prediction of the thresholds for aortic diameter and length. In our retrospective data, the model resulted in better identification of aortas at the risk of dissection than the conventional 55-mm diameter threshold. The model is available as an Internet calculator (www.aorticcalculator.com).

Platelet GPIbα is a mediator and potential interventional target for NASH and subsequent liver cancer.

Non-alcoholic fatty liver disease ranges from steatosis to non-alcoholic steatohepatitis (NASH), potentially progressing to cirrhosis and hepatocellular carcinoma (HCC). Here, we show that platelet number, platelet activation and platelet aggregation are increased in NASH but not in steatosis or insulin resistance. Antiplatelet therapy (APT; aspirin/clopidogrel, ticagrelor) but not nonsteroidal anti-inflammatory drug (NSAID) treatment with sulindac prevented NASH and subsequent HCC development. Intravital microscopy showed that liver colonization by platelets depended primarily on Kupffer cells at early and late stages of NASH, involving hyaluronan-CD44 binding. APT reduced intrahepatic platelet accumulation and the frequency of platelet-immune cell interaction, thereby limiting hepatic immune cell trafficking. Consequently, intrahepatic cytokine and chemokine release, macrovesicular steatosis and liver damage were attenuated. Platelet cargo, platelet adhesion and platelet activation but not platelet aggregation were identified as pivotal for NASH and subsequent hepatocarcinogenesis. In particular, platelet-derived GPIbα proved critical for development of NASH and subsequent HCC, independent of its reported cognate ligands vWF, P-selectin or Mac-1, offering a potential target against NASH.

Detectability of Brain Metastases by Using Frequency-Selective Nonlinear Blending in Contrast-Enhanced Computed Tomography.

PURPOSE: The aim of this study to evaluate the role of frequency-selective nonlinear blending (FS-NLB) for the detectability of brain metastases with contrast-enhanced computed tomography (CECT) using magnetic resonance imaging (MRI) as standard of reference. MATERIALS AND METHODS: A retrospective patient data search at our institution yielded 91 patients who underwent both brain CECT and MRI for screening of brain metastases (n = 173) between 2014 and 2016 (mean time interval, 29 ± 37 [malignant: 15 ± 16/benign: 42 ± 47] days). A recently introduced FS-NLB postprocessing technique was applied to CECT images. Two readers interpreted all CT images in an independent fashion. The conventional, linear blending (LB) CT images were evaluated first. After a washout period, the same readers evaluated the FS-NLB CT images. The standard of reference was established by a consensus interpretation of the brain MRI studies. Outcome variables included determination of best performing FS-NLB settings, region of interest (ROI)-based calculation of contrast-to-noise ratios (CNRs), size, and number of brain metastases. Based on the number of metastases, we classified patients in 5 therapeutically relevant categories (0, no metastasis; 1, singular metastasis; 2, less than 4 metastases; 3, >4 and <10 metastases; 4, >10 metastases). Statistical comparison and diagnostic performance tests were applied. RESULTS: A center of 47 Hounsfield units (HU), delta of 5 HU, and slope of 5 resulted in the best delineation of hyperdense brain metastases, whereas for hypodense brain metastases, a center of 32 HU, delta of 5 HU, and slope of 5 showed best delineation. Frequency-selective nonlinear blending significantly increased CNR in hyperdense cerebral metastases (CECT: 9.11 [6.9-10.9], FS-NLB: 18.1 [11.9-22.8]; P < 0.0001) and hypodense cerebral metastases (CECT: 6.3 [5.2-8], FS-NLB: 17.8 [14.5-19.7]; P < 0.0001). Sensitivity, specificity, negative predictive values, positive predictive values, and accuracy for LB, and FS-NLB were 40%, 98%, 99%, 31%, and 52%, and 62%, 94%, 97%, 40%, and 69%, respectively. Magnetic resonance imaging, LB, and FS-NLB classification of metastatic patients were group 0 (47, 47, 46), group 1 (14, 8, 11), group 2 (16, 12, 15), group 3 (8, 7, 8), and group 4 (6, 4, 6). CONCLUSIONS: Frequency-selective nonlinear blending postprocessing of CECT significantly increases the detection of brain metastases over conventional CECT; however, the sensitivity remains lower than MRI. Frequency-selective nonlinear blending is slightly inferior in the categorization of patients into therapeutically relevant groups, when compared with MRI.