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Objectives: Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification., Material and Methods: Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used., Results: In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0]., Conclusion: For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable., Competing Interests: The authors of this manuscript have the following competing interests: GM.C. is employed as a clinician scientist by Philips Health Care Germany. This does not alter our adherence to PLOS ONE policies on sharing data and materials. All other authors do not declare any competing interests. There are no patents, products in development or marketed products associated with this research to declare., (Copyright: © 2024 Molwitz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: To identify magnetic resonance (MR) angiography derived predictors of progressive dilatation and surgery of the aortic root in Marfan syndrome., Material and Methods: We retrospectively included 111 patients (32.7±16.5 years, range: 7-75 years) with a total of 446 MR angiographies. Aortic diameter growth rates of the entire thoracic aorta and Z-scores were estimated from annual diameter measurements. Aortic root shape was subdivided into three different types: (T0) normal; (T1) localized dilatation; (T2) generalized aortic root dilatation. Aortic diameter, Z-score, age, and aortic root shape at baseline were tested as predictors of aortic root dilatation using a multivariate logistic regression model., Results: The highest aortic growth rate was observed at the level of the sinuses of Valsalva. Higher aortic root diameters and Z-scores at baseline predicted an increased growth of the aortic root (p = 0.003 and p<0.001). Young age (<30 years) was a predictor for the increase of Z-scores when compared to patients ≥30 years (p = 0.019). 25/111 patients (22.5%) had a T0 aortic root shape, 59/111 patients (53.2%) had a T1 aortic root shape, and 27/111 patients (24.3%) had a T2 aortic root shape. Aortic root shape did not predict further aortic growth (p>0.05). However, significantly more patients undergoing surgery had a generalized aortic dilatation (19/28, 76.9%) than a localized aortic root dilatation (9/28, 32.1%) (p = 0.001)., Conclusion: Larger baseline aortic root diameter and Z-score as well as young age predict solely progressive aortic root dilatation in Marfan patients. MR angiography derived type of aortic root shape does not predict aortic growth, but patients with generalized aortic root dilatation are referred more frequently for aortic surgery., Competing Interests: The authors have declared that no competing interests exist.

Objectives: The aim of this study was to investigate possible patterns of demand for chest imaging during the first wave of the SARS-CoV-2 pandemic and derive a decision aid for the allocation of resources in future pandemic challenges., Materials and Methods: Time data of requests for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) lung disease were analyzed between February 27th and May 27th 2020. A multinomial logistic regression model was used to evaluate differences in the number of requests between 3 time intervals (I1: 6am - 2pm, I2: 2pm - 10pm, I3: 10pm - 6am). A cosinor model was applied to investigate the demand per hour. Requests per day were compared to the number of regional COVID-19 cases., Results: 551 COVID-19 related chest imagings (32.8% outpatients, 67.2% in-patients) of 243 patients were conducted (33.3% female, 66.7% male, mean age 60 ± 17 years). Most exams for outpatients were required during I2 (I1 vs. I2: odds ratio (OR) = 0.73, 95% confidence interval (CI) 0.62-0.86, p = 0.01; I2 vs. I3: OR = 1.24, 95% CI 1.04-1.48, p = 0.03) with an acrophase at 7:29 pm. Requests for in-patients decreased from I1 to I3 (I1 vs. I2: OR = 1.24, 95% CI 1.09-1.41, p = 0.01; I2 vs. I3: OR = 1.16, 95% CI 1.05-1.28, p = 0.01) with an acrophase at 12:51 pm. The number of requests per day for outpatients developed similarly to regional cases while demand for in-patients increased later and persisted longer., Conclusions: The demand for COVID-19 related chest imaging displayed distinct distribution patterns depending on the sector of patient care and point of time during the SARS-CoV-2 pandemic. These patterns should be considered in the allocation of resources in future pandemic challenges with similar disease characteristics., Competing Interests: The authors have declared that no competing interests exist.