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We aimed to explore the feasibility of 4D flow magnetic resonance imaging (MRI) for patients undergoing thoracic aorta endovascular repair (TEVAR). We retrospectively evaluated ten patients (two female), with a mean (±standard deviation) age of 61 ± 20 years, undergoing MRI for a follow-up after TEVAR. All 4D flow examinations were performed using a 1.5-T system (MAGNETOM Aera, Siemens Healthcare, Erlangen, Germany). In addition to the standard examination protocol, a 4D flow-sensitive 3D spatial-encoding, time-resolved, phase-contrast prototype sequence was acquired. Among our cases, flow evaluation was feasible in all patients, although we observed some artifacts in 3 out of 10 patients. Three individuals displayed a reduced signal within the vessel lumen where the endograft was placed, while others presented with turbulent or increased flow. An aortic endograft did not necessarily hinder the visualization of blood flow through 4D flow sequences, although the graft could generate flow artifacts in some cases. A 4D Flow MRI may represent the ideal tool to follow up on both healthy subjects deemed to be at an increased risk based on their anatomical characteristics or patients submitted to TEVAR for whom a surveillance protocol with computed tomography angiography would be cumbersome and unjustified.

Objective Breast arterial calcifications (BAC) are a sex-specific cardiovascular disease biomarker that might improve cardiovascular risk stratification in women. We implemented a deep convolutional neural network for automatic BAC detection and quantification. Methods In this retrospective study, four readers labelled four-view mammograms as BAC positive (BAC+) or BAC negative (BAC−) at image level. Starting from a pretrained VGG16 model, we trained a convolutional neural network to discriminate BAC+ and BAC− mammograms. Accuracy, F1 score, and area under the receiver operating characteristic curve (AUC-ROC) were used to assess the diagnostic performance. Predictions of calcified areas were generated using the generalized gradient-weighted class activation mapping (Grad-CAM++) method, and their correlation with manual measurement of BAC length in a subset of cases was assessed using Spearman ρ. Results A total 1493 women (198 BAC+) with a median age of 59 years (interquartile range 52–68) were included and partitioned in a training set of 410 cases (1640 views, 398 BAC+), validation set of 222 cases (888 views, 89 BAC+), and test set of 229 cases (916 views, 94 BAC+). The accuracy, F1 score, and AUC-ROC were 0.94, 0.86, and 0.98 in the training set; 0.96, 0.74, and 0.96 in the validation set; and 0.97, 0.80, and 0.95 in the test set, respectively. In 112 analyzed views, the Grad-CAM++ predictions displayed a strong correlation with BAC measured length (ρ = 0.88, p Conclusion Our model showed promising performances in BAC detection and in quantification of BAC burden, showing a strong correlation with manual measurements. Clinical relevance statement Integrating our model to clinical practice could improve BAC reporting without increasing clinical workload, facilitating large-scale studies on the impact of BAC as a biomarker of cardiovascular risk, raising awareness on women’s cardiovascular health, and leveraging mammographic screening. Key Points • We implemented a deep convolutional neural network (CNN) for BAC detection and quantification. • Our CNN had an area under the receiving operator curve of 0.95 for BAC detection in the test set composed of 916 views, 94 of which were BAC+ . • Furthermore, our CNN showed a strong correlation with manual BAC measurements (ρ = 0.88) in a set of 112 views.

Background We investigated the radiodensity of epicardial (EAT), subcutaneous (SAT), and visceral adipose tissue (VAT) before and after treatment with anthracyclines in a population of breast cancer (BC) patients, and in controls not treated with anthracyclines, to detect a potential role of EAT density as a biomarker of changes related to chemotherapy cardiotoxicity. Methods We reviewed BC patients treated with anthracyclines who underwent CT before (CT-t0) and after (CT-t1) chemotherapy, and age- and sex-matched controls who underwent two CT examinations at comparable intervals. On non-contrast scans, EAT was segmented contouring the pericardium and thresholding between -190 and -30 Hounsfield units (HU), and SAT and VAT were segmented with two 15-mm diameter regions of interest thresholded between -195 and -45 HU. Results Thirty-two female patients and 32 controls were included. There were no differences in age (p = 0.439) and follow-up duration (p = 0.162) between patients and controls. Between CT-t0 and CT-t1, EAT density decreased in BC patients (-66 HU, interquartile range [IQR] -71 to -63 HU, to -71 HU, IQR -75 to -66 HU, p = 0.003), while it did not vary in controls (p = 0.955). SAT density increased from CT-t0 to CT-t1 in BC patients (-107 HU, IQR -111 to -105 HU, to -105 HU, IQR -110 to -100 HU, p = 0.014), whereas it did not change in controls (p = 0.477). VAT density did not vary in either BC patients (p = 0.911) or controls (p = 0.627). Conclusions EAT density appears to be influenced by anthracycline treatment for BC, well known for its cardiotoxicity, shifting towards lower values indicative of a less active metabolism.

Background We applied an artificial intelligence-based model to predict fragility fractures in postmenopausal women, using different dual-energy x-ray absorptiometry (DXA) parameters. Methods One hundred seventy-four postmenopausal women without vertebral fractures (VFs) at baseline (mean age 66.3 ± 9.8) were retrospectively evaluated. Data has been collected from September 2010 to August 2018. All subjects performed a spine x-ray to assess VFs, together with lumbar and femoral DXA for bone mineral density (BMD) and the bone strain index (BSI) evaluation. Follow-up exams were performed after 3.34 ± 1.91 years. Considering the occurrence of new VFs at follow-up, two groups were created: fractured versus not-fractured. We applied an artificial neural network (ANN) analysis with a predictive tool (TWIST system) to select relevant input data from a list of 13 variables including BMD and BSI. A semantic connectivity map was built to analyse the connections among variables within the groups. For group comparisons, an independent-samples t-test was used; variables were expressed as mean ± standard deviation. Results For each patient, we evaluated a total of n = 6 exams. At follow-up, n = 69 (39.6%) women developed a VF. ANNs reached a predictive accuracy of 79.56% within the training testing procedure, with a sensitivity of 80.93% and a specificity of 78.18%. The semantic connectivity map showed that a low BSI at the total femur is connected to the absence of VFs. Conclusion We found a high performance of ANN analysis in predicting the occurrence of VFs. Femoral BSI appears as a useful DXA index to identify patients at lower risk for lumbar VFs.

The purpose of this study was to evaluate the level of agreement between flow/velocity data obtained from 2D-phase-contrast (PC) and 4D-flow in patients scheduled for treatment of carotid artery stenosis. Image acquisition was performed using a 1.5 T scanner. We compared mean flow rates, vessel areas, and peak velocities obtained during the acquisition with both techniques in 20 consecutive patients, 15 males and 5 females aged 69 ± 5 years (mean ± standard deviation). There was a good correlation between both techniques for the CCA flow (r = 0.65, p &lt, 0.001), whereas for the ICA flow and ECA flow the correlation was only moderate (r = 0.4, p = 0.011 and r = 0.45, p = 0.003, respectively). Correlations of peak velocities between methods were good for CCA (r = 0.56, p &lt, 0.001) and moderate for ECA (r = 0.41, p = 0.008). There was no correlation for ICA (r = 0.04, p = 0.805). Cross-sectional area values between methods showed no significant correlations for CCA (r = 0.18, p = 0.269), ICA (r = 0.1, p = 0.543), and ECA (r = 0.05, p = 0.767). Conclusion: the 4D-flow imaging provided a good correlation of CCA and a moderate correlation of ICA flow rates against 2D-PC, underestimating peak velocities and overestimating cross-sectional areas in all carotid segments.

Sarcopenia has been associated with an increased incidence of adverse outcomes, including higher mortality, after endovascular aortic repair (EVAR). We aim to use computed tomography (CT) to quantify changes in total psoas muscles area (PMA) and psoas muscle density (PMD) after EVAR, and to evaluate the reproducibility of both measurements. PMA and PMD were assessed via manual segmentation of the psoas muscle on pre- and post-operative CT scans belonging to consecutive patients who underwent EVAR. Wilcoxon test was used to compare PMA and PMD before and after EVAR, and inter- and intra-reader agreements of both methods were evaluated through Bland–Altman analysis. A total of 50 patients, 42 of them males (84%), were included in the study. PMA changes from 1243 mm2 (1006–1445 mm2) to 1102 mm2 (IQR 937–1331 mm2), after EVAR (p < 0.001). PMD did not vary between pre-EVAR (33 HU, IQR 26.5–38.7 HU) and post-EVAR (32 HU, IQR 26–37 HU, p = 0.630). At inter-reader Bland–Altman analysis, PMA showed a bias of 64.0 mm2 and a coefficient of repeatability (CoR) of 359.2 mm2, whereas PMD showed a bias of −2.43 HU and a CoR of 6.19 HU. At intra-reader Bland–Altman analysis, PMA showed a bias of −81.1 mm2 and a CoR of 394.6 mm2, whereas PMD showed a bias of 1.41 HU and a CoR of 6.36 HU. In conclusion, PMA decreases after EVAR. A good intra and inter-reader reproducibility was observed for both PMA and PMD. We thus propose to use PMA during the follow-up of patients who underwent EVAR to monitor muscle depletion after surgery.

Breast density (BD) and breast arterial calcifications (BAC) can expand the role of mammography. In premenopause, BD is related to body fat composition: breast adipose tissue and total volume are potential indicators of fat storage in visceral depots, associated with higher risk of cardiovascular disease (CVD). Women with fatty breast have an increased likelihood of hypercholesterolemia. Women without cardiometabolic diseases with higher BD have a lower risk of diabetes mellitus, hypertension, chest pain, and peripheral vascular disease, while those with lower BD are at increased risk of cardiometabolic diseases. BAC, the expression of Monckeberg sclerosis, are associated with CVD risk. Their prevalence, 13 % overall, rises after menopause and is reduced in women aged over 65 receiving hormonal replacement therapy. Due to their distinct pathogenesis, BAC are associated with hypertension but not with other cardiovascular risk factors. Women with BAC have an increased risk of acute myocardial infarction, ischemic stroke, and CVD death; furthermore, moderate to severe BAC load is associated with coronary artery disease. The clinical use of BAC assessment is limited by their time-consuming manual/visual quantification, an issue possibly solved by artificial intelligence-based approaches addressing BAC complex topology as well as their large spectrum of extent and x-ray attenuations. A link between BD, BAC, and osteoporosis has been reported, but data are still inconclusive. Systematic, standardised reporting of BD and BAC should be encouraged.

PURPOSE: To compare real-time compressed sensing (CS) and standard balanced steady-state free precession (bSSFP) cardiac cine imaging in children. MATERIALS AND METHODS: Twenty children (mean age, 15 years ± 5 [SD], range, 7–21 years; 10 male participants) with biventricular congenital heart disease (n = 11) or cardiomyopathy (n = 9) were prospectively included. Examinations were performed with 1.5-T imagers by using both bSSFP and CS sequences in all participants. Quantification of ventricular volumes and function was performed for all images by two readers blinded to patient diagnosis and type of sequence. Values were correlated with phase-contrast flow measurements by one reader. Intra- and interreader agreement were analyzed. RESULTS: There were no significant differences between ventricular parameters measured on CS compared with those of bSSFP (P > .05) for reader 1. Only ejection fraction showed a significant difference (P = .02) for reader 2. Intrareader agreement was considerable for both sequences (bSSFP: mean difference range, +1 to −2.6; maximum CI, +7.9, −13; bias range, 0.1%–4.1%; intraclass correlation coefficient [ICC] range, 0.931–0.997. CS: mean difference range, +7.4 to −5.6; maximum CI, +37.2, −48.8; bias range, 0.5%–7.5%; ICC range, 0.717–0.997). Interreader agreement was acceptable but less robust, especially for CS (bSSFP: mean difference range, +2.6 to −5.6; maximum CI, +60.7, −65.3; bias range, 1.6%–6.2%; ICC range, 0.726–0.951. CS: mean difference range, +10.7 to −9.1; maximum CI, +87.5, −84.6; bias range, 1.1%–17.3%; ICC range, 0.509–0.849). The mean acquisition time was shorter for CS (20 seconds; range, 17–25 seconds) compared with that for bSSFP (160 seconds; range, 130–190 seconds) (P < .001). CONCLUSION: CS cardiac cine imaging provided equivalent ventricular volume and function measurements with shorter acquisition times compared with those of bSSFP and may prove suitable for the pediatric population. Keywords: Compressed Sensing, Balanced Steady-State Free Precession, Cine Imaging, Cardiovascular MRI, Pediatrics, Cardiac, Heart, Cardiomyopathies, Congenital, Segmentation © RSNA, 2022

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022

Objectives To investigate the knowledge of radiologists on breast arterial calcifications (BAC) and attitude about BAC reporting, communication to women, and subsequent action. Methods An online survey was offered to EUSOBI members, with 17 questions focused on demographics, level of experience, clinical setting, awareness of BAC association with cardiovascular risk, mammographic reporting, modality of BAC assessment, and action habits. Descriptive statistics were used. Results Among 1084 EUSOBI members, 378 (34.9%) responded to the survey, 361/378 (95.5%) radiologists, 263 females (69.6%), 112 males (29.6%), and 3 (0.8%) who did not specify their gender. Of 378 respondents, 305 (80.7%) declared to be aware of BAC meaning in terms of cardiovascular risk and 234 (61.9%) to routinely include BAC in mammogram reports, when detected. Excluding one inconsistent answer, simple annotation of BAC presence was declared by 151/233 (64.8%), distinction between low versus extensive BAC burden by 59/233 (25.3%), and usage of an ordinal scale by 22/233 (9.5%) and of a cardinal scale by 1/233 (0.4%). Among these 233 radiologists reporting BAC, 106 (45.5%) declared to orally inform the woman and, in case of severe BAC burden, 103 (44.2%) to investigate cardiovascular history, and 92 (39.5%) to refer the woman to a cardiologist. Conclusion Among EUSOBI respondents, over 80% declared to be aware of BAC cardiovascular meaning and over 60% to include BAC in the report. Qualitative BAC assessment predominates. About 40% of respondents who report on BAC, in the case of severe BAC burden, investigate cardiovascular history and/or refer the woman to a cardiologist. Key Points • Of 1084 EUSOBI members, 378 (35%) participated: 81% of respondents are aware of breast arterial calcification (BAC) cardiovascular meaning and 62% include BAC in the mammogram report. • Of those reporting BAC, description of presence was declared by 65%, low versus extensive burden distinction by 25%, usage of an ordinal scale by 10%, and of a cardinal scale by 0.4%; 46% inform the woman and, in case of severe BAC burden, 44% examine cardiovascular history, and 40% refer her to a cardiologist. • European breast radiologists may be ready for large-scale studies to ascertain the role of BAC assessment in the comprehensive framework of female cardiovascular disease prevention.

The “bovine” aortic arch is an anatomic variant consisting in a common origin of the innominate and left carotid artery (CILCA), associated with a greater risk of thoracic aortic diseases (aneurysms and dissections), stroke, and complications after endovascular procedures. CILCA can be detected by visual assessment of computed tomography (CT) chest scans, but it is rarely reported. We developed a deep learning (DL) segmentation-plus-classification system to automatically detect CILCA based on 302 CT studies acquired at 2 centers. One model (3D U-Net) was trained from scratch (supervised by manual segmentation), validated, and tested for the automatic segmentation of the aortic arch and supra-aortic vessels. Three DL architectures (ResNet50, DenseNet-201, and SqueezeNet), pre-trained over millions of common images, were trained, validated, and tested for the automatic classification of CILCA versus non-CILCA, supervised by radiologist’s classification. The 3D U-Net-plus-DenseNet-201 was found to be the best system (Dice index 0.912); its classification performance obtained from internal, independent testing on 126 patients gave a receiver operating characteristic area under the curve of 87.0%, sensitivity 66.7%, specificity 90.5%, positive predictive value 87.5%, negative predictive value 73.1%, positive likelihood ratio 7.0, and negative likelihood ratio 0.4. In conclusion, a combined DL system applied to chest CT scans was developed and proven to be an effective tool to detect individuals with “bovine” aortic arch with a low rate of false-positive findings.

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Adipose tissue, in particular epicardial adipose tissue, has been identified as a potential biomarker of cardiovascular pathologies such as coronary artery disease (CAD) in the light of its metabolic activity and close anatomic and pathophysiologic relationship to the heart. Our purpose was to evaluate epicardial adipose tissue density at both unenhanced and contrast-enhanced computed tomography (CT), along with CT densities of paracardiac and subcutaneous adipose tissue, as well as the relations of such densities with CAD. We retrospectively reviewed patients who underwent cardiac CT at our institution for CAD assessment. We segmented regions of interest on epicardial, paracardiac, and subcutaneous adipose tissue on unenhanced and contrast-enhanced scans. A total of 480 patients were included, 164 of them presenting with CAD. Median epicardial adipose tissue density measured on contrast-enhanced scans (−81.5 HU, interquartile range −84.9 to −78.0) was higher than that measured on unenhanced scans (−73.4 HU, −76.9 to −69.4) (p &lt, 0.001), whereas paracardiac and subcutaneous adipose tissue densities were not (p ≥ 0.055). Patients with or without CAD, did not show significant differences in density of epicardial, paracardiac, and subcutaneous adipose tissue either on unenhanced or contrast-enhanced scans (p ≥ 0.092). CAD patients may experience different phenomena (inflammation, fibrosis, increase in adipose depots) leading to rises or drops in epicardial adipose tissue density, resulting in variations that are difficult to detect.

Objectives To assess changes in working patterns and education experienced by radiology residents in Northwest Italy during the COVID-19 pandemic. Methods An online questionnaire was sent to residents of 9 postgraduate schools in Lombardy and Piedmont, investigating demographics, changes in radiological workload, involvement in COVID-19-related activities, research, distance learning, COVID-19 contacts and infection, changes in training profile, and impact on psychological wellbeing. Descriptive and χ2 statistics were used. Results Among 373 residents invited, 300 (80%) participated. Between March and April 2020, 44% (133/300) of respondents dedicated their full time to radiology; 41% (124/300) engaged in COVID-19-related activities, 73% (90/124) of whom working in COVID-19 wards; 40% (121/300) dedicated > 25% of time to distance learning; and 66% (199/300) were more involved in research activities than before the pandemic. Over half of residents (57%, 171/300) had contacts with COVID-19-positive subjects, 5% (14/300) were infected, and 8% (23/300) lost a loved one due to COVID-19. Only 1% (3/300) of residents stated that, given the implications of this pandemic scenario, they would not have chosen radiology as their specialty, whereas 7% (22/300) would change their subspecialty. The most common concerns were spreading the infection to their loved ones (30%, 91/300), and becoming sick (7%, 21/300). Positive changes were also noted, such as being more willing to cooperate with other colleagues (36%, 109/300). Conclusions The COVID-19 pandemic changed radiology residents’ training programmes, with distance learning, engaging in COVID-19-related activities, and a greater involvement in research becoming part of their everyday practice. Key Points • Of 300 participants, 44% were fully dedicated to radiological activity and 41% devoted time to COVID-19-related activities, 73% of whom to COVID-19 wards. • Distance learning was substantial for 40% of residents, and 66% were involved in research activities more than before the COVID-19 pandemic. • Over half of residents were exposed to COVID-19 contacts and less than one in twenty was infected.

BACKGROUND: Breast arterial calcifications (BAC), representing Mönckeberg’s sclerosis of the tunica media of breast arteries, are an imaging biomarker for cardiovascular risk stratification in the female population. Our aim was to estimate the intra- and inter-reader reproducibility of a semiquantitative score for BAC assessment (BAC-SS). METHODS: Consecutive women who underwent screening mammography at our center from January 1(st) to January 31(st), 2018 were retrieved and included according to BAC presence. Two readers (R1 and R2) independently applied the BAC-SS to medio-lateral oblique views, obtaining a BAC score by summing: (I) number of calcified vessels (from 0 to n); (II) vessel opacification, i.e., the degree of artery coverage by calcium bright pixels (0 or 1); and (III) length class of calcified vessels (from 0 to 4). R1 repeated the assessment 2 weeks later. Scoring time was recorded. Cohen’s κ statistics and Bland-Altman analysis were used. RESULTS: Among 408 women, 57 (14%) had BAC; 114 medio-lateral oblique views were assessed. Median BAC score was 4 [interquartile range (IQR): 3–6] for R1 and 4 (IQR: 2–6) for R2 (P=0.417) while median scoring time was 156 s (IQR: 99–314 s) for R1 and 191 s (IQR: 137–292 s) for R2 (P=0.743). Bland-Altman analysis showed a 77% intra-reader reproducibility [bias: 0.193, coefficient of repeatability (CoR): 0.955] and a 64% inter-reader reproducibility (bias: 0.211, CoR: 1.516). Cohen’s κ for BAC presence was 0.968 for intra-reader agreement and 0.937 for inter-reader agreement. CONCLUSIONS: Our BAC-SS has a good intra- and inter-reader reproducibility, within acceptable scoring times. A large-scale study is warranted to test its ability to stratify cardiovascular risk in women.

Objectives We aimed to assess extracellular volume (ECV) through non-gated, contrast-enhanced computed tomography (CT) before and after radiation therapy (RT) in patients with esophageal cancer (EC). Materials and methods EC patients who had undergone CT before and after RT were retrospectively assessed. Patients with preexisting cardiovascular disease or with heavily artifacted CT were excluded. ECV was calculated using density values for the myocardial septum and blood pool. Data were reported as mean and standard deviation or median and interquartile range according to their distribution; t test or Wilcoxon and Pearson r or Spearman ρ were subsequently used. Results Twenty-one patients with stage ≥ IB EC, aged 64 ± 18 years, were included. Mean and maximum RT doses were 21.2 Gy (16.9–24.1) and 42.5 Gy (41.8–49.2), respectively. At baseline (n = 21), hematocrit was 39% ± 4%, ECV 27.9% ± 3.5%; 35 days (30–38) after RT (n = 20), hematocrit was 36% ± 4%, lower than at baseline (p = 0.002), ECV 30.3% ± 8.3%, higher than at baseline (p = 0.081); at follow-up 420 days (244–624) after RT (n = 13), hematocrit was 36% ± 5%, lower than at baseline (p = 0.030), ECV 31.4% ± 4.5%, higher than at baseline (p = 0.011). No patients showed signs of overt cardiotoxicity. ECV early after RT was moderately positively correlated with maximum RT dose (ρ = 0.50, p = 0.036). Conclusions In EC patients, CT-derived myocardial ECV was increased after RT and may thus appear as a potential early biomarker of cardiotoxicity.

In this paper the authors present the results of a dosimetric analysis related to the exposure of live-line workers to the magnetic fields generated by high voltage overhead lines and substations. The study extends the work published by Dawson et al. in 2002, considering more evolved anatomical models nowadays available, the new reference limits given by the 2013/35/EU Directive, and a new methodology, based on the intercomparison of two alternative solvers and the use of data filtering. Moreover, additional exposure scenarios are here considered with respect to the studies already available in literature. The results show that for the exposure scenario of high voltage live line works with bare hand method, in any analyzed position, the exposure limits for the tissues of the central nervous system, as well as for all other tissues, are never exceeded, despite in some cases the action levels are exceeded. For the exposure of workers in substations near 220 kV and 380 kV line trap coils exposure is compliant with the regulatory limits if the current flowing through the line trap does not exceed the value of 1000 A. Finally, for the exposure of workers in substations near cable connections, electric field values induced in the body are always lower than regulatory limits with a phase current value equal to 1600 A r.m.s.

Purpose The aim of our work was to systematically review and meta-analyze epicardial adipose tissue (EAT) density values reported in literature, assessing potential correlations of EAT density with segmentation thresholds and other technical and clinical variables. Method A systematic search was performed, aiming for papers reporting global EAT density values in Hounsfield Units (HU) in patients undergoing chest CT for any clinical indication. After screening titles, abstract and full text of each retrieved work, studies reporting mean and standard deviation for EAT density were ultimately included. Technical, clinical and EAT data were extracted, and divided into subgroups according to clinical conditions of reported subjects. Pooled density analyses were performed both overall and for subgroups according to clinical conditions. Metaregression analyses were done to appraise the impact of clinical and technical variables on EAT volume. Results Out of 152 initially retrieved works, 13 were ultimately included, totaling for 7683 subjects. EAT density showed an overall pooled value of −85.86 HU (95% confidence interval [95% CI] −91.84, −79.89 HU), being −86.40 HU (95% CI −112.69, −60.12 HU) in healthy subjects and −80.71 HU (95% CI −87.43, −73.99 HU) in patients with coronary artery disease. EAT volume and lower and higher segmentation thresholds were found to be significantly correlated with EAT density (p = 0.044, p Conclusions Patients with coronary artery disease appear to present with higher EAT density values, while the correlations observed at metaregression highlight the need for well-established, shared thresholds for EAT segmentation.

Purpose The aim of this study is to review the literature concerning myocardial late gadolinium enhancement (LGE) with cardiac magnetic resonance in patients with Tetralogy of Fallot (ToF), with regards to its prevalence, characteristics and clinical relevance. Methods We performed a systematic search, aiming to retrieve original articles that evaluated LGE in ToF, running a search string on MEDLINE and EMBASE in November 2019 and November 2020. Papers were then selected by two independent, blinded readers based on title and abstract, and then on full-text reading, and articles which did not include LGE evaluation were excluded. From each included paper two readers extracted descriptive data concerning technical parameters of LGE acquisition, LGE description and clinical significance. Results 18 articles were eventually included in our review. The included studies observed that a higher amount of right ventricular LGE relates with higher right ventricular volumes, lower ejection fraction and a higher pulmonary regurgitant fraction, thus acting as a marker of progressive impairment of myocardial function. Moreover, LGE in ToF patients correlated with the onset of arrhythmias, and with serum biomarkers indicative of myocardial stress and fibrosis. Conclusions LGE could be used in the follow-up repaired ToF patients as its appraisal can provide information concerning cardiac dysfunction. Moreover, it may be ideal to aim towards a common framework for standardizing assessment and quantification of LGE in ToF patients.

Purpose To assess right ventricular (RV) myocardial strain both globally and segmentally through feature-tracking cardiac magnetic resonance (CMR) in patients with Tetralogy of Fallot (ToF), with regards to pulmonary valve replacement (PVR). Methods After Ethics Committee approval, we retrospectively included 46 consecutive ToF patients who had two CMR examinations performed at our institution between March 2014 and June 2019. We divided patients into those who had not undergone PVR between the two CMR examinations (Group-0), and those who had (Group-1). Ventricular volumes were quantified on cine sequences, and strain was calculated through feature-tracking, using the previously traced segmentations. RV longitudinal and radial strain were assessed both globally and separately for the septum and free wall. Variations were normalized for intercurrent years, differences were appraised with t-tests or Mann-Whitney U. Results 30 patients belonged to Group-0 and 16 to Group-1. Median age was 22 years (interquartile range [IQR] 17–29 years) in Group-0, and 21 years (IQR 16–29 years) in Group-1. No significant differences were reported in RV strain between groups (p ≥ 0.254) except for RV septal radial strain, significantly higher (p = 0.010) in Group-0 (24.2 %, IQR 10.1–52.4 %) than in Group-1 (6.0 %, IQR −3.3–23.3 %) at the second CMR. Both global and segmental RV strains decreased over time in both groups, and yearly variations did not differ significantly (p ≥ 0.081) between groups. Conclusions While PVR performed at the appropriate timing eases the burden on the RV allowing for a reduction in volumes, RV strain seems to continuously deteriorate as in patients who do not undergo PVR.

This work reports the first results of atmospheric radon measurements performed at the Italian Antarctic station located at Terra Nova Bay (74.69°S; 164.12°E) during summer campaigns of 1995-96, 1996-97 and 1997-98. Mean222Rn concentrations was 0.51 - 0.43 Bq mm3 (median = 0.37 Bq mm3), and ranged from 0.01 to 2.74 Bq mm3 . On the average, these values were considered high, in comparison to results reported for other sites in Antarctica at equivalent latitudes. This could be explained by two causes: radon data for Terra Nova Bay were only for the summer period, when the ice-free area is at its maximum and thus the radon emission to the atmosphere; and by the larger ice-free area at Terra Nova Bay compared, for example, to measurements taken at another Antarctic site by the same technique (Ferraz station - 62°05'S; 058°23.5'W). The mean 222Rn to 220Rn activity ratio was 4.4 - 4.2, ranging between 0.1 and 45.3. The highest ratios indicated that some of observed surges of concentration of 222Rn could be attr...