Search

Aims: To evaluate the repeatability of cardiac magnetic resonance (CMR) radiomics features on test-retest scanning using a multi-centre multi-vendor dataset with a varied case-mix.Methods and Results: The sample included 54 test-retest studies from the VOLUMES resource (thevolumesresource.com). Images were segmented according to a pre-defined protocol to select three regions of interest (ROI) in end-diastole and end-systole: right ventricle, left ventricle (LV), and LV myocardium. We extracted radiomics shape features from all three ROIs and, additionally, first-order and texture features from the LV myocardium. Overall, 280 features were derived per study. For each feature, we calculated intra-class correlation coefficient (ICC), within-subject coefficient of variation, and mean relative difference. We ranked robustness of features according to mean ICC stratified by feature category, ROI, and cardiac phase, demonstrating a wide range of repeatability. There were features with good and excellent repeatability (ICC ≥ 0.75) within all feature categories and ROIs. A high proportion of first-order and texture features had excellent repeatability (ICC ≥ 0.90), however, these categories also contained features with the poorest repeatability (ICC < 0.50).Conclusion: CMR radiomic features have a wide range of repeatability. This paper is intended as a reference for future researchers to guide selection of the most robust features for clinical CMR radiomics models. Further work in larger and richer datasets is needed to further define the technical performance and clinical utility of CMR radiomics.

Children with congenital and acquired heart disease may be exposed to relatively high lifetime cumulative doses of ionizing radiation from necessary medical invasive and non-invasive imaging procedures. Although these imaging procedures are all essential to the care of these complex paediatric population and have contributed to meaningfully improved outcomes in these patients, exposure to ionizing radiation is associated with potential risks, including an increased lifetime attributable risk of cancer. The goal of this manuscript is to provide a comprehensive review of radiation dose management and cardiac computed tomography performance in the paediatric population with congenital and acquired heart disease, to encourage informed imaging to achieve indication-appropriate study quality at the lowest achievable dose.

Background The rapid global spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), has re-ignited interest in the possible role of vitamin D in modulation of host responses to respiratory pathogens. Indeed, vitamin D supplementation has been proposed as a potential preventative or therapeutic strategy. Recommendations for any intervention, particularly in the context of a potentially fatal pandemic infection, should be strictly based on clinically informed appraisal of the evidence base. In this narrative review, we examine current evidence relating to vitamin D and COVID-19 and consider the most appropriate practical recommendations. Observations Although there are a growing number of studies investigating the links between vitamin D and COVID-19, they are mostly small and observational with high risk of bias, residual confounding, and reverse causality. Extrapolation of molecular actions of 1,25(OH)2-vitamin D to an effect of increased 25(OH)-vitamin D as a result of vitamin D supplementation is generally unfounded, as is the automatic conclusion of causal mechanisms from observational studies linking low 25(OH)-vitamin D to incident disease. Efficacy is ideally demonstrated in the context of adequately powered randomised intervention studies, although such approaches may not always be feasible. Conclusions At present, evidence to support vitamin D supplementation for the prevention or treatment of COVID-19 is inconclusive. In the absence of any further compelling data, adherence to existing national guidance on vitamin D supplementation to prevent vitamin D deficiency, predicated principally on maintaining musculoskeletal health, appears appropriate.

Introduction Cardiac magnetic resonance (CMR) radiomics use voxel-level data to derive quantitative indices of myocardial tissue texture, which may provide complementary risk information to traditional CMR measures. Purpose In this first stage of our work, establishing the performance characteristics of CMR radiomics in relation to disease outcomes, we aimed to elucidate differences in radiomic features by sex and age in apparently healthy adults. Methods We defined a healthy cohort from the first 5,065 individuals completing the UK Biobank Imaging Enhancement, limiting to white Caucasian ethnicity, and excluding those with major co-morbidities, or cardiovascular risk factors/symptoms. We created evenly distributed age groups: 45–54 years, 55–64 years, 65–74 years. Radiomics features were extracted from left ventricle segmentations, with normalisation to body surface area. We compared mean values of individual features between the sexes, stratified by age and separately between the oldest and youngest age groups for each sex. Results We studied 657 (309 men, 358 women) healthy individuals. There were significant differences between radiomics features of men and women. Different features appeared more important at different age groups. For instance, in the youngest age group “end-systolic coarseness” showed greatest difference between men and women, whilst “end-diastolic run percentage” and “end-diastolic high grey level emphasis” showed most variation in the oldest and middle age groups. In the oldest age groups, differences between men and women were most predominant in the texture features, whilst in the younger groups a mixture of shape and texture differences were observed. We demonstrate significant variation between radiomics features by age, these differences are exclusively in texture features with different features implicated in men and women (“end-diastolic mean intensity” in women, “end-systolic sum entropy in men”). Conclusions There are significant age and sex differences in CMR radiomics features of apparently healthy adults, demonstrating alterations in myocardial architecture not appreciated by conventional indices. In younger ages, shape and texture differences are observed, whilst in older ages texture differences dominate. Furthermore, texture features are the most different features between the youngest and oldest hearts. We provide proof-of-concept data indicating CMR radiomics has discriminatory value with regard to two characteristics strongly linked to cardiovascular outcomes. We will next elucidate relationships between CMR radiomics, cardiac risk factors, and clinical outcomes, establishing predictive value incremental to existing measures. Funding Acknowledgement Type of funding source: Other. Main funding source(s): European Union's Horizon 2020 research and innovation programme (825903),British Heart Foundation Clinical Research Training Fellowship (FS/17/81/33318)

Right ventricular (RV) structure and function influence the morbidity and mortality from coronary artery disease (CAD), dilated cardiomyopathy (DCM), pulmonary hypertension and heart failure. Little is known about the genetic basis of RV measurements. Here we perform genome-wide association analyses of four clinically relevant RV phenotypes (RV end-diastolic volume, RV end-systolic volume, RV stroke volume, RV ejection fraction) from cardiovascular magnetic resonance images, using a state-of-the-art deep learning algorithm in 29,506 UK Biobank participants. We identify 25 unique loci associated with at least one RV phenotype at P 2.27 ×10

Cholesterol and triglycerides are among the most well-known risk factors for cardiovascular disease.This study investigated whether higher low-density lipoprotein (LDL) cholesterol and triglyceride levels and lower high-density lipoprotein cholesterol level are causal risk factors for changes in prognostically important left ventricular (LV) parameters.One-sample Mendelian randomization (MR) of 17,311 European individuals from the UK Biobank with paired lipid and cardiovascular magnetic resonance data was performed. Two-sample MR was performed by using summary-level data from the Global Lipid Genetics Consortium (n = 188,577) and UK Biobank Cardiovascular Magnetic Resonance substudy (n = 16,923) for sensitivity analyses.In 1-sample MR analysis, higher LDL cholesterol was causally associated with higher LV end-diastolic volume (β = 1.85 ml; 95% confidence interval [CI]: 0.59 to 3.14 ml; p = 0.004) and higher LV mass (β = 0.81 g; 95% CI: 0.11 to 1.51 g; p = 0.023) and triglycerides with higher LV mass (β = 1.37 g; 95% CI: 0.45 to 2.3 g; p = 0.004). High-density lipoprotein cholesterol had no significant association with any LV parameter. Similar results were obtained by using 2-sample MR. Observational analyses were frequently discordant with those derived from MR.MR analysis demonstrates that LDL cholesterol and triglycerides are associated with adverse changes in cardiac structure and function, in particular in relation to LV mass. These findings suggest that LDL cholesterol and triglycerides may have a causal effect in influencing cardiac morphology in addition to their established role in atherosclerosis.

Over the past 2 decades, cardiac magnetic resonance (CMR) has become an essential component of cardiovascular clinical care and contributed to imaging-guided diagnosis and management of coronary artery disease, cardiomyopathy, congenital heart disease, cardio-oncology, valvular, and vascular disease, amongst others. The widespread availability, safety, and capability of CMR to provide corresponding anatomical, physiological, and functional data in 1 imaging session can improve the design and conduct of clinical trials through both a reduction of sample size and provision of important mechanistic data that may augment clinical trial findings. Moreover, prospective imaging-guided strategies using CMR can enhance safety, efficacy, and cost-effectiveness of cardiovascular pathways in clinical practice around the world. As the future of large-scale clinical trial design evolves to integrate personalized medicine, cost-effectiveness, and mechanistic insights of novel therapies, the integration of CMR will continue to play a critical role. In this document, the attributes, limitations, and challenges of CMR's integration into the future design and conduct of clinical trials will also be covered, and recommendations for trialists will be explored. Several prominent examples of clinical trials that test the efficacy of CMR-imaging guided pathways will also be discussed.

Background: Cardiometabolic morbidity and medications, specifically Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARBs), have been linked with adverse outcomes from coronavirus disease 2019 (COVID-19). This study aims to investigate, factors associated with COVID-19 positivity in hospital for 1,436 UK Biobank participants; compared with individuals who tested negative, and with the untested, presumed negative, rest of the cohort. Methods: We studied 7,099 participants from the UK Biobank who had been tested for COVID-19 in hospital. We considered the following exposures: age, sex, ethnicity, body mass index (BMI), diabetes, hypertension, hypercholesterolaemia, ACEi/ARB use, prior myocardial infarction (MI), and smoking. We undertook comparisons between (1) COVID-19 positive and COVID-19 negative tested participants; and (2) COVID-19 tested positive and the remaining participants (tested negative plus untested, n = 494,838). Logistic regression models were used to investigate univariate and mutually adjusted associations. Results: Among participants tested for COVID-19, Black, Asian, and Minority ethnic (BAME) ethnicity, male sex, and higher BMI were independently associated with a positive result. BAME ethnicity, male sex, greater BMI, diabetes, hypertension, and smoking were independently associated with COVID-19 positivity compared to the remaining cohort (test negatives plus untested). However, similar associations were observed when comparing those who tested negative for COVID-19 with the untested cohort; suggesting that these factors associate with general hospitalization rather than specifically with COVID-19. Conclusions: Among participants tested for COVID-19 with presumed moderate to severe symptoms in a hospital setting, BAME ethnicity, male sex, and higher BMI are associated with a positive result. Other cardiometabolic morbidities confer increased risk of hospitalization, without specificity for COVID-19. ACE/ARB use did not associate with COVID-19 status.

Background Left ventricular maximum wall thickness (MWT) is central to diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM), but measurement has variation. Objectives We developed a fully automated machine learning (ML) algorithm for MWT measurement and compared it to international experts using precision (repeatability) on a dataset of HCM patients scanned twice with cardiovascular magnetic resonance (CMR). Methods Training dataset: Endo- and epicardial end-diastolic contours were derived using a fully-automated convolutional neural network trained on 1,923 independent multi-centre multi-disease cases (14 centres from 3 countries, 10 scanner models, 2 field strengths, with balanced pathologies - health, athletes, myocardial infarction, aortic stenosis, HCM, dilated cardiomyopathy, infiltrative diseases) all segmented by a single expert. Patients: 60 HCM patients were scanned twice (scan:rescan) in the same session (no biological variability) at different field strengths and vendors (Siemens, GE, Philips) in 3 centres to allow generalizability. The protocol consisted of long axis cines and a short axis (SAX) bSSFP cine stack. Between scans, patients were brought out of the bore, repositioned on the table and re-isocentered. Wall thickness: MWT was measured in the SAX cine stack in end-diastole (scans A and B) by 11 experts (from 4 continents, 6 countries, 9 centers). For ML performance, the contours were based on a repurposed algorithm used for brain cortical thickness measurement, applying the Laplace equation for all contour points – effectively creating nested smoothly deforming surfaces from endo- to epicardium. We created orthogonal field lines to connect endo-and epicardial points, measured these distances and took the maximum as MWT. Results 1320 MWT measurements by experts were analyzed. Mean MWT varied significantly from 14.9 mm to 19.0 mm (Δ4.1 mm, p Conclusions ML MWT measurement in HCM is superior to all international experts studied with implications for risk stratification and sample sizes for clinical trials.

BackgroundWe examined whether the greater severity of coronavirus disease 2019 (COVID-19) amongst men and non-White ethnicities is explained by cardiometabolic, socio-economic, or behavioural factors.MethodsWe studied 4,510 UK Biobank participants tested for COVID-19 (positive, n = 1,326). Multivariate logistic regression models including age, sex, and ethnicity were used to test whether addition of: 1)cardiometabolic factors (diabetes, hypertension, high cholesterol, prior myocardial infarction, smoking, BMI); 2)25(OH)-vitamin D; 3)poor diet; 4)Townsend deprivation score; 5)housing (home type, overcrowding); or 6)behavioural factors (sociability, risk taking) attenuated sex/ethnicity associations with COVID-19 status.ResultsThere was over-representation of men and non-White ethnicities in the COVID-19 positive group. Non-Whites had, on average, poorer cardiometabolic profile, lower 25(OH)-vitamin D, greater material deprivation, and were more likely to live in larger households and flats/apartments. Male sex, non-White ethnicity, higher BMI, Townsend deprivation score, and household overcrowding were independently associated with significantly greater odds of COVID-19. The pattern of association was consistent for men and women; cardiometabolic, socio-demographic and behavioural factors did not attenuate sex/ethnicity associations.ConclusionsSex and ethnicity differential pattern of COVID-19 is not adequately explained by variations in cardiometabolic factors, 25(OH)-vitamin D levels, or socio-economic factors. Investigation of alternative biological pathways and different genetic susceptibilities is warranted.

The aim of this study was to define the variability of maximal wall thickness (MWT) measurements across modalities and predict its impact on care in patients with hypertrophic cardiomyopathy (HCM).Left ventricular MWT measured by echocardiography or cardiovascular magnetic resonance (CMR) contributes to the diagnosis of HCM, stratifies risk, and guides key decisions, including whether to place an implantable cardioverter-defibrillator (ICD).A 20-center global network provided paired echocardiographic and CMR data sets from patients with HCM, from which 17 paired data sets of the highest quality were selected. These were presented as 7 randomly ordered pairs (at 6 cardiac conferences) to experienced readers who report HCM imaging in their daily practice, and their MWT caliper measurements were captured. The impact of measurement variability on ICD insertion decisions was estimated in 769 separately recruited multicenter patients with HCM using the European Society of Cardiology algorithm for 5-year risk for sudden cardiac death.MWT analysis was completed by 70 readers (from 6 continents; 91% with5 years' experience). Seventy-nine percent and 68% scored echocardiographic and CMR image quality as excellent. For both modalities (echocardiographic and then CMR results), intramodality inter-reader MWT percentage variability was large (range -59% to 117% [SD ±20%] and -61% to 52% [SD ±11%], respectively). Agreement between modalities was low (SE of measurement 4.8 mm; 95% CI 4.3 mm-5.2 mm; r = 0.56 [modest correlation]). In the multicenter HCM cohort, this estimated echocardiographic MWT percentage variability (±20%) applied to the European Society of Cardiology algorithm reclassified risk in 19.5% of patients, which would have led to inappropriate ICD decision making in 1 in 7 patients with HCM (8.7% would have had ICD placement recommended despite potential low risk, and 6.8% would not have had ICD placement recommended despite intermediate or high risk).Using the best available images and experienced readers, MWT as a biomarker in HCM has a high degree of inter-reader variability and should be applied with caution as part of decision making for ICD insertion. Better standardization efforts in HCM recommendations by current governing societies are needed to improve clinical decision making in patients with HCM.

Background: Cardiometabolic morbidity and medications, specifically Angiotensin Converting Enzyme inhibitors (ACEi) and Angiotensin Receptor Blockers (ARBs), have been linked with adverse outcomes from coronavirus disease 2019 (COVID-19). This study aims to investigate factors associated with COVID-19 positivity for the first 669 UK Biobank participants; compared with individuals who tested negative, and with the untested, presumed negative, rest of the population. Methods: We studied 1,474 participants from the UK Biobank who had been tested for COVID-19. Given UK testing policy, this implies a hospital setting, suggesting at least moderate to severe symptoms. We considered the following exposures: age, sex, ethnicity, body mass index (BMI), diabetes, hypertension, hypercholesterolaemia, ACEi/ARB use, prior myocardial infarction (MI), and smoking. We undertook comparisons between: 1) COVID-19 positive and COVID-19 tested negative participants; and 2) COVID-19 tested positive and the remaining participants (tested negative plus untested, n=501,837). Logistic regression models were used to investigate univariate and mutually adjusted associations. Results: Among participants tested for COVID-19, non-white ethnicity, male sex, and greater BMI were independently associated with COVID-19 positive result. Non-white ethnicity, male sex, greater BMI, diabetes, hypertension, prior MI, and smoking were independently associated with COVID-19 positivity compared to the remining cohort (test negatives plus untested). However, similar associations were observed when comparing those who tested negative for COVID-19 with the untested cohort; suggesting that these factors associate with general hospitalisation rather than specifically with COVID-19. Conclusions: Among participants tested for COVID-19 with presumed moderate to severe symptoms in a hospital setting, non-white ethnicity, male sex, and higher BMI are associated with a positive result. Other cardiometabolic morbidities confer increased risk of hospitalisation, without specificity for COVID-19. Notably, ACE/ARB use did not associate with COVID-19 status.

Recent EACVI recommendations described the importance of limiting cardiovascular imaging during the COVID-19 pandemic in order to reduce virus transmission, protect healthcare professionals from contamination, and reduce consumption of personal protective equipment. However, an elevated troponin remains a frequent request for cardiac imaging in COVID-19 patients, partly because it signifies cardiac injury due to a variety of causes and partly because it is known to convey a worse prognosis. The present paper aims to provide guidance to clinicians regarding the appropriateness of cardiac imaging in the context of troponin elevation and myocardial injury, how best to decipher the mechanism of myocardial injury, and how to guide patient management.

Cardiac magnetic resonance imaging (MRI) provides a wealth of imaging biomarkers for cardiovascular disease care and segmentation of cardiac structures is required as a first step in enumerating these biomarkers. Deep convolutional neural networks (CNNs) have demonstrated remarkable success in image segmentation but typically require large training datasets and provide suboptimal results that require further improvements. Here, we developed a way to enhance cardiac MRI multi-class segmentation by combining the strengths of CNN and interpretable machine learning algorithms. We developed a continuous kernel cut segmentation algorithm by integrating normalized cuts and continuous regularization in a unified framework. The high-order formulation was solved through upper bound relaxation and a continuous max-flow algorithm in an iterative manner using CNN predictions as inputs. We applied our approach to two representative cardiac MRI datasets across a wide range of cardiovascular pathologies. We comprehensively evaluated the performance of our approach for two CNNs trained with various small numbers of training cases, tested on the same and different datasets. Experimental results showed that our approach improved baseline CNN segmentation by a large margin, reduced CNN segmentation variability substantially, and achieved excellent segmentation accuracy with minimal extra computational cost. These results suggest that our approach provides a way to enhance the applicability of CNN by enabling the use of smaller training datasets and improving the segmentation accuracy and reproducibility for cardiac MRI segmentation in research and clinical patient care.

Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2020., The coronavirus disease 2019 (COVID-19) pandemic has created new and unpredictable challenges for modern medicine and healthcare systems. Preliminary reports have demonstrated that older age, previous cardiovascular disease, diabetes, and hypertension are risk factors for increased mortality. Data on the cardiac affinity of the virus and its potential to harm the cardiovascular system and the mechanisms by which this occurs are sparse. A systemic infection generally increases demand on the heart, and can exacerbate underlying cardiac conditions. When the lungs are heavily involved, as seen in COVID-19 patients, this may have a major impact on cardiac function, particularly that of the right ventricle. Finally, COVID-19 may have direct effects on the heart, as may some drugs being used in its treatment.

Accurate ventricular volume measurements are the primary indicators of normal/abnor- mal cardiac function and are dependent on the Cardiac Magnetic Resonance (CMR) volumes being complete. However, missing or unusable slices owing to the presence of image artefacts such as respiratory or motion ghosting, aliasing, ringing and signal loss in CMR sequences, significantly hinder accuracy of anatomical and functional cardiac quantification, and recovering from those is insufficiently addressed in population imaging. In this work, we propose a new robust approach, coined Image Imputation Generative Adversarial Network (I2-GAN), to learn key features of cardiac short axis (SAX) slices near missing information, and use them as conditional variables to infer missing slices in the query volumes. In I2-GAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold, conditioned on intensity features through a generator net. The generator comprises residual blocks with normalisation layers that are modulated with auxiliary slice information, enabling propagation of fine details through the network. In addition, a multi-scale discriminator was implemented, along with a discriminator-based feature matching loss, to further enhance performance and encourage the synthesis of visually realistic slices. Experimental results show that our method achieves significant improvements over the state-of-the-art, in missing slice imputation for CMR, with an average SSIM of 0.872. Linear regression analysis yields good agreement between reference and imputed CMR images for all cardiac measurements, with correlation coefficients of 0.991 for left ventricular volume, 0.977 for left ventricular mass and 0.961 for right ventricular volume.

Aims Assessment of haemodynamically significant coronary artery disease (CAD) using cardiovascular magnetic resonance (CMR) imaging perfusion or dynamic stress myocardial perfusion imaging by computed tomography (CT perfusion) may aid patient selection for invasive coronary angiography (ICA). We evaluated the diagnostic performance and incremental value of qualitative CMR perfusion and quantitative CT perfusion complementary to cardiac computed tomography angiography (CCTA) for the diagnosis of haemodynamically significant CAD using fractional flow reserve (FFR) and quantitative coronary angiography (QCA) as reference standard. Methods and results CCTA, qualitative visual CMR perfusion, visual CT perfusion, and quantitative relative myocardial blood flow (CT-MBF) were performed in patients with stable angina pectoris. FFR was measured in coronary vessels with stenosis visually estimated between 30% and 90% diameter reduction on ICA. Haemodynamically significant CAD was defined as FFR Conclusion Visual CMR perfusion and relative CT-MBF outperformed visual CT perfusion and provided incremental discrimination compared with CCTA alone for the diagnosis of haemodynamically significant CAD.

This prospective cohort study evaluates associations between structural and ultrastructural parameters in baseline biopsies from human kidney transplants and long-term graft survival after more than 14 years’ follow-up. Baseline kidney graft biopsies were obtained prospectively from 54 consecutive patients receiving a kidney transplant at a single institution. Quantitative measurements were performed on the baseline biopsies by computer-assisted light microscopy and electron microscopy. Stereology-based techniques estimated the fraction of interstitial tissue, the volume of glomeruli, mesangial fraction, and basement membrane thickness of glomerular capillaries. The fraction of occluded glomeruli and scores according to the Banff classification were achieved. Kidney graft survival was analyzed by Kaplan–Meier estimates and Cox regression. Association to long-term kidney function was also analyzed. The long-term surviving kidney transplants were characterized at implantation by less arteriolar hyaline thickening (P < 0.001) and less interstitial fibrosis (P = 0.001), as well as a lower fraction of occluded glomeruli (P = 0.004) and lower glomerular volume (P = 0.03). At the latest follow-up, eGFR was decreased by 12 ml/min/1.73 m2 per unit increase in the score for arteriolar hyalinosis at implantation (P = 0.02), and eGFR was decreased by 19 ml/min/1.73 m2 per 106 μm3 increase in glomerular volume at baseline (P = 0.03). The unbiased Cavalieri estimate of glomerular volume and the ultrastructural parameters are the first to be evaluated in a cohort study with prospective follow-up for more than 14 years. The study shows that baseline biopsies from human kidney grafts contain extraordinary long-term prognostic information, and it highlights the importance of these intrinsic graft factors.

Background:Myocardial perfusion reflects the macro- and microvascular coronary circulation. Recent quantitation developments using cardiovascular magnetic resonance (CMR) perfusion permit automated measurement clinically. We explored the prognostic significance of stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR, the ratio of stress to rest MBF).Methods:A two center study of patients with both suspected and known coronary artery disease referred clinically for perfusion assessment. Image analysis was performed automatically using a novel artificial intelligence approach deriving global and regional stress and rest MBF and MPR. Cox proportional hazard models adjusting for co-morbidities and CMR parameters sought associations of stress MBF and MPR with death and major adverse cardiovascular events (MACE), including myocardial infarction, stroke, heart failure hospitalization, late (>90 day) revascularization and death.Results:1049 patients were included with median follow-up 605 (interquartile range 464-814) days. There were 42 (4.0%) deaths and 188 MACE in 174 (16.6%) patients. Stress MBF and MPR were independently associated with both death and MACE. For each 1ml/g/min decrease in stress MBF the adjusted hazard ratio (HR) for death and MACE were 1.93 (95% CI 1.08-3.48, P=0.028) and 2.14 (95% CI 1.58-2.90, PConclusions:In patients with known or suspected coronary artery disease, reduced MBF and MPR measured automatically inline using artificial intelligence quantification of CMR perfusion mapping provides a strong, independent predictor of adverse cardiovascular outcomes.

Purpose To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI–tagged images. Materials and Methods In this retrospective cross-sectional study, 4508 cases from the U.K. Biobank were split randomly into 3244 training cases, 812 validation cases, and 452 test cases. Ground truth myocardial landmarks were defined and tracked by manual initialization and correction of deformable image registration using previously validated software with five readers. The fully automatic framework consisted of (a) a convolutional neural network (CNN) for localization and (b) a combination of a recurrent neural network (RNN) and a CNN to detect and track the myocardial landmarks through the image sequence for each slice. Radial and circumferential strain were then calculated from the motion of the landmarks and averaged on a slice basis. Results Within the test set, myocardial end-systolic circumferential Green strain errors were −0.001 ± 0.025, −0.001 ± 0.021, and 0.004 ± 0.035 in the basal, mid-, and apical slices, respectively (mean ± standard deviation of differences between predicted and manual strain). The framework reproduced significant reductions in circumferential strain in participants with diabetes, hypertensive participants, and participants with a previous heart attack. Typical processing time was approximately 260 frames (approximately 13 slices) per second on a GPU with 12 GB RAM compared with 6–8 minutes per slice for the manual analysis. Conclusion The fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due to diabetes, hypertension, and previous heart attack. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Supplemental Digital Content is available in the text., Background: Although left ventricular noncompaction (LVNC) has been associated with an increased risk of adverse cardiovascular events, the accurate incidence of cardiovascular morbidity and mortality is unknown. We, therefore, aimed to assess the incidence rate of LVNC-related cardiovascular events. Methods: We systematically searched observational studies reporting the adverse outcomes related to LVNC. The primary end point was cardiovascular mortality. Results: We identified 28 eligible studies enrolling 2501 LVNC patients (mean age, 46 years; male/female ratio, 1.7). After a median follow-up of 2.9 years, the pooled event rate for cardiovascular mortality was 1.92 (95% CI, 1.54–2.30) per 100 person-years. LVNC patients had a similar risk of cardiovascular mortality compared with a dilated cardiomyopathy control group (odds ratio, 1.10 [95% CI, 0.18–6.67]). The incidence rates of all-cause mortality, stroke and systemic emboli, heart failure admission, cardiac transplantation, ventricular arrhythmias, and cardiac device implantation were 2.16, 1.54, 3.53, 1.24, 2.17, and 2.66, respectively, per 100 person-years. Meta-regression and subgroup analyses revealed that left ventricular ejection fraction, not the extent of left ventricular trabeculation, had an important influence on the variability of incidence rates. The risks of thromboembolism and ventricular arrhythmias in LVNC patients were similar to dilated cardiomyopathy patients. However, LVNC patients had a higher incidence of heart failure hospitalization than dilated cardiomyopathy patients. Conclusions: Patients with LVNC carry a similar cardiovascular risk when compared with dilated cardiomyopathy patients. Left ventricular ejection fraction—a conventional indicator of heart failure severity, not the extent of trabeculation—appears to be an important determinant of adverse outcomes in LVNC patients. Registration: https://www.crd.york.ac.uk/PROSPERO/ Unique identifier: CRD42018096313.

Objective: Convolutional neural networks (CNNs) have demonstrated promise in automated cardiac magnetic resonance image segmentation. However, when using CNNs in a large real-world dataset, it is important to quantify segmentation uncertainty and identify segmentations which could be problematic. In this work, we performed a systematic study of Bayesian and non-Bayesian methods for estimating uncertainty in segmentation neural networks. Methods: We evaluated Bayes by Backprop, Monte Carlo Dropout, Deep Ensembles, and Stochastic Segmentation Networks in terms of segmentation accuracy, probability calibration, uncertainty on out-of-distribution images, and segmentation quality control. Results: We observed that Deep Ensembles outperformed the other methods except for images with heavy noise and blurring distortions. We showed that Bayes by Backprop is more robust to noise distortions while Stochastic Segmentation Networks are more resistant to blurring distortions. For segmentation quality control, we showed that segmentation uncertainty is correlated with segmentation accuracy for all the methods. With the incorporation of uncertainty estimates, we were able to reduce the percentage of poor segmentation to 5% by flagging 31--48% of the most uncertain segmentations for manual review, substantially lower than random review without using neural network uncertainty (reviewing 75--78% of all images). Conclusion: This work provides a comprehensive evaluation of uncertainty estimation methods and showed that Deep Ensembles outperformed other methods in most cases. Significance: Neural network uncertainty measures can help identify potentially inaccurate segmentations and alert users for manual review., Comment: Accepted to IEEE Transactions on Biomedical Engineering. Copyright (c) 2022 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending an email to pubs-permissions@ieee.org

Cardiovascular magnetic resonance (CMR) radiomics is a novel technique for advanced cardiac image phenotyping by analyzing multiple quantifiers of shape and tissue texture. In this paper, we assess, in the largest sample published to date, the performance of CMR radiomics models for identifying changes in cardiac structure and tissue texture due to cardiovascular risk factors. We evaluated five risk factor groups from the first 5,065 UK Biobank participants: hypertension (n = 1,394), diabetes (n = 243), high cholesterol (n = 779), current smoker (n = 320), and previous smoker (n = 1,394). Each group was randomly matched with an equal number of healthy comparators (without known cardiovascular disease or risk factors). Radiomics analysis was applied to short axis images of the left and right ventricles at end-diastole and end-systole, yielding a total of 684 features per study. Sequential forward feature selection in combination with machine learning (ML) algorithms (support vector machine, random forest, and logistic regression) were used to build radiomics signatures for each specific risk group. We evaluated the degree of separation achieved by the identified radiomics signatures using area under curve (AUC), receiver operating characteristic (ROC), and statistical testing. Logistic regression with L1-regularization was the optimal ML model. Compared to conventional imaging indices, radiomics signatures improved the discrimination of risk factor vs. healthy subgroups as assessed by AUC [diabetes: 0.80 vs. 0.70, hypertension: 0.72 vs. 0.69, high cholesterol: 0.71 vs. 0.65, current smoker: 0.68 vs. 0.65, previous smoker: 0.63 vs. 0.60]. Furthermore, we considered clinical interpretation of risk-specific radiomics signatures. For hypertensive individuals and previous smokers, the surface area to volume ratio was smaller in the risk factor vs. healthy subjects; perhaps reflecting a pattern of global concentric hypertrophy in these conditions. In the diabetes subgroup, the most discriminatory radiomics feature was the median intensity of the myocardium at end-systole, which suggests a global alteration at the myocardial tissue level. This study confirms the feasibility and potential of CMR radiomics for deeper image phenotyping of cardiovascular health and disease. We demonstrate such analysis may have utility beyond conventional CMR metrics for improved detection and understanding of the early effects of cardiovascular risk factors on cardiac structure and tissue.

Background: Most biomedical research has focused on sampling COVID-19 patients presenting to hospital with advanced disease, with less focus on the asymptomatic or paucisymptomatic. We established a bioresource with serial sampling of health care workers (HCWs) designed to obtain samples before and during mainly mild disease, with follow-up sampling to evaluate the quality and duration of immune memory. Methods: We conducted a prospective observational study on HCWs from three hospital sites in London, initially at a single centre (recruited just prior to first peak community transmission in London), but then extended to multiple sites 3 weeks later (recruitment still ongoing, target n=1,000). Asymptomatic participants attending work complete a health questionnaire, and provide a nasal swab (for SARS-CoV-2 RNA by RT-PCR tests) and blood samples (mononuclear cells, serum, plasma, RNA and DNA are biobanked) at 16 weekly study visits, and at 6 and 12 months. Results: Preliminary baseline results for the first 731 HCWs (400 single-centre, 331 multicentre extension) are presented. Mean age was 38±11 years; 67% are female, 31% nurses, 20% doctors, and 19% work in intensive care units. COVID-19-associated risk factors were: 37% black, Asian or minority ethnicities; 18% smokers; 13% obesity; 11% asthma; 7% hypertension and 2% diabetes mellitus. At baseline, 41% reported symptoms in the preceding 2 weeks. Preliminary test results from the initial cohort (n=400) are available: PCR at baseline for SARS-CoV-2 was positive in 28 of 396 (7.1%, 95% CI 4.9-10.0%) and 15 of 385 (3.9%, 2.4-6.3%) had circulating IgG antibodies. Conclusions: This COVID-19 bioresource established just before the peak of infections in the UK will provide longitudinal assessments of incident infection and immune responses in HCWs through the natural time course of disease and convalescence. The samples and data from this bioresource are available to academic collaborators by application https://covid-consortium.com/application-for-samples/.

The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date.

The European Heart Journal—Cardiovascular Imaging was launched in 2012 and has during these years become one of the leading multimodality cardiovascular imaging journals. The journal is now established as one of the top cardiovascular journals and is the most important cardiovascular imaging journal in Europe. The most important studies published in our Journal in 2019 will be highlighted in two reports. Part I of the review will focus on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.

Distinguishing between adaptive and maladaptive cardiovascular response to exercise is crucial to prevent the unnecessary termination of an athlete’s career and to minimize the risk of sudden death. This is a challenging task essentially due to the substantial phenotypic overlap between electrical and structural changes seen in the physiological athletic heart remodeling and pathological changes seen in inherited or acquired cardiomyopathies. Stress testing is an ideal tool to discriminate normal from abnormal cardiovascular response by unmasking subtle pathologic responses otherwise undetectable at rest. Treadmill or bicycle electrocardiography, transthoracic echocardiography, and cardiopulmonary exercise testing are common clinical investigations used in sports cardiology, specifically among participants presenting with resting electrocardiographic abnormalities, frequent premature ventricular beats, or non-sustained ventricular arrhythmias. In this setting, as well as in cases of left ventricular hypertrophy or asymptomatic left ventricular dysfunction, stress imaging and myocardial tissue characterization by cardiovascular magnetic resonance show promise. In this review, we aimed to reappraise current diagnostic schemes, screening strategies and novel approaches that may be used to distinguish adaptive remodeling patterns to physical exercise from early phenotypes of inherited or acquired pathological conditions commanding prompt intervention.

Chronic obstructive pulmonary disease (COPD) is a complex multi-morbid disorder with significant cardiac mortality. Current cardiovascular risk prediction models do not include COPD. We investigated whether COPD modifies future cardiovascular risk to determine if it should be considered in risk prediction models.Case-control study using baseline data from two randomized controlled trials performed between 2012 and 2015. Of the 90 eligible subjects, 26 COPD patients with lung hyperinflation were propensity matched for 10-year global cardiovascular risk score (QRISK2) with 26 controls having normal lung function. Patients underwent cardiac magnetic resonance imaging, arterial stiffness and lung function measurements. Differences in pulse wave velocity (PWV), total arterial compliance (TAC) and aortic distensibility were main outcome measures.PWV (mean difference 1.0 m/s, 95% CI 0.02-1.92

Background: Current guidance from International Society for Heart and Lung Transplantation recommends using body weight for donor-recipient size matching for heart transplantation. However, recent studies have shown that predicted heart mass, using body weight, height, age, and sex, may represent a better method of size matching. We aim to validate a cardiovascular magnetic resonance (CMR)–derived equation for predicted left ventricular mass (LVM) in a cohort of normal individuals in the United Kingdom. Methods: This observational study was conducted in 5065 middle-aged (44–77 years old) UK Biobank participants who underwent CMR imaging in 2014 to 2015. Individuals with cancer diagnosis in the previous 12 months or history of cardiovascular disease were excluded. Predicted LVM was calculated based on participants’ sex, height, and weight recorded at the time of imaging. Correlation analyses were performed between the predicted LVM and the LVM obtained from manual contouring of CMR cine images. The analysis included 3398 participants (age 61.5±7.5 years, 47.8% males). RESULTS: Predicted LVM was considerably higher than CMR-derived LVM (mean±SD of 138.8±28.9 g versus 86.3±20.9 g). However, there was a strong correlation between the 2 measurements (Spearman correlation coefficient 0.802, P Conclusions: Predicted LVM calculated using a CMR-derived equation that incorporates height, weight, and sex has a strong correlation with CMR LVM in large cohort of normal individuals in the United Kingdom. Our findings suggest that predicted heart mass equations may be a valid tool for donor-recipient size matching for heart transplantation in the United Kingdom.

International audience; The European Heart Journal - Cardiovascular Imaging was launched in 2012. It has gained an impressive impact factor of 8.336 during its first 6 years and is now established as one of the top 10 cardiovascular journals in the world and the most important cardiovascular imaging journal in Europe. The most important studies published in the journal in 2017 will be highlighted in two reports. Part I will focus on studies about myocardial function, coronary artery disease and myocardial ischaemia, and emerging techniques and applications in cardiovascular imaging, whereas Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.

Aims: Perfusion scans after coronary computed tomography angiography (CCTA) in patients with suspected coronary artery disease (CAD) may reduce unnecessary invasive coronary angiographies (ICAs). However, the diagnostic accuracy of perfusion scans after primary CCTA is unknown. The aim of this study was to determine the diagnostic accuracy of cardiac magnetic resonance (CMR) and myocardial perfusion scintigraphy (MPS) against ICA with fractional flow reserve (FFR) in patients suspected of CAD by CCTA.Methods and results: Included were consecutive patients (1675) referred to CCTA with symptoms of CAD and low/intermediate risk profile. Patients with suspected CAD based on CCTA were randomized 1:1 to CMR or MPS followed by ICA with FFR. Obstructive CAD was defined as FFR ≤ 0.80 or > 90% diameter stenosis by visual assessment. After initial CCTA, 392 patients (23%) were randomized; 197 to CMR and 195 to MPS. Perfusion scans and ICA were completed in 292 patients (CMR 148, MPS 144). Based on the ICA, 117/292 (40%) patients were classified with CAD. Sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for CMR were 41%, 95% CI [28-54], 84% [75-91], 62% [45-78], and 68% [58-76], respectively. For the MPS group 36% [24-50], 94% [87-98], 81% [61-93], and 68% [59-76], respectively.Conclusion: Patients with low/intermediate CAD risk and a positive CCTA scan represent a challenge to perfusion techniques indicated by the low sensitivity of both CMR and MPS with FFR as a reference. The mechanisms underlying this discrepancy need further investigation.

Restrictive cardiomyopathies (RCMs) are a diverse group of myocardial diseases with a wide range of aetiologies, including familial, genetic and acquired diseases and ranging from very rare to relatively frequent cardiac disorders. In all these diseases, imaging techniques play a central role. Advanced imaging techniques provide important novel data on the diagnostic and prognostic assessment of RCMs. This EACVI consensus document provides comprehensive information for the appropriateness of all non-invasive imaging techniques for the diagnosis, prognostic evaluation, and management of patients with RCM.

To investigate the hypothesis that persistence of apical contraction into diastole is linked to reduced myocardial perfusion and chest pain.Apical hypertrophic cardiomyopathy (HCM) is defined by left ventricular (LV) hypertrophy predominantly of the apex. Hyperdynamic contractility resulting in obliteration of the apical cavity is often present. Apical HCM can lead to drug-refractory chest pain.We retrospectively studied 126 subjects; 76 with apical HCM and 50 controls (31 with asymmetrical septal hypertrophy (ASH) and 19 with non-cardiac chest pain and culprit free angiograms and structurally normal hearts). Perfusion cardiac magnetic resonance imaging (CMR) scans were assessed for myocardial perfusion reserve index (MPRi), late gadolinium enhancement (LGE), LV volumes (muscle and cavity) and regional contractile persistence (apex, mid and basal LV).In apical HCM, apical MPRi was lower than in normal and ASH controls (p0.05). In apical HCM, duration of contractile persistence was associated with lower MPRi (p0.01) and chest pain (p0.05). In multivariate regression, contractile persistence was independently associated with chest pain (p0.01) and reduced MPRi (p0.001).In apical HCM, regional contractile persistence is associated with impaired myocardial perfusion and chest pain. As apical myocardium makes limited contributions to stroke volume, apical contractility is also largely ineffective. Interventions to reduce apical contraction and/or muscle mass are potential therapies for improving symptoms without reducing cardiac output.

Background: The clinical use and prognostic value of plasma brain natriuretic peptide (NT-proBNP) and soluble suppression of tumourigenicity-2 (sST2) levels are not known in patients with repaired Tetralogy of Fallot (rTOF).Objectives: We evaluated blood biomarkers in rTOF patients by combining the available evidence, focussing on prognosis, adverse echocardiographic findings and exercise intolerance.Methods: This systematic review and meta-analysis were carried out in accordance with the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. For the primary prognostic outcomes, a meta-analysis was performed. For hemodynamic outcomes, a pooled meta-analysis of correlation coefficients (r) was performed. The study protocol was registered with PROSPERO(CRD42020211897).Results: We analysed 1479 patients with repaired TOF in 23 studies. Mean age was 22.7 ​± ​8.3 years. The mean value of NT-proBNP was 174.4.1 ​± ​56.4 ​pg/ml while ST2 drawn from two investigations was 26.95 ​ng/ml. There was no difference in mean NT-proBNP between older and younger subjects (160.4 ​± ​37.7 vs190.6 ​± ​72.9, pg/ml, respectively; p ​> ​.05). NT-proBNP levels were higher in TAP studies than others with other RVOT intervention (191.6 ​± ​57 vs 151 ​± ​46, pg/ml, p ​

Previous studies have identified significant demand response (DR) potentials in using economic model predictive control (E-MPC) of space heating to exploit the inherent thermal mass in residential buildings for short-term energy storage. However, the economically viable realisation of E-MPC in residential buildings requires an effort to minimise the need for additional equipment and labour-intensive modelling processes. This paper reports on an experiment where a novel E-MPC setup was used for thermostatically control of a hydronic radiator in a highly-insulated residential building located on the NTNU Campus in Trondheim, Norway. The E-MPC utilized data from a heating meter, two temperature sensors and an existing weather forecast web service to train a linear black-box model. The results showed that the precision of model trained on excitation data that was generated using setpoints of either 21 or 24 °C was sufficient to obtain good control of the indoor air temperature while shifting consumption from high to low price periods. The findings of the experiment indicate that a minimal E-MPC setup is able to realize the significant DR potential that lies in utilizing the inherent thermal mass in residential buildings.

High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is challenging since it requires long acquisition and patient breath-hold times. Instead, 2D balanced steady-state free precession (SSFP) sequence is widely used in clinical routine. However, it produces highly-anisotropic image stacks, with large through-plane spacing that can hinder subsequent image analysis. To resolve this, we propose a novel, robust adversarial learning super-resolution (SR) algorithm based on conditional generative adversarial nets (GANs), that incorporates a state-of-the-art optical flow component to generate an auxiliary image to guide image synthesis. The approach is designed for real-world clinical scenarios and requires neither multiple low-resolution (LR) scans with multiple views, nor the corresponding HR scans, and is trained in an end-to-end unsupervised transfer learning fashion. The designed framework effectively incorporates visual properties and relevant structures of input images and can synthesise 3D isotropic, anatomically plausible cardiac MR images, consistent with the acquired slices. Experimental results show that the proposed SR method outperforms several state-of-the-art methods both qualitatively and quantitatively. We show that subsequent image analyses including ventricle segmentation, cardiac quantification, and non-rigid registration can benefit from the super-resolved, isotropic cardiac MR images, to produce more accurate quantitative results, without increasing the acquisition time. The average Dice similarity coefficient (DSC) for the left ventricular (LV) cavity and myocardium are 0.95 and 0.81, respectively, between real and synthesised slice segmentation. For non-rigid registration and motion tracking through the cardiac cycle, the proposed method improves the average DSC from 0.75 to 0.86, compared to the original resolution images. ispartof: MEDICAL IMAGE ANALYSIS vol:71 ispartof: location:Netherlands status: published

Objectives: The study sought to determine the associations between local (pericardial) fat and incident cardiovascular disease (CVD) events and cardiac remodeling independent of markers of ...

Background A common characteristic of migraine-inducing substances is that they cause headache and no pain in other areas of the body. Few studies have compared pain mechanisms in the trigeminal and spinal systems and, so far, no major differences have been noted. We compared signalling molecules in the trigeminal and spinothalamic system after infusion of the migraine-provoking substance glyceryltrinitrate. Method A catheter was placed in the femoral vein of rats and one week later glyceryltrinitrate 4 µg/kg/min was infused for 20 min. Protein expression in the dura mater, trigeminal ganglion, nucleus caudalis, dorsal root ganglion and the dorsal horn of the thoracic spinal cord was analysed at different time points using western blotting and immunohistochemistry. Results Glyceryltrinitrate caused a threefold increase in expression of phosphorylated extracellular signal-regulated kinases at 30 min in the dura mater and nucleus caudalis ( P Conclusion The dura, trigeminal ganglion and nucleus caudalis are activated shortly after glycerytrinitrate infusion with long-lasting expression of phosphorylated extracellular signal-regulated kinases observed in the nucleus caudalis. These activations were not observed at the spinal level.

Left ventricular noncompaction (LVNC) is characterized by the presence of an extensive noncompacted myocardial layer lining the cavity of the left ventricle (LV) and potentially leads to cardiac failure, thromboembolism, and malignant arrhythmias [(1)][1]. LVNC is a heterogeneous clinical condition

Introduction Research in development of new migraine therapeutics is hindered by the lack of suitable, predictive animal models. Cilostazol provokes headache in healthy humans and migraineurs by increasing intracellular cAMP levels. We aimed to investigate whether cilostazol could provoke headache-like behaviours and c-fos expression in rats. In order to evaluate the predictive validity of the model, we examined the response to the migraine specific drug sumatriptan. Methods The effect of cilostazol (125 mg/kg p.o.) in female Sprague Dawley rats was evaluated on a range of spontaneous behavioural parameters, light sensitivity and mechanical sensitivity thresholds. We also measured c-fos expression in the trigeminal nucleus caudalis. Results Cilostazol increased light sensitivity and grooming behaviour. These manifestations were not inhibited by sumatriptan. Cilostazol also induced c-fos expression in the trigeminal nucleus caudalis. Furthermore, trigeminal – but not hind paw hyperalgesia was observed. Conclusion The altered behaviours are suggestive of cilostazol induced headache with migraine-like features, but not specific. The presence of head specific hyperalgesia and the c-fos response in the trigeminal nucleus caudalis imply that the model involves trigeminal nociception. The model will be useful for studying mechanisms related to the cAMP pathway in headache, but its predictive properties appear to be more limited due to the lack of response to sumatriptan.

Contract grant sponsor: UK National Institute for Health Research Rare Diseases Translational Research Collaboration; contract grant number: NIHR RD-TRC, 171603; Contract grant sponsor: NIHR University College London Hospitals Biomedical Research Centre and the Biomedical Research Unit at Barts Hospital.

The evidence is increasing that left ventricular noncompaction cardiomyopathy as it is currently defined does not represent a failure of compaction of pre-existing trabecular myocardium found during embryonic development to form the compact component of the ventricular walls. Neither is there evidence of which we are aware to favour the notion that the entity is a return to a phenotype seen in cold-blooded animals. It is also known that when seen in adults, the presence of excessive ventricular trabeculations does not portend a poor prognosis when the ejection fraction is normal, with the risks of complications such as arrhythmia and stroke being rare in this setting. It is also the case that images of "noncompaction" as provided from children or autopsy studies are quite different from the features observed clinically in asymptomatic adults with excessive trabeculation. Our review suggests that the presence of an excessively trabeculated left ventricular wall is not in itself a clinical entity. It is equally possible that the excessive trabeculation is no more than a bystander in the presence of additional lesions such as dilated cardiomyopathy, with the additional lesions being responsible for the reduced ejection fraction bringing a given patient to clinical attention. We, therefore, argue that the term "noncompaction cardiomyopathy" is misleading, because there is neither failure of compaction nor a cardiomyopathic process in most individuals that fulfill widely used diagnostic criteria.

When I started to study medicine, I had a narrow view of what a doctor’s job looks like. Looking back over the last 20 years since my graduation, I now appreciate that medicine provides some of the most exciting opportunities and can lead to diverse roles. I hope by providing you with a personal glimpse into my career that you may reflect on your own plans and development. I am driven not only by personal ambition but mostly, I think, by wanting to make a positive impact on patients, the public, colleagues and trainees. I enjoy lifelong learning, but a clinical academic career has challenges. Given we all have limited time to spare, there is a tension between clinical and academic excellence. There is also tension when striving for academic excellence—should one become the world expert in a narrow research topic or have multiple research interests? We can only try and find the right balance. Also, as a clinical academic, there is more uncertainty about job security than in a clinical job. I love learning, and I personally prefer structured learning. This is why I have many degrees. My first clinical job after graduating in Germany involved cardiac magnetic resonance (CMR) research. I was lucky to be among the pioneers of the technique and to witness …

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task., 15 pages, 8 figures

Supplemental Digital Content is available in the text., Background: The genetic basis of left ventricular (LV) image-derived phenotypes, which play a vital role in the diagnosis, management, and risk stratification of cardiovascular diseases, is unclear at present. Methods: The LV parameters were measured from the cardiovascular magnetic resonance studies of the UK Biobank. Genotyping was done using Affymetrix arrays, augmented by imputation. We performed genome-wide association studies of 6 LV traits—LV end-diastolic volume, LV end-systolic volume, LV stroke volume, LV ejection fraction, LV mass, and LV mass to end-diastolic volume ratio. The replication analysis was performed in the MESA study (Multi-Ethnic Study of Atherosclerosis). We identified the candidate genes at genome-wide significant loci based on the evidence from extensive bioinformatic analyses. Polygenic risk scores were constructed from the summary statistics of LV genome-wide association studies to predict the heart failure events. Results: The study comprised 16 923 European UK Biobank participants (mean age 62.5 years; 45.8% men) without prevalent myocardial infarction or heart failure. We discovered 14 genome-wide significant loci (3 loci each for LV end-diastolic volume, LV end-systolic volume, and LV mass to end-diastolic volume ratio; 4 loci for LV ejection fraction, and 1 locus for LV mass) at a stringent P