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Aims Obstructive hypertrophic cardiomyopathy (oHCM) is characterized by dynamic obstruction of the left ventricular (LV) outflow tract (LVOT). Although this may be mediated by interplay between the hypertrophied septal wall, systolic anterior motion of the mitral valve, and papillary muscle abnormalities, the mechanistic role of LV shape is still not fully understood. This study sought to identify the LV end-diastolic morphology underpinning oHCM. Methods and results Cardiovascular magnetic resonance images from 2398 HCM individuals were obtained as part of the NHLBI HCM Registry. Three-dimensional LV models were constructed and used, together with a principal component analysis, to build a statistical shape model capturing shape variations. A set of linear discriminant axes were built to define and quantify (Z-scores) the characteristic LV morphology associated with LVOT obstruction (LVOTO) under different physiological conditions and the relationship between LV phenotype and genotype. The LV remodelling pattern in oHCM consisted not only of basal septal hypertrophy but a combination with LV lengthening, apical dilatation, and LVOT inward remodelling. Salient differences were observed between obstructive cases at rest and stress. Genotype negative cases showed a tendency towards more obstructive phenotypes both at rest and stress. Conclusions LV anatomy underpinning oHCM consists of basal septal hypertrophy, apical dilatation, LV lengthening, and LVOT inward remodelling. Differences between oHCM cases at rest and stress, as well as the relationship between LV phenotype and genotype, suggest different mechanisms for LVOTO. Proposed Z-scores render an opportunity of redefining management strategies based on the relationship between LV anatomy and LVOTO.

BackgroundLong Covid is associated with multiple symptoms and impairment in multiple organs. Cardiac impairment has been reported to varying degrees by varying methodologies in cross-sectional studies. Using cardiac magnetic resonance (CMR), we investigated the 12-month trajectory of cardiac impairment in individuals with Long Covid.Methods534 individuals with Long Covid underwent baseline CMR (T1 and T2 mapping, cardiac mass, volumes, function, and strain) and multi-organ MRI at 6 months (IQR 4.3,7.3) since first post-COVID-19 symptoms and 330 were rescanned at 12.6 (IQR 11.4, 14.2) months if abnormal findings were reported at baseline. Symptoms, standardised questionnaires, and blood samples were collected at both timepoints. Cardiac impairment was defined as one or more of: low left or right ventricular ejection fraction (LVEF and RVEF), high left or right ventricular end diastolic volume (LVEDV and RVEDV), low 3D left ventricular global longitudinal strain (GLS), or elevated native T1 in ≥3 cardiac segments. A significant change over time was reported by comparison with 92 healthy controls.ResultsThe technical success of this multiorgan assessment in non-acute settings was 99.1% at baseline, and 98.3% at follow up, with 99.6% and 98.8% for CMR respectively. Of individuals with Long Covid, 102/534 [19%] had cardiac impairment at baseline; 71/102 had complete paired data at 12 months. Of those, 58% presented with ongoing cardiac impairment at 12 months. High sensitivity cardiac troponin I and B-type natriuretic peptide were not predictive of CMR findings, symptoms, or clinical outcomes. At baseline, low LVEF, high RVEDV and low GLS were associated with cardiac impairment. Low LVEF at baseline was associated with persistent cardiac impairment at 12 months.ConclusionCardiac impairment, other than myocarditis, is present in 1 in 5 individuals with Long Covid at 6 months, persisting in over half of those at 12 months. Cardiac-related blood biomarkers are unable to identify cardiac impairment in Long COVID. Subtypes of disease (based on symptoms, examination, and investigations) and predictive biomarkers are yet to be established. Interventional trials with pre-specified subgroup analyses are required to inform therapeutic options.

Aims Data regarding the effects of regular alcohol consumption on cardiac anatomy and function are scarce. Therefore, we sought to determine the relationship between regular alcohol intake and cardiac structure and function as evaluated with cardiac magnetic resonance imaging. Methods and results Participants of the UK Biobank who underwent cardiac magnetic resonance were enrolled in our analysis. Data regarding regular alcohol consumption were obtained from questionnaires filled in by the study participants. Exclusion criteria were poor image quality, missing, or incongruent data regarding alcohol drinking habits, prior drinking, presence of heart failure or angina, and prior myocardial infarction or stroke. Overall, 4335 participants (61.5 ± 7.5 years, 47.6% male) were analysed. We used multivariate linear regression models adjusted for age, ethnicity, body mass index, smoking, hypertension, diabetes mellitus, physical activity, cholesterol level, and Townsend deprivation index to examine the relationship between regular alcohol intake and cardiac structure and function. In men, alcohol intake was independently associated with marginally increased left ventricular end-diastolic volume [β = 0.14; 95% confidence interval (CI) = 0.05–0.24; P = 0.004], left ventricular stroke volume (β = 0.08; 95% CI = 0.03–0.14; P = 0.005), and right ventricular stroke volume (β = 0.08; 95% CI = 0.02–0.13; P = 0.006). In women, alcohol consumption was associated with increased left atrium volume (β = 0.14; 95% CI = 0.04–0.23; P = 0.006). Conclusion Alcohol consumption is independently associated with a marginal increase in left and right ventricular volumes in men, but not in women, whereas alcohol intake showed an association with increased left atrium volume in women. Our results suggest that there is only minimal relationship between regular alcohol consumption and cardiac morphology and function in an asymptomatic middle-aged population.

Background and Objectives: Obstructive sleep apnea (OSA) is a common disorder with an increased risk for left ventricular and right ventricular dysfunction. Most studies to date have examined populations with manifest cardiovascular disease using echocardiography to analyze ventricular dysfunction with little or no reference to ventricular volumes or myocardial mass. Our aim was to explore these parameters with cardiac MRI. We hypothesized that there would be stepwise increase in left ventricular mass and right ventricular volumes from the unaffected, to the snoring and the OSA group. Materials and Methods: We analyzed cardiac MRI data from 4978 UK Biobank participants free from cardiovascular disease. Participants were allocated into three cohorts: with OSA, with self-reported snoring and without OSA or snoring (n = 118, 1886 and 2477). We analyzed cardiac parameters from balanced cine-SSFP sequences and indexed them to body surface area. Results: Patients with OSA were mostly males (47.3% vs. 79.7%, p &lt, 0.001) with higher body mass index (25.7 ± 4.0 vs. 31.3 ± 5.3 kg/m², 0.001 and higher blood pressure (135 ± 18 vs. 140 ± 17 mmHg, p = 0.012) compared to individuals without OSA or snoring. Regression analysis showed a significant effect for OSA in left ventricular end-diastolic index (LVEDVI) (β = −4.9 ± 2.4 mL/m², p = 0.040) and right ventricular end-diastolic index (RVEDVI) (β = −6.2 ± 2.6 mL/m², p = 0.016) in females and for right ventricular ejection fraction (RVEF) (β = 1.7 ± 0.8%, p = 0.031) in males. A significant effect was discovered in snoring females for left ventricular mass index (LVMI) (β = 3.5 ± 0.9 g/m², 0.001) and in males for left ventricular ejection fraction (LVEF) (β = 1.0 ± 0.3%, p = 0.001) and RVEF (β = 1.2 ± 0.3%, 0.001). Conclusion: Our study suggests that OSA is highly underdiagnosed and that it is an evolving process with gender specific progression. Females with OSA show significantly lower ventricular volumes while males with snoring show increased ejection fractions which may be an early sign of hypertrophy. Separate prospective studies are needed to further explore the direction of causality.

Background: Myocardial T1-mapping is increasingly used in multicentre studies and trials. Inconsistent image analysis introduces variability, hinders differentiation of diseases, and results in larger sample sizes. We present a systematic approach to standardize T1-map analysis by human operators to improve accuracy and consistency. Methods: We developed a multi-step training program for T1-map post-processing. The training dataset contained 42 left ventricular (LV) short-axis T1-maps (normal and diseases; 1.5 and 3 Tesla). Contours drawn by two experienced human operators served as reference for myocardial T1 and wall thickness (WT). Trainees (n = 26) underwent training and were evaluated by: (a) qualitative review of contours; (b) quantitative comparison with reference T1 and WT. Results: The mean absolute difference between reference operators was 8.4 ± 6.3 ms (T1) and 1.2 ± 0.7 pixels (WT). Trainees' mean discrepancy from reference in T1 improved significantly post-training (from 8.1 ± 2.4 to 6.7 ± 1.4 ms; p b 0.001), with a 43% reduction in standard deviation (SD) (p = 0.035). WT also improved significantly post-training (from 0.9 ± 0.4 to 0.7 ± 0.2 pixels, p = 0.036), with 47% reduction in SD (p = 0.04). These experimentally-derived thresholds served to guide the training process: T1 (±8 ms) and WT (±1 pixel) from reference. Conclusion: A standardized approach to CMR T1-map image post-processing leads to significant improvements in the accuracy and consistency of LV myocardial T1 values and wall thickness. Improving consistency between operators can translate into 33–72% reduction in clinical trial sample-sizes. This work may: (a) serve as a basis for re-certification for core-lab operators; (b) translate to sample-size reductions for clinical studies; (c) produce better-quality training datasets for machine learning.

Funding: UK Engineering and Physical Sciences Research Council [grant number EP/N509711/1 (J.K.)]. Chaste (Cancer, Heart And Soft Tissue Environment) is an open source simulation package for the numerical solution of mathematical models arising in physiology and biology. To date, Chaste development has been driven primarily by applications that include continuum modelling of cardiac electrophysiology (‘Cardiac Chaste’), discrete cell-based modelling of soft tissues (‘Cell-based Chaste’), and modelling of ventilation in lungs (‘Lung Chaste’). Cardiac Chaste addresses the need for a high-performance, generic, and verified simulation framewor kfor cardiac electrophysiology that is freely available to the scientific community. Cardiac chaste provides a software package capable of realistic heart simulations that is efficient, rigorously tested, and runs on HPC platforms. Cell-based Chaste addresses the need for efficient and verified implementations of cell-based modelling frameworks, providing a set of extensible tools for simulating biological tissues. Computational modelling, along with live imaging techniques, plays an important role in understanding the processes of tissue growth and repair. A wide range of cell-based modelling frameworks have been developed that have each been successfully applied in a range of biological applications. Cell-based Chaste includes implementations of the cellular automaton model, the cellular Potts model, cell-centre models with cell representations as overlapping spheres or Voronoi tessellations, and the vertex model. Lung Chaste addresses the need for a novel, generic and efficient lung modelling software package that is both tested and verified. It aims to couple biophysically-detailed models of airway mechanics with organ-scale ventilation models in a package that is freely available to the scientific community. Publisher PDF

Medical practice makes significant use of imaging scans such as Ultrasound or Magnetic Resonance Imaging as a diagnostic tool. They are used in the visual inspection or quantification of medical parameters computed from the images in post-processing. However, the value of such parameters depends much on the user's variability, device, and algorithmic differences. In this paper, we focus on quantifying the variability due to the human factor, which can be primarily addressed by the structured training of a human operator. We focus on a specific emerging cardiovascular MRI methodology, the T1 mapping, that has proven useful to identify a range of pathological alterations of the myocardial tissue structure. Training, especially in emerging techniques, is typically not standardized, varying dramatically across medical centers and research teams. Additionally, training assessment is mostly based on qualitative approaches. Our work aims to provide a software tool combining traditional clinical metrics and convolutional neural networks to aid the training process by gathering contours from multiple trainees, quantifying discrepancy from local gold standard or standardized guidelines, classifying trainees output based on critical parameters that affect contours variability.

Background: Diabetes mellitus (DM) is associated with increased risk of cardiovascular disease. Detection of early cardiac changes before manifest disease develops is important. We investigated early alterations in cardiac structure and function associated with DM using cardiovascular magnetic resonance imaging. Methods: Participants from the UK Biobank Cardiovascular Magnetic Resonance Substudy, a community cohort study, without known cardiovascular disease and left ventricular ejection fraction ≥50% were included. Multivariable linear regression models were performed. The investigators were blinded to DM status. Results: A total of 3984 individuals, 45% men, (mean [SD]) age 61.3 (7.5) years, hereof 143 individuals (3.6%) with DM. There was no difference in left ventricular (LV) ejection fraction (DM versus no DM; coefficient [95% CI]: −0.86% [−1.8 to 0.5]; P =0.065), LV mass (−0.13 g/m 2 [−1.6 to 1.3], P =0.86), or right ventricular ejection fraction (−0.23% [−1.2 to 0.8], P =0.65). However, both LV and right ventricular volumes were significantly smaller in DM, (LV end-diastolic volume/m 2 : −3.46 mL/m 2 [−5.8 to −1.2], P =0.003, right ventricular end-diastolic volume/m 2 : −4.2 mL/m 2 [−6.8 to −1.7], P =0.001, LV stroke volume/m 2 : −3.0 mL/m 2 [−4.5 to −1.5], P 2 : −3.8 mL/m 2 [−6.5 to −1.1], P =0.005), LV mass/volume: 0.026 (0.01 to 0.04) g/mL, P =0.006. Both left atrial and right atrial emptying fraction were lower in DM (right atrial emptying fraction: −6.2% [−10.2 to −2.1], P =0.003; left atrial emptying fraction:−3.5% [−6.9 to −0.1], P =0.043). LV global circumferential strain was impaired in DM (coefficient [95% CI]: 0.38% [0.01 to 0.7], P =0.045). Conclusions: In a low-risk general population without known cardiovascular disease and with preserved LV ejection fraction, DM is associated with early changes in all 4 cardiac chambers. These findings suggest that diabetic cardiomyopathy is not a regional condition of the LV but affects the heart globally.

Introduction Coffee is widely reported to be the world’s most popular drink and previous studies revealed acute increases in arterial stiffness with its consumption. But, the reported chronic effects of coffee on arterial stiffness are inconsistent and limited by modest number of studied subjects. This study aims to evaluate the association of coffee consumption on arterial stiffness using two forms of stiffness measures in a large population cohort. Aortic distensibility (AoD) is a local measure of arterial stiffness whilst arterial stiffness index (ASI) is a measure of wave reflection. Both measures have been shown to be predictors of cardiovascular events. Methods This cross-sectional cohort analysis comprised of 17,932 participants in the UK Biobank Imaging Study who underwent both cardiovascular magnetic resonance (CMR) imaging and pulse waveform measurements via finger probes. Participants with known cardiovascular disease were excluded. Coffee consumption habits were self-reported at the time of imaging and those who drink >25 cups/day were excluded. Coffee consumption was categorised into 3 groups (≤1, 1–3, >3 cups/day) with the lowest group used as the reference in the analyses. AoD was derived using an automated method to obtain the maximum and minimum luminal areas in the ascending (AA) and descending aorta (DA) from cine CMR images. ASI was calculated by dividing the participant’s height by the time interval between the peaks of the waveform recorded. AoD and ASI outliers (1.5x inter-quartile range rule) were excluded. Log transformation of AoD values was performed prior to regression analyses. Associations between coffee consumption and stiffness measures were assessed separately using univariate linear regression models adjusting for age, sex, ethnicity, Townsend deprivation index, current smoking, higher levels of education, height, weight, regular alcohol consumption (≥3 times/week), systolic blood pressure, resting heart rate, presence of hypertension, hypercholesterolaemia or diabetes, intake of vegetable, meat, water and tea consumption. Results Baseline characteristics of the 8,412 participants included in the final analyses are summarised in table 1. Moderate and heavy coffee drinkers were more likely to be male, smoke and consumed alcohol regularly. No significant differences were observed in the systolic blood pressures and heart rates between the groups. The unadjusted distributions of AoD in AA and DA were similar across the 3 groups (figure 1). Our regression models found no statistically significant differences in all three arterial stiffness measures for individuals who drink 1–3 cups or >3 cups of coffee/day compared with the reference group (table 2). Conclusion In this large middle-aged cohort without cardiovascular disease, moderate to heavy coffee consumption was not associated with significant changes in arterial stiffness measured by AoD and ASI compared with individuals who drink ≤1 cups of coffee/day. Conflict of Interest None

Background The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed to derive quantitative measures or biomarkers for further analyses. Manual inspection and visual QC of each segmentation result is not feasible at large scale. However, it is important to be able to automatically detect when a segmentation method fails in order to avoid inclusion of wrong measurements into subsequent analyses which could otherwise lead to incorrect conclusions. Methods To overcome this challenge, we explore an approach for predicting segmentation quality based on Reverse Classification Accuracy, which enables us to discriminate between successful and failed segmentations on a per-cases basis. We validate this approach on a new, large-scale manually-annotated set of 4800 cardiovascular magnetic resonance (CMR) scans. We then apply our method to a large cohort of 7250 CMR on which we have performed manual QC. Results We report results used for predicting segmentation quality metrics including Dice Similarity Coefficient (DSC) and surface-distance measures. As initial validation, we present data for 400 scans demonstrating 99% accuracy for classifying low and high quality segmentations using the predicted DSC scores. As further validation we show high correlation between real and predicted scores and 95% classification accuracy on 4800 scans for which manual segmentations were available. We mimic real-world application of the method on 7250 CMR where we show good agreement between predicted quality metrics and manual visual QC scores. Conclusions We show that Reverse classification accuracy has the potential for accurate and fully automatic segmentation QC on a per-case basis in the context of large-scale population imaging as in the UK Biobank Imaging Study.

Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies.

Recent advances in deep learning based image segmentation methods have enabled real-time performance with human-level accuracy. However, occasionally even the best method fails due to low image quality, artifacts or unexpected behaviour of black box algorithms. Being able to predict segmentation quality in the absence of ground truth is of paramount importance in clinical practice, but also in large-scale studies to avoid the inclusion of invalid data in subsequent analysis. In this work, we propose two approaches of real-time automated quality control for cardiovascular MR segmentations using deep learning. First, we train a neural network on 12,880 samples to predict Dice Similarity Coefficients (DSC) on a per-case basis. We report a mean average error (MAE) of 0.03 on 1,610 test samples and 97% binary classification accuracy for separating low and high quality segmentations. Secondly, in the scenario where no manually annotated data is available, we train a network to predict DSC scores from estimated quality obtained via a reverse testing strategy. We report an MAE=0.14 and 91% binary classification accuracy for this case. Predictions are obtained in real-time which, when combined with real-time segmentation methods, enables instant feedback on whether an acquired scan is analysable while the patient is still in the scanner. This further enables new applications of optimising image acquisition towards best possible analysis results., Accepted at MICCAI 2018

Introduction Aortic distensibility can be calculated using semi-automated methods to segment the aortic lumen on cine CMR (Cardiovascular Magnetic Resonance) images. However, these methods require visual quality control and manual localization of the region of interest (ROI) of ascending (AA) and proximal descending (PDA) aorta, which limit the analysis in large-scale population-based studies. Using 5100 scans from UK Biobank, this study sought to develop and validate a fully automated method to 1) detect and locate the ROIs of AA and PDA, and 2) provide a quality control mechanism. Methods The automated AA and PDA detection-localization algorithm followed these steps: 1) foreground segmentation; 2) detection of candidate ROIs by Circular Hough Transform (CHT); 3) spatial, histogram and shape feature extraction for candidate ROIs; 4) AA and PDA detection using Random Forest (RF); 5) quality control based on RF detection probability. To provide the ground truth, overall image quality (IQ = 0–3 from poor to good) and aortic locations were visually assessed by 13 observers. The automated algorithm was trained on 1200 scans and Dice Similarity Coefficient (DSC) was used to calculate the agreement between ground truth and automatically detected ROIs. Results The automated algorithm was tested on 3900 scans. Detection accuracy was 99.4% for AA and 99.8% for PDA. Aorta localization showed excellent agreement with the ground truth, with DSC ≥ 0.9 in 94.8% of AA (DSC = 0.97 ± 0.04) and 99.5% of PDA cases (DSC = 0.98 ± 0.03). AA×PDA detection probabilities could discriminate scans with IQ ≥ 1 from those severely corrupted by artefacts (AUC = 90.6%). If scans with detection probability < 0.75 were excluded (350 scans), the algorithm was able to correctly detect and localize AA and PDA in all the remaining 3550 scans (100% accuracy). Conclusion The proposed method for automated AA and PDA localization was extremely accurate and the automatically derived detection probabilities provided a robust mechanism to detect low quality scans for further human review. Applying the proposed localization and quality control techniques promises at least a ten-fold reduction in human involvement without sacrificing any accuracy.

UK Biobank, a large cohort study, plans to acquire 100,000 cardiac MRI studies by 2020. Although fully-automated left ventricular (LV) analysis was performed in the original acquisition, this was not designed for unsupervised incorporation into epidemiological studies. We sought to evaluate automated LV mass and volume (Siemens syngo InlineVF versions D13A and E11C), against manual analysis in a substantial sub-cohort of UK Biobank participants. Eight readers from two centers, trained to give consistent results, manually analyzed 4874 UK Biobank cases for LV end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF) and LV mass (LVM). Agreement between manual and InlineVF automated analyses were evaluated using Bland–Altman analysis and the intra-class correlation coefficient (ICC). Tenfold cross-validation was used to establish a linear regression calibration between manual and InlineVF results. InlineVF D13A returned results in 4423 cases, whereas InlineVF E11C returned results in 4775 cases and also reported LVM. Rapid visual assessment of the E11C results found 178 cases (3.7%) with grossly misplaced contours or landmarks. In the remaining 4597 cases, LV function showed good agreement: ESV −6.4 ± 9.0 ml, 0.853 (mean ± SD of the differences, ICC) EDV −3.0 ± 11.6 ml, 0.937; SV 3.4 ± 9.8 ml, 0.855; and EF 3.5 ± 5.1%, 0.586. Although LV mass was consistently overestimated (29.9 ± 17.0 g, 0.534) due to larger epicardial contours on all slices, linear regression could be used to correct the bias and improve accuracy. Automated InlineVF results can be used for case-control studies in UK Biobank, provided visual quality control and linear bias correction are performed. Improvements between InlineVF D13A and InlineVF E11C show the field is rapidly advancing, with further improvements expected in the near future.

We introduce a tool to reconstruct a geometrical surface mesh from sparse, heterogeneous, non coincidental contours and show its application to cardiac data. In recent years much research has looked at creating personalised 3D anatomical models of the heart. These models usually incorporate a geometrical reconstruction of the anatomy in order to understand better cardiovascular functions as well as predict different processes after a clinical event. The ability to accurately reconstruct heart anatomy from MRI in three dimensions commonly comes with fundamental challenges, notably the trade off between data fitting and regularization. Most current techniques requires data to be either parallel, or coincident, and bias the final result due to prior shape models or smoothing terms. Our approach uses a composition of smooth approximations towards the maximization of the data fitting. Assessment of our method was performed on synthetic data obtained from a mean cardiac shape model as well as on clinical data belonging to one normal subject and one affected by hypertrophic cardiomyopathy. Our method is both used on epicardial and endocardial left ventricle surfaces, but as well as on the right ven tricle.

In the last decade, electrophysiological models for in-silico simulations of cardiac electrophysiology have gained much attention in the research field. However, to translate them to clinical uses, the models need personalization based on recordings from the patient. In this work, we explore methodologies for the patient-specific personalization of torso and heart geometric models based on standard clinical cardiac magnetic resonance acquisitions to enable simulations. The inclusion of the torso and its internal structures allows simulations of the human ventricular electrophysiological activity from the ionic level to the body surface potentials and to the electrocardiogram.

Background Cardiovascular magnetic resonance (CMR) is the gold standard method for the assessment of cardiac structure and function. Reference ranges permit differentiation between normal and pathological states. To date, this study is the largest to provide CMR specific reference ranges for left ventricular, right ventricular, left atrial and right atrial structure and function derived from truly healthy Caucasian adults aged 45–74. Methods Five thousand sixty-five UK Biobank participants underwent CMR using steady-state free precession imaging at 1.5 Tesla. Manual analysis was performed for all four cardiac chambers. Participants with non-Caucasian ethnicity, known cardiovascular disease and other conditions known to affect cardiac chamber size and function were excluded. Remaining participants formed the healthy reference cohort; reference ranges were calculated and were stratified by gender and age (45–54, 55–64, 65–74). Results After applying exclusion criteria, 804 (16.2%) participants were available for analysis. Left ventricular (LV) volumes were larger in males compared to females for absolute and indexed values. With advancing age, LV volumes were mostly smaller in both sexes. LV ejection fraction was significantly greater in females compared to males (mean ± standard deviation [SD] of 61 ± 5% vs 58 ± 5%) and remained static with age for both genders. In older age groups, LV mass was lower in men, but remained virtually unchanged in women. LV mass was significantly higher in males compared to females (mean ± SD of 53 ± 9 g/m2 vs 42 ± 7 g/m2). Right ventricular (RV) volumes were significantly larger in males compared to females for absolute and indexed values and were smaller with advancing age. RV ejection fraction was higher with increasing age in females only. Left atrial (LA) maximal volume and stroke volume were significantly larger in males compared to females for absolute values but not for indexed values. LA ejection fraction was similar for both sexes. Right atrial (RA) maximal volume was significantly larger in males for both absolute and indexed values, while RA ejection fraction was significantly higher in females. Conclusions We describe age- and sex-specific reference ranges for the left ventricle, right ventricle and atria in the largest validated normal Caucasian population. Electronic supplementary material The online version of this article (doi:10.1186/s12968-017-0327-9) contains supplementary material, which is available to authorized users.

Subject-specific, or personalised, modelling is one of the main targets in current cardiac modelling research. The aim of this study is to assess the improvement in predictive power gained by introducing subject-specific fibre models within an electromechanical model of left ventricular contraction in rat. A quantitative comparison of a series of global rule-based fibre models with an image-based locally optimised fibre model was performed. Our results show small difference in the predicted values of ejection fraction, wall thickening and base-to-apex shortening between the fibre models considered. In comparison, much larger differences appear between predicted values and those measured in experimental images. Further study of the constitutive behaviour and architecture of cardiac tissue is required before electromechanical models can fully benefit from the introduction of subject-specific fibres. Additionally, our study shows that, in the current model, an orthotropic description of the tissue is preferable to a transversely isotropic one, for the metrics considered. © 2013 Springer-Verlag.