After the 2014, European Society of Cardiology (ESC) guidelines on hypertrophic cardiomyopathy (HCM) were published, the European Association of Cardiovascular Imaging (EACVI) of the ESC developed the 2015 EACVI consensus paper on multi-modality imaging (MMI) in HCM, providing in-depth knowledge on the role of imaging in this disease. Since then, new evidence on HCM diagnosis, management and patient prognosis has accumulated, and the role of MMI has further expanded. Now that the 2023 ESC guidelines on cardiomyopathies have been published, a new EACVI document on MMI in HCM is needed, providing state-of-the-art, in-depth knowledge of imaging in HCM. The scope of this document is to focus on the role of the different imaging techniques in HCM in a logical, didactic and comprehensive way using a multi-modality approach. We provide our vision for future directions on MMI in HCM.

Aims:Hypertension-mediated left ventricular hypertrophy (LVH) phenotypes: normal left ventricle (LV), LV remodelling, eccentric and concentric LVH have been reported using cardiac magnetic resonance (CMR). Although previous smaller studies have explored associations of these phenotypes with select CMR metrics, large population-based longitudinal data comparing their clinical trajectories are lacking. This study aimed to evaluate CMR characteristics across hypertension-mediated LVH phenotypes and their associations with incident cardiovascular outcomes. Methods and results:In the UK Biobank imaging cohort, 24 463 hypertensives were categorized into LVH phenotypes using CMR. Logistic regression models explored the relationship between phenotypes, setting normal LV as the reference, and CMR parameters as exposures. Cox proportional hazard models evaluated associations with incident major adverse cardiovascular events (MACE) and separately heart failure over a median follow-up of 4.9 years. Among the participants, 23 206 had normal LV, 889 LV remodelling, 253 eccentric and 115 concentric LVH. Hypertensives with eccentric LVH had the most impaired LV function using ejection fraction and strain, and those with concentric LVH had the highest T1 values and maximal wall thickness. Hypertensives with eccentric LVH were associated with a 2.5 times higher rate of MACE (HR 2.5, CI: 1.7-3.8) and 9 times higher heart failure event rates (HR 9.0, CI: 5.7-14.2). Hypertensives with concentric LVH had 4.1 times higher heart failure events rates (HR 4.1, CI: 1.8-9.3), and no association with MACE. Conclusion:In this large population study, we found distinct differences in CMR characteristics between hypertension-mediated LVH phenotypes with eccentric and concentric LVH exhibiting the worst prognosis.

Patient-specific digital simulation is emerging as a tool to support personalized planning of transcatheter aortic valve replacement (TAVR), particularly as the procedure expands to younger, lower-risk patients, and more complex anatomies. Despite procedural advances, complications such as paravalvular leak, conduction disturbances, coronary obstruction, and aortic injury remain important determinants of outcome. Current pre-procedural planning relies heavily on computed tomography-based anatomical assessment, which is indispensable but largely static and cannot fully capture dynamic device-tissue interactions, and haemodynamic mechanisms underlying many procedural events. Computational modelling derived from patient-specific imaging can extend this assessment by simulating valve deployment, device-tissue contact, and flow, offering mechanistic insight and potential support for individualized procedural decision-making. This systematic review evaluates modelling approaches addressing TAVR complications and procedural planning, including high-risk scenarios such as bicuspid valves and valve-in-valve procedures. Across the literature, modelling enables patient-specific simulations and exploration of procedural strategies that may reduce complication risk. However, clinical translation remains limited by small study populations, heterogeneous methodologies, limited patient-specific validation, and lack of integration into routine workflows. Future progress will require validation against clinically meaningful endpoints, scalable digital infrastructure, and close collaboration between clinicians and engineers to incorporate simulation outputs into routine Heart Team decision-making.

AF can mediate left ventricular systolic dysfunction (LVSD) through a tachycardia-mediated cardiomyopathic process that may reverse with rate control alone. However, additional mechanisms contribute to AF-induced cardiomyopathy (AIC) that require rhythm control therapies. AIC can currently only be diagnosed retrospectively, as these component mechanisms are difficult to distinguish from each other and from other causes of LVSD prospectively. This narrative review considers the different potential mechanisms through which AF can impair ventricular function: rapid ventricular rate; irregularity of the ventricular rhythm; and impaired atrial contraction. How these features may exploit underlying structural vulnerability are considered and additional imaging-based parameters such as late gadolinium enhancement on cardiac MRI and contractile reserve during stress echocardiography are discussed. The limitations of existing parameters are discussed and a novel, non-parametric marker of ventricular rate with consideration of the inherent irregularity of AF - the Restitution Threshold Index (RTI) - is reviewed. Integrating RTI with these imaging-based measures may enhance clinical decision-making by more accurately identifying patients who would benefit from timely rhythm control. Further prospective validation is essential to develop accessible tools and an open-access RTI calculator has been made available (https://restitutionthreshold.com) to facilitate reproducibility and wider application.

BACKGROUND:Hypertrophic cardiomyopathy (HCM) is a heritable trait with marked variability in expression and outcomes. Our aims were to discover new genetic loci associated with HCM and to test the effect of a new polygenic risk score (PRS) on incidence, phenotype and outcomes stratified by genotype status. METHODS:A discovery genome-wide association study (GWAS) was performed on 2284 HCM cases and 4525 controls. Two fixed-effects meta-analyses combined our discovery GWAS with single-trait and multi-trait results from a published study. Discovered loci underwent comprehensive bioinformatic analysis including functional and druggability annotations. A PRS using loci from the two meta-analyses was evaluated for association with HCM diagnosis in 411 213 individuals from UK Biobank (UKBB); imaging phenotypes in individuals without HCM; a composite endpoint (including all-cause mortality and transplantation); and sudden cardiac death (SCD) in 1756 HCM cases. PRS analyses were stratified by genotype status. RESULTS:Three loci were found in the discovery GWAS (BAG3, FHOD3 and novel locus PPP1R3A). In the meta-analyses, 70 unique loci were identified, four novel (MYPN, YWHAE, NOS1AP and OBSCN). Bioinformatic analyses identified NOS1AP as a candidate HCM gene. A new PRS was significantly associated with HCM diagnosis (HR=3.19, 95% CI 2.46 to 4.14 for top 5% vs lower 95%; HR=1.88, 95% CI 1.72 to 2.06 per SD increase). Significant associations were found between PRS and greater left ventricular (LV) wall thickness and higher LV ejection fraction in UKBB participants without HCM. Genotype-negative HCM cases in the top 20% of the PRS distribution had an increased risk of SCD (HR=2.72, 95% CI 1.03 to 7.17). CONCLUSIONS:We report novel HCM loci. A new PRS predicted the risk of HCM development and associated imaging characteristics in the UKBB and outcomes in an HCM cohort.

The generation of geometric representations of the heart is essential for personalised approaches to cardiac assessment. Structured biventricular meshes customised to imaging data have demonstrated utility in a number of model-based applications that can provide more sensitive insights into patient health than routine cardiac indices alone. Cardiovascular magnetic resonance (CMR) imaging is a common starting point for the creation of digital twin geometries, with numerous published methods for mesh reconstruction. However, the majority of these methods are not open-source, are typically developed and validated using data from a single-centre, and lack deployability across heterogeneous scanning protocols and patient groups. We present an open-source, end-to-end pipeline (biv-me), to automatically generate time-varying biventricular meshes from cine CMR DICOM images, and perform external validation against a clinical reference software tool on 1313 CMR imaging studies across five publicly available datasets. We report excellent agreement in left and right ventricular indices and high scan-rescan reproducibility. Mesh generation was rapid, with a mean processing time of 2.5 min, and highly feasible, with 99% of meshes successfully generated to a high standard with median error of <1.5 mm. The biv-me pipeline - including code, models, and documentation - is available at https://github.com/UOA-Heart-Mechanics-Research/biv-me.

AIMS:Ischemic heart disease (IHD) patients undergo cardiovascular alterations that can accelerate heart ageing. Estimating biological heart age using advanced cardiac magnetic resonance (CMR) and electrocardiogram (ECG)-derived phenotypes provides a biomarker for heart ageing. We investigated the relationship of IHD and heart ageing using biological age estimation biomarkers, and the contribution of conventional cardiac imaging indices and vascular risk factors (VRFs). METHODS AND RESULTS:Heart age was estimated in prevalent IHD cases (n = 2,142) incorporating CMR radiomics and ECG features. Heart age gap (HAG), representing the disparity between predicted and actual heart age, was calculated. IHD subjects had significantly higher heart age compared to those without the disease (HAG: 1.55 years ± 5.66; p <0.001). The main radiomics and ECG features linked to heart ageing in IHD delineated a phenotype of structural and electrical remodelling not typically seen in the ageing heart. Conventional CMR indices accounted for only a negligible fraction of the association between IHD and HAG. Among the VRFs, adiposity and hypertension were significantly associated with increasing HAG in IHD. CONCLUSION:Individuals with IHD showed higher estimated heart ageing than controls, consistent with greater deviation from normal heart ageing, and this was associated with selected VRFs. This relationship was only minimally explained by conventional CMR indices, suggesting that the heart age model captures additional imaging and electrical features beyond standard CMR measures. HAG may offer an exploratory framework for characterising phenotypic heterogeneity in IHD and contextualising cardiovascular risk; however, prospective validation is required prior to clinical application.

Aims Left ventricular hypertrophy (LVH) is a strong predictor of cardiovascular disease. We previously compared supervised machine learning techniques to classify cardiac magnetic resonance (CMR)-derived LVH using electrocardiogram (ECG) and clinical variables in 37 534 UK Biobank participants, obtaining an area under the receiving operating curve (AUROC) of 0.85, but with limited specificity and requiring external validation. In this study, we develop a deep learning (DL) model to improve classification with external evaluation in the Study of Health in Pomerania (SHIP).Methods and results We analysed 12-lead ECGs of 48 835 participants from the UK Biobank imaging study. The dataset was split into a training set (70%), validation set (15%), and test set (15%) for performance evaluation. The model architecture was a fully convolutional network, for which the input was the participants' median ECG and clinical variables and the predicted indexed left ventricular mass (iLVM) as the output. A subsequent logistic regression model was used to recalibrate iLVM predictions. In UK Biobank, 717 (1.5%) participants had CMR-derived LVH and the AUROC for the DL model was 0.97. The ECG components most predictive of LVH were the QRS complex and ventricular rate. The DL model outperformed our supervised algorithms, previous DL modelling efforts and clinical ECG benchmarks. There was modest generalizability of the DL model to 1423 participants in SHIP (AUROC 0.78), with differences in clinical profile, ECG acquisition, and CMR labelling as important factors.Conclusion Our findings support the feasibility of scalable DL-based screening tools for the prediction of LVH from the ECG, whilst highlighting the need for model development using larger datasets with greater diversity to ensure generalizability.

The number of individuals engaging in sports continues to rise, and identifying those with cardiac substrates associated with increased risk of exercise-related adverse events is crucial. Athlete evaluation requires a refined diagnostic strategy to distinguish physiological cardiac remodelling from pathology. This joint European Association of Preventive Cardiology/European Association of Cardiovascular Imaging consensus provides a multimodality approach for advanced cardiovascular imaging in sports cardiology. Cardiovascular magnetic resonance, cardiac computed tomography, and nuclear imaging each offer complementary insights into cardiac structure, function, coronary anatomy, tissue characterization, perfusion, and inflammation. When integrated with clinical data and first-line tests, they improve diagnostic precision and risk stratification in scenarios frequently encountered in athletes, including ventricular arrhythmias, cardiomyopathies, congenital coronary anomalies, inflammatory myocardial disease, and coronary artery disease. Standardized protocols tailored to age, training, and clinical indication are essential to ensure reliability and avoid misinterpreting physiological adaptation as disease. The consensus emphasizes responsible reporting, considering performance and legal implications of diagnoses, and recommends second-line imaging when justified. Functional imaging, for ischaemia or inflammation, is central in guiding return-to-play decisions. Persistent evidence gaps include limited normative datasets across athletic subgroups and uncertain significance of subtle tissue abnormalities. Overall, this consensus supports harmonized, safe, and judicious multimodality imaging to protect athletes while preventing unnecessary sport restriction.

BACKGROUND AND AIMS:Adiposity exerts multisystem insults that influence multiple organs and physiological pathways. This underscores the need for a systems-level framework integrating key organ fat measurements to disentangle the heterogeneous pathways through which adiposity shapes differential cardiometabolic risk profiles. Such an approach could advance mechanistic understanding and enable more precise risk stratification. METHODS:In the UK Biobank, 24 935 participants without overt cardiac disease were studied. Six adiposity phenotypic groups were identified using unsupervised clustering of magnetic resonance imaging-derived adiposity measures (subcutaneous, visceral, pericardial, liver, pancreatic, and muscle fat). These phenotypes were characterised in terms of body composition, cardiac remodelling, and the development of major cardiometabolic diseases. RESULTS:Each phenotype showed distinct organ-dominant fat accumulation and body composition pattern. A pancreatic fat dominant phenotype, marked by visceral adiposity and sarcopenic features, showed a cardiorenal-metabolic risk profile. A muscle fat dominant phenotype, characterised by subcutaneous adiposity and sarcopenic features, was associated with increased heart failure risk. While pericardial fat dominant and liver fat dominant phenotypes did not associate with cardiac disease risk, they exhibited distinctive cardiac remodelling patterns, revealing phenotypespecific metabolic-cardiac interactions. Lastly, normal-weight individuals with mild multi-organ fat showed elevated chronic ischaemic heart disease risk, highlighting the value of phenotype-based risk assessment beyond general weight measures. CONCLUSIONS:Distinct patterns of multi-organ fat accumulation were associated with differential body composition, cardiac remodelling, and cardiometabolic disease risk profiles. The identified adiposity phenotypic groups capture clinically meaningful heterogeneity across the cardiorenal-metabolic spectrum and may inform future personalised, multisystem approaches to prevention and management.

The UK Biobank Imaging Study, with its dedicated cardiovascular magnetic resonance substudy, has redefined the scale and scope of cardiovascular research, generating high-quality imaging in 100 000 participants with linkage to rich genetic, demographic, lifestyle, and clinical data. The resource has enabled transformative discoveries across genomics, epidemiology, and biomedical engineering and has served as a global blueprint for population imaging studies. Its success has been accelerated by an equitable data access model that fosters international collaboration. The UK Biobank cardiovascular magnetic resonance experience illustrates the power of large-scale imaging cohorts and sets a benchmark for future initiatives aimed at improving cardiovascular health through integrated, collaborative science. Looking ahead, efforts should focus on harmonization across cohorts, adherence to rigorous methodological standards, and multidisciplinary collaboration to drive meaningful clinical translation. This article provides an overview of the UK Biobank and its cardiovascular magnetic resonance substudy, systematically reviews publications to date, discusses limitations and methodological considerations, and highlights future directions.

Accurate reconstruction of cardiac anatomy from sparse clinical images remains a major challenge in patient-specific modeling. While neural implicit functions have previously been applied to this task, their application to mapping anatomical consistency across subjects has been limited. In this work, we introduce Neural Implicit Heart Coordinates (NIHCs), a standardized implicit coordinate system, based on universal ventricular coordinates, that provides a common anatomical reference frame for the human heart. Our method predicts NIHCs directly from a limited number of 2D segmentations (sparse acquisition) and subsequently decodes them into dense 3D segmentations and high-resolution meshes at arbitrary output resolution. Trained on a large dataset of 5000 cardiac meshes, the model achieves high reconstruction accuracy on clinical contours, with mean Euclidean surface errors of 2.51 ± 0.33 mm in a diseased cohort (n=4549) and 2.31 ± 0.36 mm in a healthy cohort (n=5576). The NIHC representation enables anatomically coherent reconstruction even under severe slice sparsity and segmentation noise, faithfully recovering complex structures such as the valve planes. Compared with traditional pipelines, inference time is reduced from over 60 s to 5-15 s. These results demonstrate that NIHCs constitute a robust and efficient anatomical representation for patient-specific 3D cardiac reconstruction from minimal input data. The code is available at https://github.com/marsof97/NIHC.

Background: Genetic studies using cardiac magnetic resonance (CMR) imaging have identified loci related to cardiac shape, but most focus on static morphology. The value of a dynamic cardiac shape atlas capturing both shape and function remains unknown. Methods: A dynamic shape atlas comprising CMR-derived shape models at end-diastole and end-systole was combined with genetic and outcome data in 36,992 UK Biobank participants. Dynamic shape principal components (PCs) describing >1% of variance were characterized, and tested for associations with prevalent and incident cardiometabolic diseases, including ischemic heart disease (IHD), heart failure (HF), significant atrioventricular block (AVB), and atrial fibrillation (AF), and independent predictive power alongside standard CMR measures. Genome-wide association studies (GWAS) were performed to identify candidate genes and biological pathways, and polygenic risk scores (PRS) were assessed for disease associations. Mendelian randomization (MR) was performed to test causality of observed disease associations. Results: We identified 14 dynamic cardiac shape PCs capturing 83.3% of total dynamic cardiac shape variance. These PCs captured distinct functional remodeling patterns such as variation in annular plane systolic excursion, while remaining only modestly correlated with standard CMR measures. All 14 PCs were associated with at least one incident cardiometabolic disease, with the strongest associations observed for incident IHD, HF, and AVB. Notably, incorporating dynamic shape PCs improved the prediction of incident IHD beyond standard CMR measures. GWAS identified 75 genetic loci associated with dynamic shape, including 14 previously unreported for cardiac traits, and candidate genes demonstrated enrichment in pathways related to cardiac development and contractile function. PRS derived from dynamic shape loci were significantly associated with multiple outcomes, most prominently HF. MR identified significant causal relationships between several PCs and cardiometabolic disease. Conclusions: Dynamic cardiac shape features capture aspects of cardiac structure and function not fully represented by standard CMR measures. These features are strongly associated with incident cardiometabolic disease and provide new insights into the genetic architecture of cardiac remodeling. Keywords: biventricular, dynamic cardiac shape and function, cardiometabolic disease, genome-wide association study

Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular (LV) displacement—without requiring inference-time optimisation. Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86 ∼ 380× faster than the most accurate baseline. These results highlight the suitability of INR-based models for accurate and scalable analysis of myocardial strain in large CMR datasets. www.github.com/andrewjackbell/Displacement-INR .

Cardiac cine magnetic resonance imaging (MRI) is central to functional cardiac assessment, yet a full current cine sequence may not always be directly available at the point of analysis. We introduce Chain of Flow (COF), an electrocardiography (ECG)-conditioned framework that combines patient-specific MRI and current ECG for subject-specific 4D cardiac cine generation. On the UK Biobank dataset, COF achieves strong image-level fidelity and downstream function-oriented performance on a shared same-visit evaluable benchmark. Multi-slice and multi-resolution analyses indicate stable structural generation quality across the short-axis stack and heterogeneous acquisition resolutions. Controlled phase-robustness analyses across resampled input MRI phases further provide same-visit proxy support for patient-specific MRI plus current ECG when a target MRI phase is not directly observed. A cross-visit route provides exploratory serial evidence, with the clearest gains in current-facing region-of-interest readout. Disease-category functional audits, case-level volume-trajectory evidence review further delineate where the current patient-specific MRI plus ECG formulation remains stable for anatomy-aware downstream cardiac analysis. Code is available at https://anonymous.4open.science/r/COF-paper-release-C88B.

AIMS:This study aimed to determine the impact of left ventricular mass (LVM) on discordant stress cardiac magnetic resonance (CMR) imaging and invasive coronary angiography (ICA) in patients with suspected coronary artery disease (CAD) at coronary computed tomography angiography (CCTA). METHODS AND RESULTS:In this substudy of the Dan-NICAD 2 trial (NCT03481712), 354 patients with suspected obstructive CAD on CCTA were examined with both rest and stress CMR and ICA for invasive physiological measurements. An abnormal stress CMR was defined as ≥2 contiguous segments with a stress perfusion defect, late gadolinium enhancement, or wall motion abnormality. CMR-derived LVM was sex-adjusted by conversion from grams to per cent. Haemodynamically obstructive CAD at ICA was defined as visual diameter stenosis >90% or FFR ≤0.80. LVM was higher in patients with an abnormal stress CMR compared to those with a normal CMR (median difference = 8.0%, P < 0.001). Patients with or without haemodynamically obstructive CAD had similar LVM (median difference = 2%, P = 0.222). Within four binary groups based on normal/abnormal stress CMR and ICA, both median LVM and index of microvascular resistance were higher in patients with discordant abnormal stress CMR and normal ICA than in patients with concordant normal stress CMR and ICA (124% vs. 111%, P = 0.001, and 29 vs. 19, P = 0.072, respectively). CONCLUSION:In patients with suspected obstructive CAD, increased LVM can potentially confound concordance between stress CMR and ICA. This is due to increased microvascular resistance, which decreases the pressure gradient across an epicardial stenosis, resulting in a false high FFR and thus, normal ICA.

BACKGROUND:Obesity is a major contributor to cardiovascular disease (CVD). Different fat depots may have distinct effects on cardiac ageing and cardiovascular risk. We examined associations of imaging-defined obesity phenotypes with biological heart ageing and incident CVD and evaluated whether they provide additional information beyond anthropometric measures. METHODS:This study included UK Biobank participants without CVD, with cardiac and abdominal MRI and linked health records. Biological heart age was estimated using machine learning from 56 cardiac MRI phenotypes. Abdominal visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT) and pericardial adipose tissue (PAT) were clustered via K-means to identify distinct adiposity phenotypes. Mediation analysis quantified the contribution of individual fat compartments to incident CVD through biological heart ageing. Findings were compared with anthropometric measures. Analyses were sex-stratified. RESULTS:The analysis included 34 496 participants (55%, n=18 978 females) with an average age of 63.5 years. Median body mass index (BMI) was 25.7 kg/m2, with 58.4% classed as overweight or obese. Imaging-defined adiposity clustering identified a high-risk phenotype (comprising higher VAT, ASAT and PAT levels) associated with greater heart ageing and a lower-risk phenotype linked with reduced ageing. Imaging-defined adiposity clusters outperformed BMI categories in their association with biological heart age. In mediation analyses, VAT explained 14% of its association with incident CVD through accelerated heart ageing, the strongest mediation effect across all fat depots. PAT showed similar mediation (10.7%), but this fell to <1% after adjustment for VAT. Associations were more pronounced among males. CONCLUSION:Among the adiposity compartments studied, VAT showed the strongest relationships with cardiovascular ageing and incident CVD. While PAT showed initial associations, these were not independent of VAT. Imaging-defined adiposity phenotypes can allow more precise definition of cardiovascular risk and may enhance mechanistic understanding of obesity-related CVD, including sex-specific susceptibilities.