Publications
193 results found
Phua AIH, Le T-T, Tara SW, et al., 2020, Paradoxical higher myocardial wall stress and increased cardiac remodeling despite lower mass in females, Journal of the American Heart Association, Vol: 9, Pages: 1-7, ISSN: 2047-9980
BackgroundIncreased left ventricular (LV) mass is characterized by increased myocardial wall thickness and/or ventricular dilatation that is associated with worse outcomes. We aim to comprehensively compare sex‐stratified associations between measures of LV remodeling and increasing LV mass in the general population.Methods and ResultsParticipants were prospectively recruited in the National Heart Center Singapore Biobank to examine health and cardiovascular risk factors in the general population. Cardiovascular magnetic resonance was performed in all individuals. Participants with established cardiovascular diseases and abnormal cardiovascular magnetic resonance scan results were excluded. Global and regional measures of LV remodeling (geometry, function, interstitial volumes, and wall stress) were performed using conventional image analysis and novel 3‐dimensional machine learning phenotyping. Sex‐stratified analyses were performed in 1005 participants (57% males; 53±13 years). Age and prevalence of cardiovascular risk factors were well‐matched in both sexes (P>0.05 for all). Progressive increase in LV mass was associated with increased concentricity in either sex, but to a greater extent in females. Compared with males, females had higher wall stress (mean difference: 170 mm Hg, P<0.0001) despite smaller LV mass (42.4±8.2 versus 55.6±14.2 g/m2, P<0.0001), lower blood pressures (P<0.0001), and higher LV ejection fraction (61.9±5.9% versus 58.6±6.4%, P<0.0001). The regions of increased concentric remodeling corresponded to regions of increased wall stress. Compared with males, females had increased extracellular volume fraction (27.1±2.4% versus 25.1±2.9%, P<0.0001).ConclusionsCompared with males, females have lower LV mass, increased wall stress, and concentric remodeling. These findings provide mechanistic insights that females are susceptible to particular cardiovascular complications.
Esslinger U, Garnier S, Korniat A, et al., 2020, Correction: Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy., PLoS One, Vol: 15, Pages: e0229472-e0229472, ISSN: 1932-6203
[This corrects the article DOI: 10.1371/journal.pone.0172995.].
Mazzarotto F, Tayal U, Buchan RJ, et al., 2020, Re-evaluating the genetic contribution of monogenic dilated cardiomyopathy, Circulation, Vol: 141, Pages: 387-398, ISSN: 0009-7322
Background: Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 DCM patients across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60,706 individuals in order to identify clinically interpretable genes robustly associated with dominant monogenic DCM.Methods: We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 DCM patients and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 DCM patients sequenced in diagnostic laboratories and the ExAC database for replication and meta-analysis.Results: Truncating variants in TTN and DSP were associated with DCM in all comparisons. Variants in MYH7, LMNA, BAG3, TNNT2, TNNC1, PLN, ACTC1, NEXN, TPM1 and VCL were significantly enriched in specific patient subsets, with the last 2 genes potentially contributing primarily to early-onset forms of DCM. Overall, rare variants in these 12 genes potentially explained 17% of cases in the outpatient clinic cohort representing a broad range of adult DCM patients and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Whilst the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value since novel variants will be uninterpretable and their diagnostic yield is minimal.Conclusion: In the largest sequenced DCM cohort yet described, we observe robust disease association with 12 genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The
de Marvao A, Dawes TJ, Howard JP, et al., 2020, Artificial intelligence and the cardiologist: what you need to know for 2020., Heart, Vol: 106, Pages: 399-400, ISSN: 1355-6037
de Marvao A, Dawes TJW, O'Regan DP, 2020, Artificial intelligence for cardiac imaging-genetics research, Frontiers in Cardiovascular Medicine, Vol: 6, Pages: 1-10, ISSN: 2297-055X
Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide, with genotype being a significant influence on disease risk. Cardiac imaging-genetics aims to identify and characterize the genetic variants that influence functional, physiological, and anatomical phenotypes derived from cardiovascular imaging. High-throughput DNA sequencing and genotyping have greatly accelerated genetic discovery, making variant interpretation one of the key challenges in contemporary clinical genetics. Heterogeneous, low-fidelity phenotyping and difficulties integrating and then analyzing large-scale genetic, imaging and clinical datasets using traditional statistical approaches have impeded process. Artificial intelligence (AI) methods, such as deep learning, are particularly suited to tackle the challenges of scalability and high dimensionality of data and show promise in the field of cardiac imaging-genetics. Here we review the current state of AI as applied to imaging-genetics research and discuss outstanding methodological challenges, as the field moves from pilot studies to mainstream applications, from one dimensional global descriptors to high-resolution models of whole-organ shape and function, from univariate to multivariate analysis and from candidate gene to genome-wide approaches. Finally, we consider the future directions and prospects of AI imaging-genetics for ultimately helping understand the genetic and environmental underpinnings of cardiovascular health and disease.
Mazzarotto F, Hawley M, Beltrami M, et al., 2020, The genetic architecture of left ventricular non-compaction reveals both substantial overlap with other cardiomyopathies and a distinct aetiology in a subset of cases, Publisher: bioRxiv
Rationale: Left ventricular non-compaction (LVNC) is a condition characterised by trabeculations in the myocardial wall and is the subject of considerable conjecture as to whether it represents a distinct pathology or a secondary phenotype associated with other cardiac diseases, particularly cardiomyopathies. Objective: To investigate the genetic architecture of LVNC by identifying genes and variant classes robustly associated with disease and comparing these to other genetically characterised cardiomyopathies. Methods and Results: We performed rare variant association analysis using six different LVNC cohorts comprising 840 cases together with 125,748 gnomAD population controls and compared results to similar analyses with dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM) cases. We observed substantial overlap in genes and variant classes enriched in LVNC and DCM/HCM, indicating that in many cases LVNC belongs to a spectrum of more established cardiomyopathies, with non-compaction representing a phenotypic variation in patients with DCM- or HCM-causing variants. In contrast, five variant classes were uniquely enriched in LVNC cases, of which truncating variants in MYH7, ACTN2 and PRDM16 may represent a distinct LVNC aetiology. MYH7 truncating variants are generally considered as non-pathogenic but were detected in 2% of LVNC cases compared to 0.1% of controls, including a cluster of variants around a single splice region. Additionally, structural variants (exon deletions) in RYR2 and missense variants in the transmembrane region of HCN4 were enriched in LVNC cases, confirming prior reports regarding the association of these variant classes with combined LVNC and arrhythmia phenotypes. Conclusions: We demonstrated that genetic association analysis can clarify the relationship between LVNC and established cardiomyopathies, highlighted substantial overlap with DCM/HCM but also identified variant classes associated with distinct LVNC and with joint LVN
Biffi C, Doumou G, Duan J, et al., 2020, Explainable anatomical shape analysis through deep hierarchical generative models., Publisher: arXiv
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating highthroughput analysis of normal anatomy and pathology in largescale studies of volumetric imaging.
O'Regan DP, 2020, Putting machine learning into motion: applications in cardiovascular imaging, Clinical Radiology, Vol: 75, Pages: 33-37, ISSN: 0009-9260
Heart and circulatory diseases cause a quarter of all deaths in the UK and cardiac imaging offers an effective tool for early diagnosis and risk-stratification to improve premature death and disability. This domain of radiology is unique in that assessing flow and motion is essential for understanding and quantifying normal physiology and disease processes. Conventional image interpretation relies on manual analysis but this often fails to capture important prognostic features in the complex disturbances of cardiovascular physiology. Machine learning (ML) in cardiovascular imaging promises to be a transformative tool and addresses an unmet need for patient-specific management, accurate prediction of future events, and the discovery of tractable molecular mechanisms of disease. This review discusses the potential of ML across every aspect of image analysis including efficient acquisition, segmentation and motion tracking, disease classification, prediction tasks and modelling of genotype–phenotype interactions; however, significant challenges remain in access to high-quality data at scale, robust validation, and clinical interpretability.
Lee AWC, O'Regan DP, Gould J, et al., 2019, Sex-dependent QRS guidelines for cardiac resynchronization therapy using computer model predictions, Biophysical Journal, Vol: 117, Pages: 2375-2381, ISSN: 0006-3495
Cardiac resynchronization therapy (CRT) is an important treatment for heart failure. Low female enrollment in clinical trials means that current CRT guidelines may be biased toward males. However, females have higher response rates at lower QRS duration (QRSd) thresholds. Sex differences in the left ventricle (LV) size could provide an explanation for the improved female response at lower QRSd. We aimed to test if sex differences in CRT response at lower QRSd thresholds are explained by differences in LV size and hence predict sex-specific guidelines for CRT. We investigated the effect that LV size sex difference has on QRSd between male and females in 1093 healthy individuals and 50 CRT patients using electrophysiological computer models of the heart. Simulations on the healthy mean shape models show that LV size sex difference can account for 50–100% of the sex difference in baseline QRSd in healthy individuals. In the CRT patient cohort, model simulations predicted female-specific guidelines for CRT, which were 9–13 ms lower than current guidelines. Sex differences in the LV size are able to account for a significant proportion of the sex difference in QRSd and provide a mechanistic explanation for the sex difference in CRT response. Simulations accounting for the smaller LV size in female CRT patients predict 9–13 ms lower QRSd thresholds for female CRT guidelines.
Orini M, Graham AJ, MartinezNaharro A, et al., 2019, Noninvasive mapping of the electrophysiological substrate in cardiac amyloidosis and its relationship to structural abnormalities, Journal of the American Heart Association, Vol: 8, ISSN: 2047-9980
BackgroundThe relationship between structural pathology and electrophysiological substrate in cardiac amyloidosis is unclear. Differences between light‐chain (AL) and transthyretin (ATTR) cardiac amyloidosis may have prognostic implications.Methods and ResultsECG imaging and cardiac magnetic resonance studies were conducted in 21 cardiac amyloidosis patients (11 AL and 10 ATTR). Healthy volunteers were included as controls. With respect to ATTR, AL patients had lower amyloid volume (51.0/37.7 versus 73.7/16.4 mL, P=0.04), lower myocardial cell volume (42.6/19.1 versus 58.5/17.2 mL, P=0.021), and higher T1 (1172/64 versus 1109/80 ms, P=0.022) and T2 (53.4/2.9 versus 50.0/3.1 ms, P=0.003). ECG imaging revealed differences between cardiac amyloidosis and control patients in virtually all conduction‐repolarization parameters. With respect to ATTR, AL patients had lower epicardial signal amplitude (1.07/0.46 versus 1.83/1.26 mV, P=0.026), greater epicardial signal fractionation (P=0.019), and slightly higher dispersion of repolarization (187.6/65 versus 158.3/40 ms, P=0.062). No significant difference between AL and ATTR patients was found using the standard 12‐lead ECG. T1 correlated with epicardial signal amplitude (cc=−0.78), and extracellular volume with epicardial signal fractionation (cc=0.48) and repolarization time (cc=0.43). Univariate models based on single features from both cardiac magnetic resonance and ECG imaging classified AL and ATTR patients with an accuracy of 70% to 80%.ConclusionsIn this exploratory study cardiac amyloidosis was associated with ventricular conduction and repolarization abnormalities, which were more pronounced in AL than in ATTR. Combined ECG imaging–cardiac magnetic resonance analysis supports the hypothesis that additional mechanisms beyond infiltration may contribute to myocardial damage in AL amyloidosis. Further studies are needed to assess the clinical impact of this approach.
Duan J, Schlemper J, Qin C, et al., 2019, VS-Net: variable splitting network for accelerated parallel MRI reconstruction, International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 713-722, ISSN: 0302-9743
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data. We formulate the generalized parallel compressed sensing reconstruction as an energy minimization problem, for which a variable splitting optimization method is derived. Based on this formulation we propose a novel, end-to-end trainable deep neural network architecture by unrolling the resulting iterative process of such variable splitting scheme. VS-Net is evaluated on complex valued multi-coil knee images for 4-fold and 6-fold acceleration factors. We show that VS-Net outperforms state-of-the-art deep learning reconstruction algorithms, in terms of reconstruction accuracy and perceptual quality. Our code is publicly available at https://github.com/j-duan/VS-Net.
Duan J, Bello G, Schlemper J, et al., 2019, Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 2151-2164, ISSN: 0278-0062
Deep learning approaches have achieved state-of-the-art performance incardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular3D models, despite the artefacts in input CMR volumes.
Biffi C, Cerrolaza JJ, Tarroni G, et al., 2019, 3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders, 16th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1643-1646, ISSN: 1945-7928
Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 ).
Garcia-Pavia P, Kim Y, Restrepo-Cordoba MA, et al., 2019, Genetic variants associated with cancer therapy-induced cardiomyopathy, Circulation, Vol: 140, Pages: 31-41, ISSN: 0009-7322
BackgroundCancer therapy-induced cardiomyopathy (CCM) is associated with cumulative drug exposures and pre-existing cardiovascular disorders. These parametersincompletely account for substantial inter-individual susceptibility to CCM. We hypothesized that rare variants in cardiomyopathy genes contribute to CCM.MethodsWe studied 213 CCM patients from three cohorts: retrospectively recruited adults with diverse cancers (n=99), prospectively phenotyped breast cancer adults (n=73) and prospectively phenotyped children with acute myeloid leukemia (n=41). Cardiomyopathy genes, including nine pre-specified genes were sequenced. The prevalence of rare variants was compared between CCM cohorts and The Cancer Genome Atlas (TCGA) participants(n=2053), healthy volunteers(n=445), and ancestry-matchedreference population. Clinical characteristics and outcomes were assessed, stratified by genotypes. A prevalent CCM genotype was modeled in anthracycline-treated mice.ResultsCCM was diagnosed 0.4-9 years after chemotherapy; 90% of these patients received anthracyclines. Adult CCM patients had cardiovascular risk factors similar to the U.S. population. Among nine prioritized genes CCM patients had more rare protein-altering variants than comparative cohorts (p≤1.98e-04). Titin-truncating variants (TTNtv) predominated, occurring in 7.5% CCM patients versus 1.1% TCGA participants (p=7.36e-08), 0.7% healthy volunteers (p=3.42e-06), and 0.6% reference population (p=5.87e-14). Adult CCM patients with TTNtv experienced more heart failure and atrial fibrillation (p=0.003)and impaired myocardial recovery (p=0.03) than those without.Consistent with human data, anthracycline-treated TTNtv mice and isolated TTNtv cardiomyocytes showed sustained contractile dysfunction unlike wildtype (p=0.0004 and p<0.002, respectively).ConclusionsUnrecognized rare variants in cardiomyopathy-associated genes, particularly TTNtv, increased the risk for CCM in children and adults, and adverse cardiac events
O'Regan D, Dawes T, 2019, UK-Digital-Heart-Project/AutoFD: Optimized UKBB parallel distribution
Optimized UKBB parallel distribution with default parameters.The files pft_ExtractMatchedAndShiftedImages.m and pft_ExtractMatchedAndShiftedImages.new exist to accommodate different naming conventions for i/p files.This is the code being used for the Nature submission.Windows, Linux and MacOS platforms should be supported, but have not been exhaustively tested.
Pillinger T, Osimo EF, de Marvao A, et al., 2019, Cardiac structure and function in patients with schizophrenia taking antipsychotic drugs: an MRI study, Translational Psychiatry, Vol: 9, ISSN: 2158-3188
Cardiovascular disease (CVD) is a major cause of excess mortality in schizophrenia. Preclinical evidence shows antipsychotics can cause myocardial fibrosis and myocardial inflammation in murine models, but it is not known if this is the case in patients. We therefore set out to determine if there is evidence of cardiac fibrosis and/or inflammation using cardiac MRI in medicated patients with schizophrenia compared with matched healthy controls. Thirty-one participants (14 patients and 17 controls) underwent cardiac MRI assessing myocardial markers of fibrosis/inflammation, indexed by native myocardial T1 time, and cardiac structure (left ventricular (LV) mass) and function (left/right ventricular end-diastolic and end-systolic volumes, stroke volumes, and ejection fractions). Participants were physically fit, and matched for age, gender, smoking, blood pressure, BMI, HbA1c, ethnicity, and physical activity. Compared with controls, native myocardial T1 was significantly longer in patients with schizophrenia (effect size, d = 0.89; p = 0.02). Patients had significantly lower LV mass, and lower left/right ventricular end-diastolic and stroke volumes (effect sizes, d = 0.86-1.08; all p-values < 0.05). There were no significant differences in left/right end-systolic volumes and ejection fractions between groups (p > 0.05). These results suggest an early diffuse fibro-inflammatory myocardial process in patients that is independent of established CVD-risk factors and could contribute to the excess cardiovascular mortality associated with schizophrenia. Future studies are required to determine if this is due to antipsychotic treatment or is intrinsic to schizophrenia.
Attard M, Dawes T, Simoes Monteiro de Marvao A, et al., 2019, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: Three dimensional analysis of cardiac magnetic resonance imaging, EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging, Vol: 20, Pages: 668-676, ISSN: 2047-2412
AimsWe sought to identify metabolic pathways associated with right ventricular (RV) adaptation to pulmonary hypertension (PH). We evaluated candidate metabolites, previously associated with survival in pulmonary arterial hypertension, and used automated image segmentation and parametric mapping to model their relationship to adverse patterns of remodelling and wall stress.Methods and resultsIn 312 PH subjects (47.1% female, mean age 60.8 ± 15.9 years), of which 182 (50.5% female, mean age 58.6 ± 16.8 years) had metabolomics, we modelled the relationship between the RV phenotype, haemodynamic state, and metabolite levels. Atlas-based segmentation and co-registration of cardiac magnetic resonance imaging was used to create a quantitative 3D model of RV geometry and function—including maps of regional wall stress. Increasing mean pulmonary artery pressure was associated with hypertrophy of the basal free wall (β = 0.29) and reduced relative wall thickness (β = −0.38), indicative of eccentric remodelling. Wall stress was an independent predictor of all-cause mortality (hazard ratio = 1.27, P = 0.04). Six metabolites were significantly associated with elevated wall stress (β = 0.28–0.34) including increased levels of tRNA-specific modified nucleosides and fatty acid acylcarnitines, and decreased levels (β = −0.40) of sulfated androgen.ConclusionUsing computational image phenotyping, we identify metabolic profiles, reporting on energy metabolism and cellular stress-response, which are associated with adaptive RV mechanisms to PH.
Tarroni G, Oktay O, Bai W, et al., 2019, Learning-based quality control for cardiac MR images, IEEE Transactions on Medical Imaging, Vol: 38, Pages: 1127-1138, ISSN: 0278-0062
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully-automated, learning-based quality control pipeline for CMR images, specifically for short-axis image stacks. Our pipeline performs three important quality checks: 1) heart coverage estimation, 2) inter-slice motion detection, 3) image contrast estimation in the cardiac region. The pipeline uses a hybrid decision forest method - integrating both regression and structured classification models - to extract landmarks as well as probabilistic segmentation maps from both long- and short-axis images as a basis to perform the quality checks. The technique was tested on up to 3000 cases from the UK Biobank as well as on 100 cases from the UK Digital Heart Project, and validated against manual annotations and visual inspections performed by expert interpreters. The results show the capability of the proposed pipeline to correctly detect incomplete or corrupted scans (e.g. on UK Biobank, sensitivity and specificity respectively 88% and 99% for heart coverage estimation, 85% and 95% for motion detection), allowing their exclusion from the analysed dataset or the triggering of a new acquisition.
Mazzarotto F, Tayal P, Buchan R, et al., 2019, RE-EVALUATING THE GENETIC CONTRIBUTION OF MONOGENIC DILATED CARDIOMYOPATHY, Annual Conference of the British-Cardiovascular-Society (BCS) - Digital Health Revolution, Publisher: BMJ PUBLISHING GROUP, Pages: A100-A100, ISSN: 1355-6037
Grapsa I, Tan T, Nunes MDC, et al., 2019, IMPACT OF ADVERSE RIGHT VENTRICULAR REMODELING ON MORTALITY IN IDIOPATHIC PULMONARY ARTERIAL HYPERTENSION, 68th Annual Scientific Session and Expo of the American-College-of-Cardiology (ACC), Publisher: ELSEVIER SCIENCE INC, Pages: 1912-1912, ISSN: 0735-1097
Meyer HV, Dawes TJW, Serrani M, et al., 2019, Genomic analysis reveals a functional role for myocardial trabeculae in adults
<jats:title>ABSTRACT</jats:title><jats:p>Since being first described by Leonardo da Vinci in 1513 it has remained an enigma why the endocardial surfaces of the adult heart retain a complex network of muscular trabeculae – with their persistence thought to be a vestige of embryonic development. For causative physiological inference we harness population genomics, image-based intermediate phenotyping and <jats:italic>in silico</jats:italic> modelling to determine the effect of this complex cardiovascular trait on function. Using deep learning-based image analysis we identified genetic associations with trabecular complexity in 18,097 UK Biobank participants which were replicated in an independently measured cohort of 1,129 healthy adults. Genes in these associated regions are enriched for expression in the fetal heart or vasculature and implicate loci associated with haemodynamic phenotypes and developmental pathways. A causal relationship between increasing trabecular complexity and both ventricular performance and electrical activity are supported by complementary biomechanical simulations and Mendelian randomisation studies. These findings show that myocardial trabeculae are a previously-unrecognised determinant of cardiovascular physiology in adult humans.</jats:p>
O'Regan D, Ptokarcz, Meyer H, et al., 2019, ImperialCollegeLondon/fractalgenetics: fractalgenetics
Analysis of the genomic architecture and functional role of myocardial trabeculae
Bello G, Dawes T, Duan J, et al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol: 1, Pages: 95-104, ISSN: 2522-5839
Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C-index) was significantly higher (P = 0.0012) for our model C = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival.
Berry A, Jarral O, Dawes T, et al., 2019, Aortic root surgery is associated with deterioration in left ventricular function and physical quality of life, SCMR 22nd Annual Scientific Sessions
Shirvani S, Tokarczuk P, Statton B, et al., 2019, Motion-corrected multiparametric renal arterial spin labelling at 3T: Reproducibility and effect of vasodilator challenge, European Radiology, Vol: 29, Pages: 232-240, ISSN: 0938-7994
ObjectivesWe investigated the feasibility and reproducibility of free-breathing motion-corrected multiple inversion time (multi-TI) pulsed renal arterial spin labelling (PASL), with general kinetic model parametric mapping, to simultaneously quantify renal perfusion (RBF), bolus arrival time (BAT) and tissue T1.MethodsIn a study approved by the Health Research Authority, 12 healthy volunteers (mean age, 27.6 ± 18.5 years; 5 male) gave informed consent for renal imaging at 3 T using multi-TI ASL and conventional single-TI ASL. Glyceryl trinitrate (GTN) was used as a vasodilator challenge in six subjects. Flow-sensitive alternating inversion recovery (FAIR) preparation was used with background suppression and 3D-GRASE (gradient and spin echo) read-out, and images were motion-corrected. Parametric maps of RBF, BAT and T1 were derived for both kidneys. Agreement was assessed using Pearson correlation and Bland-Altman plots.ResultsInter-study correlation of whole-kidney RBF was good for both single-TI (r2 = 0.90), and multi-TI ASL (r2 = 0.92). Single-TI ASL gave a higher estimate of whole-kidney RBF compared to multi-TI ASL (mean bias, 29.3 ml/min/100 g; p <0.001). Using multi-TI ASL, the median T1 of renal cortex was shorter than that of medulla (799.6 ms vs 807.1 ms, p = 0.01), and mean whole-kidney BAT was 269.7 ± 56.5 ms. GTN had an effect on systolic blood pressure (p < 0.05) but the change in RBF was not significant.ConclusionsFree-breathing multi-TI renal ASL is feasible and reproducible at 3 T, providing simultaneous measurement of renal perfusion, haemodynamic parameters and tissue characteristics at baseline and during pharmacological challenge.
Duan J, Schlemper J, Bai W, et al., 2018, Combining deep learning and shape priors for bi-ventricular segmentation of volumetric cardiac magnetic resonance images, MICCAI ShapeMI Workshop, Publisher: Springer Verlag, Pages: 258-267, ISSN: 0302-9743
In this paper, we combine a network-based method with image registration to develop a shape-based bi-ventricular segmentation tool for short-axis cardiac magnetic resonance (CMR) volumetric images. The method first employs a fully convolutional network (FCN) to learn the segmentation task from manually labelled ground truth CMR volumes. However, due to the presence of image artefacts in the training dataset, the resulting FCN segmentation results are often imperfect. As such, we propose a second step to refine the FCN segmentation. This step involves performing a non-rigid registration with multiple high-resolution bi-ventricular atlases, allowing the explicit shape priors to be inferred. We validate the proposed approach on 1831 healthy subjects and 200 subjects with pulmonary hypertension. Numerical experiments on the two datasets demonstrate that our approach is capable of producing accurate, high-resolution and anatomically smooth bi-ventricular models, despite the artefacts in the input CMR volumes.
Dawes T, Simoes Monteiro de Marvao A, Shi W, et al., 2018, Identifying the optimal regional predictor of right ventricular global function: a high resolution 3D cardiac magnetic resonance study, Anaesthesia, Vol: 74, Pages: 312-320, ISSN: 0003-2409
Right ventricular (RV) function has prognostic value in acute, chronic and peri‐operative disease, although the complex RV contractile pattern makes rapid assessment difficult. Several two‐dimensional (2D) regional measures estimate RV function, however the optimal measure is not known. High‐resolution three‐dimensional (3D) cardiac magnetic resonance cine imaging was acquired in 300 healthy volunteers and a computational model of RV motion created. Points where regional function was significantly associated with global function were identified and a 2D, optimised single‐point marker (SPM‐O) of global function developed. This marker was prospectively compared with tricuspid annular plane systolic excursion (TAPSE), septum‐freewall displacement (SFD) and their fractional change (TAPSE‐F, SFD‐F) in a test cohort of 300 patients in the prediction of RV ejection fraction. RV ejection fraction was significantly associated with systolic function in a contiguous 7.3 cm2 patch of the basal RV freewall combining transverse (38%), longitudinal (35%) and circumferential (27%) contraction and coinciding with the four‐chamber view. In the test cohort, all single‐point surrogates correlated with RV ejection fraction (p < 0.010), but correlation (R) was higher for SPM‐O (R = 0.44, p < 0.001) than TAPSE (R = 0.24, p < 0.001) and SFD (R = 0.22, p < 0.001), and non‐significantly higher than TAPSE‐F (R = 0.40, p < 0.001) and SFD‐F (R = 0.43, p < 0.001). SPM‐O explained more of the observed variance in RV ejection fraction (19%) and predicted it more accurately than any other 2D marker (median error 2.8 ml vs 3.6 ml, p < 0.001). We conclude that systolic motion of the basal RV freewall predicts global function more accurately than other 2D estimators. However, no markers summarise 3D contractile patterns, limiting their predictive accuracy.
Jaijee SK, Quinlan M, Tokarczuk P, et al., 2018, Exercise cardiac MRI unmasks right ventricular dysfunction in acute hypoxia and chronic pulmonary arterial hypertension, AJP - Heart and Circulatory Physiology, Vol: 315, Pages: H950-H957, ISSN: 1522-1539
Background - Coupling of right ventricular (RV) contractility to afterload is maintained at rest in the early stages of pulmonary arterial hypertension (PAH), but exercise may unmask depleted contractile reserves. We assessed whether elevated afterload reduces RV contractile reserve despite compensated resting function using non-invasive exercise imaging. Methods and Results - Fourteen patients with PAH (mean age 39.1 years, 10 females) and 34 healthy control subjects (mean age 35.6 years, 17 females) completed real-time cardiac magnetic resonance imaging during sub-maximal exercise breathing room-air. Controls were then also exercised during acute normobaric hypoxia (FiO2 12%). RV contractile reserve was assessed by the effect of exercise on ejection fraction (RVEF). In control subjects the increase in RVEF on exercise was less during hypoxia (P=0.017), but the response of left ventricular ejection fraction to exercise did not change. Patients with PAH had impaired RV reserve with half demonstrating a fall in RVEF on exercise despite comparable resting function to controls (PAH: rest 53.6{plus minus}4.3% vs exercise 51.4{plus minus}10.7%; controls: rest 57.1{plus minus}5.2% vs exercise 69.6{plus minus}6.1%, P<0.0001). In control subjects the increase in stroke volume index (SVi) on exercise was driven by reduced RV end-systolic volume, whereas PAH patients did not augment SVi, with increases in both end-diastolic and end-systolic volumes. From baseline hemodynamic and exercise capacity variables only VE/VCO2 was an independent predictor of RV functional reserve (P=0.021). Conclusions - Non-invasive cardiac imaging during exercise unmasks depleted RV contractile reserves in healthy adults under hypoxic conditions and PAH patients under normoxic conditions despite preserved ejection fraction.
Tarroni G, Oktay O, Sinclair M, et al., 2018, A comprehensive approach for learning-based fully-automated inter-slice motion correction for short-axis cine cardiac MR image stacks, 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) / 8th Eurographics Workshop on Visual Computing for Biology and Medicine (VCBM), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 268-276, ISSN: 0302-9743
In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by inter-slice respiratory motion and will not correctly represent the cardiac volume, introducing potential errors in the following analyses and visualisations. We propose an approach to automatically correct inter-slice respiratory motion in SA CMR image stacks. Our approach makes use of probabilistic segmentation maps (PSMs) of the left ventricular (LV) cavity generated with decision forests. PSMs are generated for each slice of the SA stack and rigidly registered in-plane to a target PSM. If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks. The proposed approach was tested on a dataset of SA stacks acquired from 24 healthy subjects (for which anatomical 3D cardiac images were also available as reference) and compared to two techniques which use LA intensity images and LA segmentations as targets, respectively. The results show the accuracy and robustness of the proposed approach in motion compensation.
Biffi C, Oktay O, Tarroni G, et al., 2018, Learning interpretable anatomical features through deep generative models: Application to cardiac remodeling, International Conference On Medical Image Computing & Computer Assisted Intervention, Publisher: Springer, Pages: 464-471, ISSN: 0302-9743
Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.
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