133 results found
Biffi C, Cerrolaza Martinez JJ, Tarroni G, et al., 2020, Explainable anatomical shape analysis through deep hierarchical generative models, IEEE Transactions on Medical Imaging, Vol: 39, Pages: 2088-2099, ISSN: 0278-0062
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.
Meyer H, Dawes T, Serrani M, et al., Genetic and functional insights into the fractal structure of the heart, Nature, ISSN: 0028-0836
Bhuva AN, Treibel TA, De Marvao A, et al., 2020, Sex and regional differences inmyocardial plasticity in aortic stenosis are revealed by 3D modelmachine learning, EUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING, Vol: 21, Pages: 417-427, ISSN: 2047-2404
Grapsa J, Tan TC, Nunes MCP, et al., 2020, Prognostic impact of right ventricular mass change in patients with idiopathic pulmonary arterial hypertension, INTERNATIONAL JOURNAL OF CARDIOLOGY, Vol: 304, Pages: 172-174, ISSN: 0167-5273
Jarral OA, Tan MKH, Salmasi MY, et al., 2020, Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review, European Journal of Cardio-Thoracic Surgery, Vol: 57, Pages: 438-446, ISSN: 1010-7940
The death rate from thoracic aortic disease is on the rise and represents a growing global health concern as patients are often asymptomatic before acute events, which have devastating effects on health-related quality of life. Biomechanical factors have been found to play a major role in the development of both acquired and congenital aortic diseases. However, much is still unknown and translational benefits of this knowledge are yet to be seen. Phase-contrast cardiovascular magnetic resonance imaging of thoracic aortic blood flow has emerged as an exceptionally powerful non-invasive tool enabling visualization of complex flow patterns, and calculation of variables such as wall shear stress. This has led to multiple new findings in the areas of phenotype-dependent bicuspid valve flow patterns, thoracic aortic aneurysm formation and aortic prosthesis performance assessment. Phase-contrast cardiovascular magnetic resonance imaging has also been used in conjunction with computational fluid modelling techniques to produce even more sophisticated analyses, by allowing the calculation of haemodynamic variables with exceptional temporal and spatial resolution. Translationally, these technologies may potentially play a major role in the emergence of precision medicine and patient-specific treatments in patients with aortic disease. This clinically focused review will provide a systematic overview of key insights from published studies to date.
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.
Osimo EF, Brugger SP, de Marvao A, et al., 2020, Cardiac structure and function in schizophrenia: cardiac magnetic resonance imaging study., British Journal of Psychiatry, Pages: 1-8, ISSN: 0007-1250
BACKGROUND: Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia. AIMS: To investigate cardiac structure and function in individuals with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. METHOD: In total, 80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity and glycated haemoglobin levels. Individuals with schizophrenia ('patients') and controls were matched for age, gender, ethnicity and body surface area. RESULTS: Patients had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size d = -0.82, P = 0.001), LV end-systolic volume (d = -0.58, P = 0.02), LV stroke volume (d = -0.85, P = 0.001), right ventricular (RV) end-diastolic volume (d = -0.79, P = 0.002), RV end-systolic volume (d = -0.58, P = 0.02), and RV stroke volume (d = -0.87, P = 0.001) but unaltered ejection fractions relative to controls. LV concentricity (d = 0.73, P = 0.003) and septal thickness (d = 1.13, P < 0.001) were significantly larger in the patients. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. CONCLUSIONS: Individuals with schizophrenia show evidence of concentric cardiac remodelling compared with healthy controls of a similar age, gender, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in schizophrenia. Future studies should investigate the contribution of antipsychotic medication to these changes.
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.
Osimo E, Brugger S, De Marvao A, et al., Cardiac structure and function in schizophrenia: a cardiac MR imaging study, British Journal of Psychiatry, ISSN: 0007-1250
Background: Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia.Aims:We investigated cardiac structure and function in patients with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. Methods:80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity, and glycated haemoglobin levels. Patients and controls were matched for age, sex, ethnicity, and body surface area. Results:Patients with schizophrenia had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size, d=-0.82, p=0.001), LV end-systolic volume (d=-0.58, p=0.02), LV stroke volume (d=-0.85, p=0.001), right ventricular (RV) end-diastolic volume (d=-0.79, p=0.002), RV end-systolic volume (d=-0.58, p=0.02), and RV stroke volume (d=-0.87, p=0.001) but unaltered ejection fractions relative to controls. LV concentricity (d=0.73, p=0.003) and septal thickness (d=1.13, p<0.001) were significantly larger in schizophrenia. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. Conclusions:Patients with schizophrenia show evidence of concentric cardiac remodelling compared to healthy controls of a similar age, sex, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in patients. Future studies should investigate the contribution of antipsychotic medication to these changes.
Osimo EF, Brugger SP, de Marvao A, et al., 2019, Cardiac structure and function in schizophrenia: a cardiac MR imaging study, Publisher: Cold Spring Harbor Laboratory
<jats:p>Background: Heart disease is the leading cause of death in schizophrenia. Aims: We investigated cardiac structure and function in patients with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. Methods: 80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity, and glycated haemoglobin levels. Patients and controls were matched for age, sex, ethnicity, and body surface area. Results: Patients with schizophrenia had significantly smaller indexed left ventricular (LV) end-diastolic volume, end-systolic volume, stroke volume, right ventricular (RV) end-diastolic volume, end-systolic volume, and stroke volume but unaltered ejection fractions relative to controls. LV concentricity and septal thickness were significantly larger in schizophrenia. The findings were largely unchanged after adjusting for smoking or exercise levels and were independent of medication dose and duration. Conclusions: Patients with schizophrenia show evidence of prognostically-adverse cardiac remodelling compared to matched controls, independent of conventional risk factors.</jats:p>
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, 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, Publisher: Cold Spring Harbor Laboratory
<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., Aortic root surgery is associated with deterioration in left ventricular function and physical quality of life, SCMR 22nd Annual Scientific Sessions
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