Imperial College London

DrDeclanO'Regan

Faculty of MedicineInstitute of Clinical Sciences

Reader in Imaging Sciences
 
 
 
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Contact

 

+44 (0)20 3313 1510declan.oregan

 
 
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Location

 

Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

133 results found

Shirvani S, Tokarczuk P, Statton B, Quinlan M, Berry A, Tomlinson J, Weale P, Kuhn B, O'Regan DPet 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.

Journal article

Dawes T, Simoes Monteiro de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det 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.

Journal article

Jaijee SK, Quinlan M, Tokarczuk P, Clemence M, Howard L, Gibbs JSR, O'Regan DPet 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.

Journal article

Tarroni G, Oktay O, Sinclair M, Bai W, Schuh A, Suzuki H, de Marvao A, O'Regan D, Cook S, Rueckert Det 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.

Conference paper

Biffi C, Oktay O, Tarroni G, Bai W, De Marvao A, Doumou G, Rajchl M, Bedair R, Prasad S, Cook S, O’Regan D, Rueckert Det 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.

Conference paper

Duan J, Schlemper J, Bai W, Dawes TJW, Bello G, Doumou G, De Marvao A, O'Regan DP, Rueckert Det al., 2018, Deep Nested Level Sets: Fully Automated Segmentation of Cardiac MR Images in Patients with Pulmonary Hypertension, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Pages: 595-603, ISSN: 0302-9743

Conference paper

Dawes T, Cai J, Quinlan M, Simoes Monteiro de Marvao A, Ostowski P, Tokarczuk P, Watson G, Wharton J, Howard L, Gibbs J, Cook S, Wilkins M, O'Regan DPet al., 2018, Fractal analysis of right ventricular trabeculae in pulmonary hypertension, Radiology, Vol: 288, Pages: 386-395, ISSN: 0033-8419

Purpose: To measure right ventricular (RV) trabecular complexity by its fractal dimension (FD) in healthy subjects and patients with pulmonary hypertension (PH) and assess its relationship to hemodynamic and functional parameters, and future cardiovascular events. Materials and methods: This retrospective study used data acquired from May 2004 to October 2013 for 256 patients with newly-diagnosed PH that underwent cardiac magnetic resonance (CMR) imaging, right heart catheterization and six-minute walk distance testing with a median follow-up of 4.0 years. 256 healthy controls underwent CMR only. Biventricular FD, volumes and function were assessed on short-axis cine images. Reproducibility was assessed by intraclass correlation coefficient, correlation between variables was assessed by Pearson’s correlation test, and mortality prediction compared by univariable and multivariable Cox regression analysis. Results: RV-FD reproducibility had an intraclass correlation coefficient of 0.97 (95% confidence interval [CI]: 0.96, 0.98).RV-FD was higher in PH patients than healthy subjects (median 1.310, inter-quartile range [IQR] 1.281-1.341 vs 1.264, 1.242-1.295, P<.001) with the greatest difference near the apex. RV-FD was associated with pulmonary vascular resistance (r=0.30, P<.001). In univariable Cox regression analysis, RV-FD was a significant predictor of death (hazards ratio [HR]: 1.256, CI: 1.011, 1.560, P=.04), but in a multivariable analysis did not predict survival independently of conventional parameters of RV remodeling (HR: 1.179, CI: 0.871, 1.596, P=0.29). Conclusion: Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in PH associated with afterload, although the gain in survival prediction over traditional markers is not significant.

Journal article

Dawes TJW, Serrani M, Bai W, Cai J, Suzuki H, de Marvao A, Quinlan M, Tokarczuk P, Ostrowski P, Matthews P, Rueckert D, Cook S, Costantino ML, O'Regan Det al., 2018, Myocardial trabeculae improve left ventricular function: a combined UK Biobank and computational analysis, GAT Annual Scientific Meeting 2018, Publisher: Association of Anaesthetists of Great Britain and Ireland

Conference paper

Xu XY, Pirola S, Jarral O, O'Regan D, Asimakopoulos G, Anderson JR, Pepper J, Athanasiou Tet al., 2018, Computational study of aortic hemodynamics for patients with an abnormal aortic valve: the importance of secondary flow at the ascending aorta inlet, APL Bioengineering, Vol: 2, Pages: 026101-1-026101-14, ISSN: 2473-2877

Blood flow in the aorta is helical, but most computational studies ignore the presence of secondary flow components at the ascending aorta (AAo) inlet. The aim of this study is to ascertain the importance of inlet boundary conditions (BCs) in computational analysis of flow patterns in the thoracic aorta based on patient-specific images, with a particular focus on patients with an abnormal aortic valve. Two cases were studied: one presenting a severe aortic valve stenosis and the other with a mechanical valve. For both aorta models, three inlet BCs were compared; these included the flat profile and 1D through-plane velocity and 3D phase-contrast magnetic resonance imaging derived velocity profiles, with the latter being used for benchmarking. Our results showed that peak and mean velocities at the proximal end of the ascending aorta were underestimated by up to 41% when the secondary flow components were neglected. The results for helical flow descriptors highlighted the strong influence of secondary velocities on the helical flow structure in the AAo. Differences in all wall shear stress (WSS)-derived indices were much more pronounced in the AAo and aortic arch (AA) than in the descending aorta (DAo). Overall, this study demonstrates that using 3D velocity profiles as inlet BC is essential for patient-specific analysis of hemodynamics and WSS in the AAo and AA in the presence of an abnormal aortic valve. However, predicted flow in the DAo is less sensitive to the secondary velocities imposed at the inlet; hence, the 1D through-plane profile could be a sufficient inlet BC for studies focusing on distal regions of the thoracic aorta.

Journal article

Ware JS, Amor-Salamanca A, Tayal U, Govind R, Serrano I, Salazar-Mendiguchia J, Garcia-Pinilla JM, Pascual-Figal DA, Nunez J, Guzzo-Merello G, Gonzalez-Vioque E, Bardaji A, Manito N, Lopez-Garrido MA, Padron-Barthe L, Edwards E, Whiffin N, Walsh R, Buchan RJ, Midwinter W, Wilk A, Prasad S, Pantazis A, Baski J, O'Regan DP, Alsonso-Pulpon A, Cook SA, Lara-Pezzi E, Barton PJ, Garcia-Pavia Pet al., 2018, A genetic etiology for alcohol-induced cardiac toxicity, Journal of the American College of Cardiology, Vol: 71, Pages: 2293-2302, ISSN: 0735-1097

Background: Alcoholic cardiomyopathy (ACM) is defined by a dilated and impaired left ventricle due to chronic excess alcohol consumption. It is largely unknown what factors determine cardiac toxicity on exposure to alcohol.Objectives: We sought to evaluate the role of variation in cardiomyopathy-associated genes in the pathophysiology of ACM, and to examine the effects of alcohol intake and genotype on DCM severity.Methods: We characterized 141 ACM cases, 716 dilated cardiomyopathy (DCM) cases and 445 healthy volunteers. We compared the prevalence of rare, protein-altering variants in 9 genes associated with inherited DCM. We evaluated the effect of genotype and alcohol-consumption on phenotype in DCM.Results: Variants in well-characterized DCM-causing genes were more prevalent in patients with ACM than controls (13.5% vs 2.9%; P=1.2e-05), but similar between patients with ACM and DCM (19.4%; P=0.12) and with a predominant burden of Titin-truncating variants (TTNtv, 9.9%). Separately, we identified an interaction between TTN genotype and excess alcohol consumption in a cohort of DCM patients not meeting ACM criteria. On multivariate analysis, DCM patients with a TTNtv who consumed excess alcohol had an 8.7% absolute reduction in ejection fraction (95% CI -2.3 to -15.1, P<0.007) compared with those without TTNtv and excess alcohol consumption. The presence of TTNtv did not predict phenotype, outcome or functional recovery on treatment in ACM patients. Conclusions: TTNtv represent a prevalent genetic predisposition for ACM, and are also associated with a worse LVEF in DCM patients who consume alcohol above recommended levels. Familial evaluation and genetic testing should be considered in patients presenting with ACM.

Journal article

de Marvao A, Biffi C, Walsh R, Doumou G, Dawes T, Shi W, Bai W, Berry A, Buchan R, Pierce I, Tokarczuk P, Statton B, Francis C, Duan J, Quinlan M, Felkin L, Le T-T, Bhuva A, Tang HC, Barton P, Chin CW-L, Rueckert D, Ware J, Prasad S, O'Regan DP, Cook SAet al., 2018, Defining The Effects Of Genetic Variation Using Machine Learning Analysis Of CMRs: A Study In Hypertrophic Cardiomyopathy And In A Healthy Population, Joint Meeting of the British-Society-of-Cardiovascular-Imaging/British-Society-of-Cardiovascular-CT, British-Society-of-Cardiovascular-Magnetic-Resonance and British-Nuclear-Cardiac-Society on British Cardiovascular Imaging, Publisher: BMJ PUBLISHING GROUP, Pages: A7-A8, ISSN: 1355-6037

Conference paper

Whiffin N, walsh R, Govind R, Edwards M, Ahmad M, Zhang X, Tayal U, Buchan R, Midwinter W, Wilk A, Najgebauer H, Francis C, Wilkinson S, Monk T, Brett L, O'Regan D, Prasad S, Morris-Rosendahl D, Barton P, Edwards E, Ware J, Cook Set al., 2018, CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation, Genetics in Medicine, Vol: 20, Pages: 1246-1254, ISSN: 1098-3600

PurposeInternationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (http://www.cardioclassifier.org), a semiautomated decision-support tool for inherited cardiac conditions (ICCs).MethodsCardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.ResultsWe benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P = 1.1  ×  10−18), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.ConclusionCardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.

Journal article

Cai J, Bryant JA, Thu-Thao L, Su B, de Marvao A, O'Regan DP, Cook SA, Chin CW-Let al., 2017, Fractal analysis of left ventricular trabeculations is associated with impaired myocardial deformation in healthy Chinese, Journal of Cardiovascular Magnetic Resonance, Vol: 19, ISSN: 1097-6647

Background:Left ventricular (LV) non-compaction (LVNC) is defined by extreme LV trabeculation, but is measured variably. Here we examined the relationship between quantitative measurement in LV trabeculation and myocardial deformation in health and disease and determined the clinical utility of semi-automated assessment of LV trabeculations.Methods:Cardiovascular magnetic resonance (CMR) was performed in 180 healthy Singaporean Chinese (age 20–69 years; males, n = 91), using balanced steady state free precession cine imaging at 3T. The degree of LV trabeculation was assessed by fractal dimension (FD) as a robust measure of trabeculation complexity using a semi-automated technique. FD measures were determined in healthy men and women to derive normal reference ranges. Myocardial deformation was evaluated using feature tracking. We tested the utility of this algorithm and the normal ranges in 10 individuals with confirmed LVNC (non-compacted/compacted; NC/C ratio > 2.3 and ≥1 risk factor for LVNC) and 13 individuals with suspected disease (NC/C ratio > 2.3).Results:Fractal analysis is a reproducible means of assessing LV trabeculation extent (intra-class correlation coefficient: intra-observer, 0.924, 95% CI [0.761–0.973]; inter-observer, 0.925, 95% CI [0.821–0.970]). The overall extent of LV trabeculation (global FD: 1.205 ± 0.031) was independently associated with increased indexed LV end-diastolic volume and mass (sβ = 0.35; p < 0.001 and sβ = 0.13; p < 0.01, respectively) after adjusting for age, sex and body mass index. Increased LV trabeculation was independently associated with reduced global circumferential strain (sβ = 0.17, p = 0.013) and global diastolic circumferential and radial strain rates (sβ = 0.25, p < 0.001 and sβ = −0.15, p 

Journal article

Suzuki HS, Gao HG, Bai WB, Evangelou EE, Glocker BG, O'regan DO, Elliott PE, Matthews PMM, Suzuki HS, Gao HG, Bai WB, Evangelou EE, Glocker BG, ORegan DPO, Elliott PE, Matthews PMMet al., 2017, Abnormal brain white matter microstructure is associated withboth pre-hypertension and hypertension, PLoS ONE, Vol: 12, ISSN: 1932-6203

ObjectivesTo characterize effects of chronically elevated blood pressure on the brain, we tested for brain white matter microstructural differences associated with normotension, pre-hypertension and hypertension in recently available brain magnetic resonance imaging data from 4659 participants without known neurological or psychiatric disease (62.3±7.4 yrs, 47.0% male) in UK Biobank.MethodsFor assessment of white matter microstructure, we used measures derived from neurite orientation dispersion and density imaging (NODDI) including the intracellular volume fraction (an estimate of neurite density) and isotropic volume fraction (an index of the relative extra-cellular water diffusion). To estimate differences associated specifically with blood pressure, we applied propensity score matching based on age, sex, educational level, body mass index, and history of smoking, diabetes mellitus and cardiovascular disease to perform separate contrasts of non-hypertensive (normotensive or pre-hypertensive, N = 2332) and hypertensive (N = 2337) individuals and of normotensive (N = 741) and pre-hypertensive (N = 1581) individuals (p<0.05 after Bonferroni correction).ResultsThe brain white matter intracellular volume fraction was significantly lower, and isotropic volume fraction was higher in hypertensive relative to non-hypertensive individuals (N = 1559, each). The white matter isotropic volume fraction also was higher in pre-hypertensive than in normotensive individuals (N = 694, each) in the right superior longitudinal fasciculus and the right superior thalamic radiation, where the lower intracellular volume fraction was observed in the hypertensives relative to the non-hypertensive group.SignificancePathological processes associated with chronically elevated blood pressure are associated with imaging differences suggesting chronic alterations of white matter axonal structure that may affect cognitive functions even with pre-hypertension.

Journal article

Michelakis ED, Gurtu V, Webster L, Barnes G, Watson G, Howard L, Cupitt J, Paterson I, Thompson RB, Chow K, O'Regan DP, Zhao L, Wharton J, Kiely DG, Kinnaird A, Boukouris AE, White C, Nagendran J, Freed DH, Wort SJ, Gibbs JSR, Wilkins MRet al., 2017, Inhibition of pyruvate dehydrogenase kinase improves pulmonary arterial hypertension in genetically susceptible patients, Science Translational Medicine, Vol: 9, ISSN: 1946-6234

Pulmonary arterial hypertension (PAH) is a progressive vascular disease with a high mortality rate. It is characterized by an occlusive vascular remodeling due to a pro-proliferative and antiapoptotic environment in the wall of resistance pulmonary arteries (PAs). Proliferating cells exhibit a cancer-like metabolic switch where mitochondrial glucose oxidation is suppressed, whereas glycolysis is up-regulated as the major source of adenosine triphosphate production. This multifactorial mitochondrial suppression leads to inhibition of apoptosis and downstream signaling promoting proliferation. We report an increase in pyruvate dehydrogenase kinase (PDK), an inhibitor of the mitochondrial enzyme pyruvate dehydrogenase (PDH, the gatekeeping enzyme of glucose oxidation) in the PAs of human PAH compared to healthy lungs. Treatment of explanted human PAH lungs with the PDK inhibitor dichloroacetate (DCA) ex vivo activated PDH and increased mitochondrial respiration. In a 4-month, open-label study, DCA (3 to 6.25 mg/kg b.i.d.) administered to patients with idiopathic PAH (iPAH) already on approved iPAH therapies led to reduction in mean PA pressure and pulmonary vascular resistance and improvement in functional capacity, but with a range of individual responses. Lack of ex vivo and clinical response was associated with the presence of functional variants of SIRT3 and UCP2 that predict reduced protein function. Impaired function of these proteins causes PDK-independent mitochondrial suppression and pulmonary hypertension in mice. This first-in-human trial of a mitochondria-targeting drug in iPAH demonstrates that PDK is a druggable target and offers hemodynamic improvement in genetically susceptible patients, paving the way for novel precision medicine approaches in this disease.

Journal article

Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert D, Oktay O, Ferrante E, Kamnitsas K, Heinrich M, Bai W, Caballero J, Cook S, de Marvao A, Dawes T, O'Regan D, Kainz B, Glocker B, Rueckert Det al., 2017, Anatomically Constrained Neural Networks (ACNN): application to cardiac image enhancement and segmentation, IEEE Transactions on Medical Imaging, Vol: 37, Pages: 384-395, ISSN: 0278-0062

Incorporation of prior knowledge about organ shape and location is key to improve performance of image analysis approaches. In particular, priors can be useful in cases where images are corrupted and contain artefacts due to limitations in image acquisition. The highly constrained nature of anatomical objects can be well captured with learning based techniques. However, in most recent and promising techniques such as CNN based segmentation it is not obvious how to incorporate such prior knowledge. State-of-the-art methods operate as pixel-wise classifiers where the training objectives do not incorporate the structure and inter-dependencies of the output. To overcome this limitation, we propose a generic training strategy that incorporates anatomical prior knowledge into CNNs through a new regularisation model, which is trained end-to-end. The new framework encourages models to follow the global anatomical properties of the underlying anatomy (e.g. shape, label structure) via learnt non-linear representations of the shape. We show that the proposed approach can be easily adapted to different analysis tasks (e.g. image enhancement, segmentation) and improve the prediction accuracy of the state-of-the-art models. The applicability of our approach is shown on multi-modal cardiac datasets and public benchmarks. Additionally, we demonstrate how the learnt deep models of 3D shapes can be interpreted and used as biomarkers for classification of cardiac pathologies.

Journal article

Biffi C, Simoes Monteiro de Marvao A, Attard M, Dawes T, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook S, Rueckert D, O'Regan DPet al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework, Bioinformatics, ISSN: 1367-4803

Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for highthroughput mapping of genotype-phenotype associations in three dimensions (3D).Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1,124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts.Availability: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work.

Journal article

Whiffin N, Walsh R, Govind R, Edwards M, Ahmad M, Zhang X, Tayal U, Buchan R, Midwinter W, Wilk AE, Najgebauer H, Francis C, Wilkinson S, Monk T, Brett L, O'Regan DP, Prasad SK, Morris-Rosendahl DJ, Barton PJR, Edwards E, Ware JS, Cook SAet al., 2017, CardioClassifier – demonstrating the power of disease- and gene-specific computational decision support for clinical genome interpretation, Publisher: Cold Spring Harbor Laboratory

<jats:title>ABSTRACT</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>Internationally-adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://www.cardioclassifier.org">www.cardioclassifier.org</jats:ext-link>), a semi-automated decision-support tool for inherited cardiac conditions (ICCs).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support varian interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We benchmarked CardioClassifier on 57 expertly-curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically-actionable variants (64/219 vs 156/219, Fisher’s <jats:bold>P</jats:bold>=1.1x10-18), with important false positives; illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually-curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data.</jats:p></jats:sec><jats:sec><jat

Working paper

Suzuki H, Gao H, Bai W, Evangelou E, Glocker B, O'Regan DP, Elliott P, Matthews PMet al., 2017, Hypertension and white matter microstructures in healthy participants in UK Biobank, Publisher: OXFORD UNIV PRESS, Pages: 248-249, ISSN: 0195-668X

Conference paper

Dawes T, de Marvao A, Shi W, Rueckert D, Cook S, O'Regan Det al., 2017, Systolic motion of the basal right ventricular freewall is the strongest predictor of global function: a high resolution 3D imaging study, Association-of-Anaesthetists-of-Great-Britain-and-Ireland (AAGBI) GAT Annual Scientific Meeting, Publisher: Wiley, Pages: 77-77, ISSN: 0003-2409

Conference paper

Pirola S, Cheng Z, Jarral OA, O'Regan DP, Pepper JR, Athanasiou T, Xu XYet al., 2017, On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamics, Journal of Biomechanics, Vol: 60, Pages: 15-21, ISSN: 1873-2380

Boundary conditions (BCs) are an essential part in computational fluid dynamics (CFD) simulations of blood flow in large arteries. Although several studies have investigated the influence of BCs on predicted flow patterns and hemodynamic wall parameters in various arterial models, there is a lack of comprehensive assessment of outlet BCs for patient-specific analysis of aortic flow. In this study, five different sets of outlet BCs were tested and compared using a subject-specific model of a normal aorta. Phase-contrast magnetic resonance imaging (PC-MRI) was performed on the same subject and velocity profiles extracted from the in vivo measurements were used as the inlet boundary condition. Computational results obtained with different outlet BCs were assessed in terms of their agreement with the PC-MRI velocity data and key hemodynamic parameters, such as pressure and flow waveforms and wall shear stress related indices. Our results showed that the best overall performance was achieved by using a well-tuned three-element Windkessel model at all model outlets, which not only gave a good agreement with in vivo flow data, but also produced physiological pressure waveforms and values. On the other hand, opening outlet BCs with zero pressure at multiple outlets failed to reproduce any physiologically relevant flow and pressure features.

Journal article

Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, Glocker B, de Marvao A, O'Regan D, Cook S, Rueckert Det al., 2017, Learning-based heart coverage estimation for short-axis cine cardiac MR images, Functional Imaging and Modelling of the Heart (FIMH), Publisher: Springer, Pages: 73-82

The correct acquisition of short axis (SA) cine cardiac MRimage stacks requires the imaging of the full cardiac anatomy betweenthe apex and the mitral valve plane via multiple 2D slices. While in theclinical practice the SA stacks are usually checked qualitatively to en-sure full heart coverage, visual inspection can become infeasible for largeamounts of imaging data that is routinely acquired, e.g. in populationstudies such as the UK Biobank (UKBB). Accordingly, we propose alearning-based technique for the fully-automated estimation of the heartcoverage for SA image stacks. The technique relies on the identificationof cardiac landmarks (i.e. the apex and the mitral valve sides) on twochamber view long axis images and on the comparison of the landmarks’positions to the volume covered by the SA stack. Landmark detection isperformed using a hybrid random forest approach integrating both re-gression and structured classification models. The technique was appliedon 3000 cases from the UKBB and compared to visual assessment. Theobtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicatethat the proposed technique is able to correctly detect the vast majorityof the cases with insufficient coverage, suggesting that it could be usedas a fully-automated quality control step for CMR SA image stacks.

Conference paper

Dawes T, Simoes monteiro de marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs J, Rueckert D, Cook S, Wilkins M, O'Regan DPet al., 2017, Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, Vol: 283, Pages: 381-390, ISSN: 1527-1315

Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.

Journal article

Esslinger U, Garnier S, Korniat A, Proust C, Kararigas G, Mueller-Nurasyid M, Empana J-P, Morley MP, Perret C, Stark K, Bick AG, Prasad SK, Kriebel J, Li J, Tiret L, Strauch K, O'Regan DP, Marguiles KB, Seidman JG, Boutouyrie P, Lacolley P, Jouven X, Hengstenberg C, Komajda M, Hakonarson H, Isnard R, Arbustini E, Grallert H, Cook SA, Seidman CE, Regitz-Zagrosek V, Cappola TP, Charron P, Cambien F, Villard Eet al., 2017, Exome-wide association study reveals novel susceptibility genes to sporadic dilated cardiomyopathy, PLOS ONE, Vol: 12, ISSN: 1932-6203

Aims:Dilated cardiomyopathy (DCM) is an important cause of heart failure with a strong familial component. We performed an exome-wide array-based association study (EWAS) to assess the contribution of missense variants to sporadic DCM.Methods and results:116,855 single nucleotide variants (SNVs) were analyzed in 2796 DCM patients and 6877 control subjects from 6 populations of European ancestry. We confirmed two previously identified associations with SNVs in BAG3 and ZBTB17 and discovered six novel DCM-associated loci (Q-value<0.01). The lead-SNVs at novel loci are common and located in TTN, SLC39A8, MLIP, FLNC, ALPK3 and FHOD3. In silico fine mapping identified HSPB7 as the most likely candidate at the ZBTB17 locus. Rare variant analysis (MAF<0.01) demonstrated significant association for TTN variants only (P = 0.0085). All candidate genes but one (SLC39A8) exhibit preferential expression in striated muscle tissues and mutations in TTN, BAG3, FLNC and FHOD3 are known to cause familial cardiomyopathy. We also investigated a panel of 48 known cardiomyopathy genes. Collectively, rare (n = 228, P = 0.0033) or common (n = 36, P = 0.019) variants with elevated in silico severity scores were associated with DCM, indicating that the spectrum of genes contributing to sporadic DCM extends beyond those identified here.Conclusion:We identified eight loci independently associated with sporadic DCM. The functions of the best candidate genes at these loci suggest that proteostasis regulation might play a role in DCM pathophysiology.

Journal article

Le T-T, Bryant JA, Ting AE, Ho PY, Su B, Teo RCC, Gan JS-J, Chung Y-C, O'Regan DP, Cook SA, Chin CW-Let al., 2017, Assessing exercise cardiac reserve using real-time cardiovascular magnetic resonance, Journal of Cardiovascular Magnetic Resonance, Vol: 19, ISSN: 1532-429X

BackgroundExercise cardiovascular magnetic resonance (ExCMR) has great potential for clinical use but its development has been limited by a lack of compatible equipment and robust real-time imaging techniques. We developed an exCMR protocol using an in-scanner cycle ergometer and assessed its performance in differentiating athletes from non-athletes.MethodsFree-breathing real-time CMR (1.5T Aera, Siemens) was performed in 11 athletes (5 males; median age 29 [IQR: 28–39] years) and 16 age- and sex-matched healthy volunteers (7 males; median age 26 [interquartile range (IQR): 25–33] years). All participants underwent an in-scanner exercise protocol on a CMR compatible cycle ergometer (Lode BV, the Netherlands), with an initial workload of 25W followed by 25W-increment every minute. In 20 individuals, exercise capacity was also evaluated by cardiopulmonary exercise test (CPET). Scan-rescan reproducibility was assessed in 10 individuals, at least 7 days apart.ResultsThe exCMR protocol demonstrated excellent scan-rescan (cardiac index (CI): 0.2 ± 0.5L/min/m2) and inter-observer (ventricular volumes: 1.2 ± 5.3mL) reproducibility. CI derived from exCMR and CPET had excellent correlation (r = 0.83, p < 0.001) and agreement (1.7 ± 1.8L/min/m2). Despite similar values at rest (P = 0.87), athletes had increased exercise CI compared to healthy individuals (at peak exercise: 12.2 [IQR: 10.2–13.5] L/min/m2 versus 8.9 [IQR: 7.5–10.1] L/min/m2, respectively; P < 0.001). Peak exercise CI, where image acquisition lasted 13–17 s, outperformed that at rest (c-statistics = 0.95 [95% confidence interval: 0.87–1.00] versus 0.48 [95% confidence interval: 0.23–0.72], respectively; P < 0.0001 for comparison) in differentiating athletes from healthy volunteers; and had similar performance as VO2max (c-statistics = 0.84 [95% confidence interval = 0.62–1.00]; P = 0.29 for comparison).ConclusionsWe have developed a nov

Journal article

Quinlan M, Jaijee S, Marvao AD, Tokarczuk PF, Gibbs JSR, O'Regan Det al., 2016, Exercise CMR: real-time assessment of cardiac performance with phase contrast imaging, Journal of Cardiovascular Magnetic Resonance, Vol: 18

Journal article

Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, Rackham O, van Heesch S, Pua CJ, Kui M, Walsh R, Tayal U, Prasad SK, Dawes TJW, Ko NSJ, Sim D, Chan LLH, Chin CWL, Mazzarotto F, Barton PJ, Kreuchwig F, de Kleijn DPV, Totman T, Biffi C, Tee N, Rueckert D, Schneider V, Faber A, Regitz-Zagrosek V, Seidman JG, Seidman CE, Linke WA, Kovalik J, O'Regan D, Ware JS, Hubner N, Cook SAet al., 2016, Titin truncating variants affect heart function in disease cohorts and the general population, Nature Genetics, Vol: 49, Pages: 46-53, ISSN: 1546-1718

Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ~1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn, TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.

Journal article

Oktay O, Bai W, Guerrero R, Rajchl M, de Marvao A, O Regan D, Cook S, Heinrich M, Glocker B, Rueckert D, Oktay O, Bai W, Guerrero R, Rajchl M, de Marvao A, O'Regan D, Cook S, Heinrich M, Glocker B, Rueckert Det al., 2016, Stratified decision forests for accurate anatomical landmark localization, IEEE Transactions on Medical Imaging, Vol: 36, Pages: 332-342, ISSN: 0278-0062

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy.

Journal article

Jaijee S, Statton B, Quinlan M, Berry A, Tokarczuk P, Murphy K, Tighe H, Lawlee E, Diamond T, Garcia LM, Howard L, O'Regan DP, Gibbs JSRet al., 2016, Right ventricular function in acute and chronic pulmonary hypertension using exercise cardiac magnetic resonance imaging, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 1186-1186, ISSN: 0195-668X

Conference paper

Schafer S, De Marvao A, Adami E, Ng WM, Fiedler L, Khin E, O'Regan D, Ware J, Hubner N, Cook SAet al., 2016, Titin truncations cause penetrant cardiac phenotypes in disease and the general population, Congress of the European-Society-of-Cardiology (ESC), Publisher: European Society of Cardiology, Pages: 1408-1408, ISSN: 0195-668X

Conference paper

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