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
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122 results found

Jarral OA, Tan MKH, Salmasi MY, Pirola S, Pepper JR, O'Regan DP, Xu XY, Athanasiou Tet al., 2019, Phase-contrast magnetic resonance imaging and computational fluid dynamics assessment of thoracic aorta blood flow: a literature review, European Journal of Cardio-Thoracic Surgery, 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.

Journal article

Osimo EF, Brugger SP, de Marvao A, Pillinger T, Whitehurst T, Statton B, Quinlan M, Berry A, Cook SA, O'Regan DP, Howes ODet al., 2019, Cardiac structure and function in schizophrenia: a cardiac MR imaging study

<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>

Journal article

Orini M, Graham AJ, MartinezNaharro A, Andrews CM, de Marvao A, Statton B, Cook SA, O'Regan DP, Hawkins PN, Rudy Y, Fontana M, Lambiase PDet 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.

Journal article

Duan J, Bello G, Schlemper J, Bai W, Dawes TJW, Biffi C, Marvao AD, Doumou G, O'Regan DP, Rueckert Det al., 2019, Automatic 3D bi-ventricular segmentation of cardiac images by a shape-constrained 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.

Journal article

Biffi C, Cerrolaza JJ, Tarroni G, de Marvao A, Cook SA, O'Regan DP, Rueckert Det 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 ).

Conference paper

Bhuva AN, Treibel TA, De Marvao A, Biffi C, Dawes TJW, Doumou G, Bai W, Patel K, Boubertakh R, Rueckert D, O'Regan DP, Hughes AD, Moon JC, Manisty CHet al., 2019, Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning., Eur Heart J Cardiovasc Imaging

AIMS: Left ventricular hypertrophy (LVH) in aortic stenosis (AS) varies widely before and after aortic valve replacement (AVR), and deeper phenotyping beyond traditional global measures may improve risk stratification. We hypothesized that machine learning derived 3D LV models may provide a more sensitive assessment of remodelling and sex-related differences in AS than conventional measurements. METHODS AND RESULTS: One hundred and sixteen patients with severe, symptomatic AS (54% male, 70 ± 10 years) underwent cardiovascular magnetic resonance pre-AVR and 1 year post-AVR. Computational analysis produced co-registered 3D models of wall thickness, which were compared with 40 propensity-matched healthy controls. Preoperative regional wall thickness and post-operative percentage wall thickness regression were analysed, stratified by sex. AS hypertrophy and regression post-AVR was non-uniform-greatest in the septum with more pronounced changes in males than females (wall thickness regression: -13 ± 3.6 vs. -6 ± 1.9%, respectively, P < 0.05). Even patients without LVH (16% with normal indexed LV mass, 79% female) had greater septal and inferior wall thickness compared with controls (8.8 ± 1.6 vs. 6.6 ± 1.2 mm, P < 0.05), which regressed post-AVR. These differences were not detectable by global measures of remodelling. Changes to clinical parameters post-AVR were also greater in males: N-terminal pro-brain natriuretic peptide (NT-proBNP) [-37 (interquartile range -88 to -2) vs. -1 (-24 to 11) ng/L, P = 0.008], and systolic blood pressure (12.9 ± 23 vs. 2.1 ± 17 mmHg, P = 0.009), with changes in NT-proBNP correlating with percentage LV mass regression in males only (ß 0.32, P = 0.02). CONCLUSION: In patients with severe AS, inc

Journal article

Garcia-Pavia P, Kim Y, Restrepo-Cordoba MA, Lunde IG, Wakimoto H, Smith AM, Toepfer CN, Getz K, Gorham J, Patel P, Ito K, Willcox JA, Arany Z, Li J, Owens AT, Govind R, Nuñez B, Mazaika E, Bayes-Genis A, Walsh R, Finkelman B, Lupon J, Whiffin N, Serrano I, Midwinter W, Wilk A, Bardaji A, Ingold N, Buchan R, Tayal U, Pascual-Figal DA, de Marvao A, Ahmad M, Garcia-Pinilla JM, Pantazis A, Dominguez F, John Baksi A, O'Regan DP, Rosen SD, Prasad SK, Lara-Pezzi E, Provencio M, Lyon AR, Alonso-Pulpon L, Cook SA, DePalma SR, Barton PJR, Aplenc R, Seidman JG, Ky B, Ware JS, Seidman CEet 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

Journal article

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.

Software

Pillinger T, Osimo EF, de Marvao A, Berry MA, Whitehurst T, Statton B, Quinlan M, Brugger S, Vazir A, Cook SA, O'Regan DP, Howes ODet 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.

Journal article

Attard M, Dawes T, Simoes Monteiro de Marvao A, Biffi C, Shi W, Wharton J, Rhodes C, Ghataorhe P, Gibbs J, Howard L, Rueckert D, Wilkins M, O'Regan Det 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.

Journal article

Mazzarotto F, Tayal P, Buchan R, Midwinter W, Wilk A, Whiffin N, Govind R, Mazaika E, De Marvao A, Felkin L, Dawes T, Ahmad M, Edwards E, Ing A, Thomson K, Chan L, Sim D, Baksi J, Pantazis A, Roberts A, Watkins H, Funke B, O'Regan D, Olivotto I, Barton P, Prasad S, Cook S, Ware J, Walsh Ret 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

Conference paper

Tarroni G, Oktay O, Bai W, Schuh A, Suzuki H, Passerat-Palmbach J, de Marvao A, O'Regan D, Cook S, Glocker B, Matthews P, Rueckert Det 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.

Journal article

O'Regan DP, 2019, Putting machine learning into motion: applications in cardiovascular imaging, Clinical Radiology, ISSN: 0009-9260

Journal article

Grapsa I, Tan T, Nunes MDC, O'Regan D, Durighel G, Howard LSGE, Gibbs SJR, Nihoyannopoulos Pet 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

Conference paper

Meyer HV, Dawes TJW, Serrani M, Bai W, Tokarczuk P, Cai J, de Marvao A, Rueckert D, Matthews PM, Costantino ML, Birney E, Cook SA, O'Regan DPet al., 2019, Genomic analysis reveals a functional role for myocardial trabeculae in adults, Publisher: Cold Spring Harbor Laboratory

<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 in silico 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>

Working paper

O'Regan D, Ptokarcz, Meyer H, Dawes T, Cai Jet al., 2019, ImperialCollegeLondon/fractalgenetics: fractalgenetics

Analysis of the genomic architecture and functional role of myocardial trabeculae

Software

Bello G, Dawes T, Duan J, Biffi C, Simoes Monteiro de Marvao A, Howard L, Gibbs S, Wilkins M, Cook S, Rueckert D, O'Regan Det 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.

Journal article

Berry A, Jarral O, Dawes T, Statton B, Quinlan M, Athanasiou T, O'Regan Det al., Aortic root surgery is associated with deterioration in left ventricular function and physical quality of life, SCMR 22nd Annual Scientific Sessions

Conference paper

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

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

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

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

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., 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

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

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, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG

Working paper

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, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG

Working paper

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