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

76 results found

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., Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation, IEEE Transactions on Medical Imaging, ISSN: 0278-0062

JOURNAL ARTICLE

Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O'Regan DPet al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework., Bioinformatics

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. Contact: declan.oregan@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

JOURNAL ARTICLE

Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O'Regan DPet al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework., Bioinformatics

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. Contact: declan.oregan@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

JOURNAL ARTICLE

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

Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard LSGE, Gibbs JSR, Rueckert D, Cook SA, Wilkins MR, 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: 0033-8419

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

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: 1097-6647

JOURNAL ARTICLE

Oktay O, Bai W, Guerrero R, Rajchl M, de Marvao A, O'Regan DP, Cook SA, Heinrich MP, Glocker B, Rueckert Det al., 2017, Stratified Decision Forests for Accurate Anatomical Landmark Localization in Cardiac Images, IEEE TRANSACTIONS ON MEDICAL IMAGING, Vol: 36, Pages: 332-342, ISSN: 0278-0062

JOURNAL ARTICLE

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: 0021-9290

JOURNAL ARTICLE

Schafer S, de Marvao A, Adami E, Fiedler LR, Ng B, Khin E, Rackham OJL, 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-P, O'Regan D, Ware JS, Hubner N, Cook SAet al., 2017, Titin-truncating variants affect heart function in disease cohorts and the general population, NATURE GENETICS, Vol: 49, Pages: 46-53, ISSN: 1061-4036

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, Pages: 73-82, ISSN: 0302-9743

© Springer International Publishing AG 2017. The correct acquisition of short axis (SA) cine cardiac MR image stacks requires the imaging of the full cardiac anatomy between the apex and the mitral valve plane via multiple 2D slices. While in the clinical practice the SA stacks are usually checked qualitatively to ensure full heart coverage, visual inspection can become infeasible for large amounts of imaging data that is routinely acquired, e.g. in population studies such as the UK Biobank (UKBB). Accordingly, we propose a learning-based technique for the fully-automated estimation of the heart coverage for SA image stacks. The technique relies on the identification of cardiac landmarks (i.e. the apex and the mitral valve sides) on two chamber view long axis images and on the comparison of the landmarks’ positions to the volume covered by the SA stack. Landmark detection is performed using a hybrid random forest approach integrating both regression and structured classification models. The technique was applied on 3000 cases from the UKBB and compared to visual assessment. The obtained results (error rate = 2.3%, sens. = 73%, spec. = 90%) indicate that the proposed technique is able to correctly detect the vast majority of the cases with insufficient coverage, suggesting that it could be used as a fully-automated quality control step for CMR SA image stacks.

CONFERENCE PAPER

Cheng Z, Kidher E, Jarral OA, O'Regan DP, Wood NB, Athanasiou T, Xu XYet al., 2016, Assessment of Hemodynamic Conditions in the Aorta Following Root Replacement with Composite Valve-Conduit Graft, ANNALS OF BIOMEDICAL ENGINEERING, Vol: 44, Pages: 1392-1404, ISSN: 0090-6964

JOURNAL ARTICLE

Corden B, de Marvao A, Dawes TJ, Shi W, Rueckert D, Cook SA, O'Regan DPet al., 2016, Relationship between body composition and left ventricular geometry using three dimensional cardiovascular magnetic resonance, JOURNAL OF CARDIOVASCULAR MAGNETIC RESONANCE, Vol: 18, ISSN: 1097-6647

JOURNAL ARTICLE

Dawes T, de Marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs S, Rueckert D, Cook S, Wilkins M, O'Regan Det al., 2016, Use of artificial intelligence to predict survival in pulmonary hypertension, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 35-35, ISSN: 0140-6736

CONFERENCE PAPER

Dawes TJW, Corden B, Cotter S, de Marvao A, Walsh R, Ware JS, Cook SA, O'Regan DPet al., 2016, Moderate Physical Activity in Healthy Adults Is Associated With Cardiac Remodeling, CIRCULATION-CARDIOVASCULAR IMAGING, Vol: 9, ISSN: 1941-9651

JOURNAL ARTICLE

Dawes TJW, Gandhi A, de Marvao A, Buzaco R, Tokarczuk P, Quinlan M, Durighel G, Diamond T, Garcia LM, de Cesare A, Cook SA, O'Regan DPet al., 2016, Pulmonary Artery Stiffness Is Independently Associated with Right Ventricular Mass and Function: A Cardiac MR Imaging Study, RADIOLOGY, Vol: 280, Pages: 398-404, ISSN: 0033-8419

JOURNAL ARTICLE

Durighel G, Tokarczuk PF, Karsa A, Gordon F, Cook SA, O'Regan DPet al., 2016, Acute myocardial infarction: susceptibility-weighted cardiac MRI for the detection of reperfusion haemorrhage at 1.5 T, CLINICAL RADIOLOGY, Vol: 71, Pages: E150-E156, ISSN: 0009-9260

JOURNAL ARTICLE

Harden SP, Bull RK, Bury RW, Castellano EA, Clayton B, Hamilton MCK, Morgan-Hughes GJ, O'Regan D, Padley SPG, Roditi GH, Roobottom CA, Stirrup J, Nicol ED, CTCA standards working party of the British Society of Cardiovascular Imaging, the Royal College of Physicians and the Royal College of Radiologistset al., 2016, The safe practice of CT coronary angiography in adult patients in UK imaging departments., Clin Radiol, Vol: 71, Pages: 722-728

Computed tomography coronary angiography is increasingly used in imaging departments in the investigation of patients with chest pain and suspected coronary artery disease. Due to the routine use of heart rate controlling medication and the potential for very high radiation doses during these scans, there is a need for guidance on best practice for departments performing this examination, so the patient can be assured of a good quality scan and outcome in a safe environment. This article is a summary of the document on 'Standards of practice of computed tomography coronary angiography (CTCA) in adult patients' published by the Royal College of Radiologists (RCR) in December 2014.

JOURNAL ARTICLE

Jaijee S, Quinlan M, Tokarczuk P, Statton B, Berry A, Diamond T, Howard L, Gibbs S, O'Regan Det al., 2016, DETERIORATION OF RIGHT VENTRICULAR FUNCTION ON EXERCISE DETECTED BY EXERCISE CARDIAC MAGNETIC RESONANCE IMAGING IN PATIENTS WITH PULMONARY ARTERIAL HYPERTENSION, Annual Conference of the British-Cardiovascular-Society (BCS) on Prediction and Prevention, Publisher: BMJ PUBLISHING GROUP, Pages: A88-A89, ISSN: 1355-6037

CONFERENCE PAPER

Jaijee S, Quinlan M, Tokarczuk P, Statton B, Diamond T, Howard LS, O'Regan D, Gibbs JSet al., 2016, Cardiac Magnetic Resonance Imaging In Healthy Volunteers In Normoxic And Hypoxic Exercise, International Conference of the American-Thoracic-Society (ATS), Publisher: AMER THORACIC SOC, ISSN: 1073-449X

CONFERENCE PAPER

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

Oktay O, Tarroni G, Bai W, de Marvao A, O'Regan D, Cook S, Rueckert Det al., 2016, Respiratory Motion Correction for 2D Cine Cardiac MR Images using Probabilistic Edge Maps, 43rd Computing in Cardiology Conference (CinC), Publisher: IEEE, Pages: 129-132, ISSN: 2325-8861

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: OXFORD UNIV PRESS, Pages: 1408-1408, ISSN: 0195-668X

CONFERENCE PAPER

de Marvao A, Cook SA, O'Regan DP, 2016, Precursors of Hypertensive Heart Phenotype Develop in Healthy Adults: An Alternative Explanation Reply, JACC-CARDIOVASCULAR IMAGING, Vol: 9, Pages: 763-764, ISSN: 1936-878X

JOURNAL ARTICLE

de Marvao A, Meyer H, Dawes T, Francis C, Shi W, Bai W, Rueckert D, Birney E, O'Regan DP, Cook Set al., 2016, Development of integrated high-resolution three-dimensional MRI and computational modelling techniques to identify novel genetic and anthropometric determinants of cardiac form and function, Spring Meeting on Clinician Scientists in Training, Publisher: ELSEVIER SCIENCE INC, Pages: 36-36, ISSN: 0140-6736

CONFERENCE PAPER

Bai W, Peressutti D, Oktay O, Shi W, O'Regan DP, King AP, Rueckert Det al., 2015, Learning a Global Descriptor of Cardiac Motion from a Large Cohort of 1000+Normal Subjects, 8th International Conference on Functional Imaging and Modeling of the Heart(FIMH), Publisher: SPRINGER-VERLAG BERLIN, Pages: 1-9, ISSN: 0302-9743

CONFERENCE PAPER

Bai W, Shi W, de Marvao A, Dawes TJW, O'Regan DP, Cook SA, Rueckert Det al., 2015, A bi-ventricular cardiac atlas built from 1000+high resolution MR images of healthy subjects and an analysis of shape and motion, MEDICAL IMAGE ANALYSIS, Vol: 26, Pages: 133-145, ISSN: 1361-8415

JOURNAL ARTICLE

Buyandelger B, Mansfield C, Kostin S, Choi O, Roberts AM, Ware JS, Mazzarotto F, Pesce F, Buchan R, Isaacson RL, Vouffo J, Gunkel S, Knoll G, McSweeney SJ, Wei H, Perrot A, Pfeiffer C, Toliat MR, Ilieva K, Krysztofinska E, Lopez-Olaneta MM, Gomez-Salinero JM, Schmidt A, Ng K-E, Teucher N, Chen J, Teichmann M, Eilers M, Haverkamp W, Regitz-Zagrosek V, Hasenfuss G, Braun T, Pennell DJ, Gould I, Barton PJR, Lara-Pezzi E, Schaefer S, Huebner N, Felkin LE, O'Regan DP, Brand T, Milting H, Nuernberg P, Schneider MD, Prasad S, Petretto E, Knoll Ret al., 2015, ZBTB17 (MIZ1) Is Important for the Cardiac Stress Response and a Novel Candidate Gene for Cardiomyopathy and Heart Failure, CIRCULATION-CARDIOVASCULAR GENETICS, Vol: 8, Pages: 643-652, ISSN: 1942-325X

JOURNAL ARTICLE

Dawes T, De Marvao A, Shi W, Rueckert D, Watson G, Howard L, Gibbs S, Cook S, Wilkins M, O'Regan Det al., 2015, Prognostic value of right heart adaptation to pulmonary arterial hypertension: a prospective cohort study, Congress of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 708-709, ISSN: 0195-668X

CONFERENCE PAPER

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