Imperial College London


Faculty of MedicineInstitute of Clinical Sciences

Reader in Imaging Sciences



+44 (0)20 3313 1510declan.oregan




Imaging Sciences DepartmentHammersmith HospitalHammersmith Campus





Dr Declan O'Regan is an MRC Clinical Scientist and Consultant Radiologist who heads the Robert Steiner MRI suite (Mansfield Centre for Innovation) at the MRC London Institute of Medical Sciences. He is lead clinician for imaging research at Imperial College Healthcare NHS Trust. He is committed to science engagement and is a past Roentgen (UK) and Rowan-Williams (Australasia) travelling professor.

His research is focussed on developing artificial intelligence to understand the genetic and physiological mechanisms that underpin cardiovascular disease using cardiac imaging. His team recently developed the 4Dsurvival algorithm for predicting outcomes in heart disease from cardiac motion analysis (Nature Machine Intelligence).  His projects include:

  • Developing advanced computer vision algorithms in cardiac MRI.
  • Predicting time-to-events from cardiac motion analysis.
  • Automated discovery of cardiac genotype-phenotype associations in health and disease.
  • Designing artificial reasoning systems to classify heart disease.
  • Accelerating drug discovery using machine learning in UK Biobank.
  • Discovering new quantitative imaging risk factors in heart disease.

His interdisciplinary collaborators include Prof Stuart Cook, Prof Daniel Rueckert, and Prof Ewan Birney. This research is funded by NIHR, BHF and MRC.

 Cardiac motion model

Selected Publications

Journal Articles

Attard M, Dawes T, Simoes Monteiro de Marvao A, et al., 2019, Metabolic pathways associated with right ventricular adaptation to pulmonary hypertension: Three dimensional analysis of cardiac magnetic resonance imaging, Ehj Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging, Vol:20, ISSN:2047-2412, Pages:668-676

Bello G, Dawes T, Duan J, et al., 2019, Deep learning cardiac motion analysis for human survival prediction, Nature Machine Intelligence, Vol:1, ISSN:2522-5839, Pages:95-104

Dawes T, Cai J, Quinlan M, et al., 2018, Fractal analysis of right ventricular trabeculae in pulmonary hypertension, Radiology, Vol:288, ISSN:0033-8419, Pages:386-395

Ware JS, Amor-Salamanca A, Tayal U, et al., 2018, A genetic etiology for alcohol-induced cardiac toxicity, Journal of the American College of Cardiology, Vol:71, ISSN:0735-1097, Pages:2293-2302

Michelakis ED, Gurtu V, Webster L, et al., 2017, Inhibition of pyruvate dehydrogenase kinase improves pulmonary arterial hypertension in genetically susceptible patients, Science Translational Medicine, Vol:9, ISSN:1946-6234

Biffi C, Simoes Monteiro de Marvao A, Attard M, et al., 2017, Three-dimensional Cardiovascular Imaging-Genetics: A Mass Univariate Framework, Bioinformatics, ISSN:1367-4803

Dawes T, Simoes monteiro de marvao A, Shi W, et al., 2017, Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study, Radiology, Vol:283, ISSN:1527-1315, Pages:381-390

Schafer S, de Marvao A, Adami E, et al., 2016, Titin truncating variants affect heart function in disease cohorts and the general population, Nature Genetics, Vol:49, ISSN:1546-1718, Pages:46-53

Dawes TJW, Corden B, Cotter S, et al., 2016, Moderate Physical Activity in Healthy Adults is Associated with Cardiac Remodeling, Circulation-cardiovascular Imaging, Vol:9, ISSN:1942-0080

Dawes TJW, Gandhi A, de Marvao A, et al., 2016, Pulmonary artery stiffness is independently associated with right ventricular mass and function: a cardiac magnetic resonance study., Radiology, Vol:280, ISSN:1527-1315

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