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We work with clinicians and MR scientists on a day-to-day basis to develop novel AI machine-learning approaches for cardiovascular imaging.

Exemplar virtual native enhancement framework

The Group’s primary aim is to advance cardiac diagnostic imaging and enrich cardiovascular clinical studies through the deep integration of AI machine learning with MRI and cardiology. In particular:

  • Make cardiovascular MRI scanning safer, faster and more informative by enhancing the image contrast with novel generative AI approaches. A representative work is the Virtual Native Enhancement technology.
  • Automate the cardiovascular MRI post-processing and reporting using pipelines empowered by feature detection, registration and segmentation machine-learning methods.
  • Enrich large biomedical studies with novel AI imaging biomarkers and machine-learning tools, through collaborations with the BHF CRE network, Big Data Institute and the Institute of Biomedical Engineering.

Our team


  • Prof Konstantinos Kamnitsas, Institute of Biomedical Imaging, University of Oxford
  • Prof Sven Plein, Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds
  • Prof Rohan Dharmakumar, Krannert Cardiovascular Research Center, Indiana University, USA
  • Prof Steffen Petersen, Queen Mary University of London, NIHR Barts Biomedical Research Centre (BRC)
  • Prof Minjie Lu, National Centre for Cardiovascular Diseases (NCCD), Chinese Academy of Medical Science, China

Related research themes