Project Description

The aim of the project at King’s College London was to evaluate machine learning algorithms developed for cardiac magnetic resonance imaging (CMR), trained on UK Biobank, on patient data in clinical CMR centres linked with HDR sites (e.g. St Thomas’, Barts BioResource).

Project Milestones

In the short duration of the postdoc working on this grant, Dr Ines Machado (6 months until 12/2020), advances have been made for data from both UK biobank, with preliminary testing on patient data acquired at St. Thomas’ Hospital trust, resulting in a publication to the prestigious MICCAI conference satellite event STACOM. She also contributed to a second paper for that event, on cardiac MR image domain generalisation. Journal paper submissions for both double-blind, peer-reviewed, full-length conference publications are in preparation.

Project Team and Collaborators

  • Professor Julia Schnabel, King’s College London and Helmholtz Center Munich & Technical University of Munich – project lead
  • Dr Ines Machado, King’s College London – funded by HDR UK
  • Dr Andrew King, King’s College London
  • Prof Alistair Young, King’s College London
  • Prof Claudia Prieto, King’s College London
  • Prof Miguel Castelo-Branco, University of Coimbra
  • Dr Devran Ugurl, King’s College London
  • Dr Gastao Cruz, King’s College London
  • Dr Bram Ruijsink, King’s College London and St. Thomas’ Hospital Trust
  • Dr Kerstin Hammernik, Imperial College London and Technical University of Munich
  • Steffen Petersen,  NIHR Barts Biomedical Research Centre (BRC)


deep learning for image reconstruction; accelerated MRI; image segmentation; quality assessment; domain generalisation