Evaluation of deep learning estimation of whole heart anatomy from automated cardiovascular magnetic resonance short- and long-axis analyses in UK Biobank.

Journal: European heart journal. Cardiovascular Imaging
PMID: 38723059

Abstract

AIMS: Standard methods of heart chamber volume estimation in cardiovascular magnetic resonance (CMR) typically utilize simple geometric formulae based on a limited number of slices. We aimed to evaluate whether an automated deep learning neural network prediction of 3D anatomy of all four chambers would show stronger associations with cardiovascular risk factors and disease than standard volume estimation methods in the UK Biobank.

Authors

  • Marica Muffoletto
    School of Biomedical Engineering and Imaging Sciences, King's College London, 1 Lambeth Palace Rd, London SE1 7EU, UK.
  • Hao Xu
    Department of Nuclear Medicine, the First Affiliated Hospital, Jinan University, Guangzhou 510632, P.R.China.gdhyx2012@126.com.
  • Richard Burns
    School of Biomedical Engineering and Imaging Sciences, King's College London, 1 Lambeth Palace Rd, London SE1 7EU, UK.
  • Avan Suinesiaputra
    Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.
  • Anastasia Nasopoulou
    School of Biomedical Engineering and Imaging Sciences, King's College London, 1 Lambeth Palace Rd, London SE1 7EU, UK.
  • Karl P Kunze
    Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.
  • Radhouene Neji
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.
  • Steffen E Petersen
    Cardiac Imaging Department, Barts Heart Centre, St Bartholomew's Hospital, London, UK; William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University of London, London, UK.
  • Steven A Niederer
    Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Daniel Rueckert
    Biomedical Image Analysis (BioMedIA) Group, Department of Computing, Imperial College London, UK. Electronic address: d.rueckert@imperial.ac.uk.
  • Alistair A Young
    Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.

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