Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

Journal: European heart journal. Cardiovascular Imaging
PMID: 36725705

Abstract

AIMS: Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views.

Authors

  • Hao Xu
    Department of Nuclear Medicine, the First Affiliated Hospital, Jinan University, Guangzhou 510632, P.R.China.gdhyx2012@126.com.
  • Steven E Williams
    Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom (O.R., I.S., C.H.R., R.K., H.C., J.W., L.O., R.M., M.O., S.E.W., S.N.).
  • Michelle C Williams
    British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Chancellor's Building, 49 Little France Cres, Edinburgh, UK.
  • David E Newby
    Edinburgh Imaging Facility QMRI, Edinburgh, EH16 4TJ, UK; Centre for Cardiovascular Science, Edinburgh, EH16 4TJ, UK.
  • Jonathan Taylor
    3DLab, Medical Imaging Medical Physics, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK.
  • Radhouene Neji
    School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Lambeth Wing, London, UK.
  • Karl P Kunze
    Department of Biomedical Engineering, King's College London, Lambeth Palace Rd, London SE1 7EU, UK.
  • Steven A Niederer
    Cardiac Electro-Mechanics Research Group (CEMRG), National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
  • Alistair A Young
    Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand.

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