Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

Journal: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Published Date: Nov 28, 2022

Abstract

BACKGROUND: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).

Authors

  • Marco Penso
    Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy. Electronic address: marco.penso@cardiologicomonzino.it.
  • Mario Babbaro
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Sara Moccia
    Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy. Electronic address: sara.moccia@iit.it.
  • Marco Guglielmo
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Maria Ludovica Carerj
    Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138, Milan, Italy.
  • Carlo Maria Giacari
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Mattia Chiesa
    Centro Cardiologico Monzino, IRCCS, Milan, Italy.
  • Riccardo Maragna
    Department of Clinical Sciences and Community Health, Cardiovascular Section, University of Milano, Milano, Italy.
  • Mark G Rabbat
    Loyola University Medical Center, Maywood, IL, USA.
  • Andrea Barison
    Cardiology and Cardiovascular Medicine, Fondazione Toscana Gabriele Monasterio, Pisa.
  • Nicola Martini
    Imaging Department, Fondazione Gabriele Monasterio, Massa, Italy.
  • Mauro Pepi
    Clinical Cardiology Unit and Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
  • Enrico G Caiani
    Department of Electronics, Information and Biomedical Engineering, Politecnico Di Milano, P.zza L. da Vinci 32, 20133, Milan, Italy. enrico.caiani@polimi.it.
  • Gianluca Pontone
    Department of Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.

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