4D segmentation of the thoracic aorta from 4D flow MRI using deep learning.

Journal: Magnetic resonance imaging
PMID: 36621555

Abstract

BACKGROUND: 4D flow MRI allows the analysis of hemodynamic changes in the aorta caused by pathologies such as thoracic aortic aneurysms (TAA). For personalized management of TAA, new biomarkers are required to analyze the effect of fluid structure iteration which can be obtained from 4D flow MRI. However, the generation of these biomarkers requires prior 4D segmentation of the aorta.

Authors

  • Diana M Marin-Castrillon
    Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France.
  • Alain Lalande
  • Sarah Leclerc
  • Khalid Ambarki
    Siemens Healthcare SAS, Saint-Denis 93200, France.
  • Marie-Catherine Morgant
    Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France; Department of cardiovascular and thoracic surgery, University Hospital of Dijon, Dijon 21000, France.
  • Alexandre Cochet
    Department of Nuclear Medicine, Centre Georges-François Leclerc, Dijon, France; LE2I UMR6306, Centre national de la recherche scientifique, Arts et Métiers, Université Bourgogne Franche-Comté, Dijon, France; MRI Unit, Centre Hospitalier Régional Universitaire, Hôpital François Mitterrand, Dijon, France.
  • Siyu Lin
    Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France.
  • Olivier Bouchot
    Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France; Department of cardiovascular and thoracic surgery, University Hospital of Dijon, Dijon 21000, France.
  • Arnaud Boucher
    ImViA Laboratory, University of Burgundy, Dijon, France.
  • Benoit Presles
    Imaging and Artificial Vision Laboratory, EA 7535, University of Burgundy, Dijon 21000, France. Electronic address: benoit.presles@u-bourgogne.fr.

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