Deep Learning-Based Analysis of Aortic Morphology From Three-Dimensional MRI.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 38216546

Abstract

BACKGROUND: Quantification of aortic morphology plays an important role in the evaluation and follow-up assessment of patients with aortic diseases, but often requires labor-intensive and operator-dependent measurements. Automatic solutions would help enhance their quality and reproducibility.

Authors

  • Jia Guo
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Kevin Bouaou
    Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France.
  • Sophia Houriez-Gombaud-Saintonge
    Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.
  • Moussa Gueda
    Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.
  • Umit Gencer
    Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France.
  • Vincent Nguyen
    Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.
  • Etienne Charpentier
    Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.
  • Gilles Soulat
    Université de Paris Cité, PARCC, INSERM, Paris, France.
  • Alban Redheuil
    Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France.
  • Elie Mousseaux
    Department of Radiology, Hôpital Européen Georges Pompidou, APHP, University of Paris & INSERM, U970 29 rue Leblanc, 75015, Paris, France.
  • Nadjia Kachenoura
    Sorbonne Université, Laboratoire d'Imagerie Biomédicale, INSERM, CNRS, Paris, France.
  • Thomas Dietenbeck
    Sorbonne Université, INSERM, CNRS, Laboratoire d'Imagerie Biomédicale (LIB), Paris, France.

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