Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.

Journal: European radiology experimental
PMID: 39881022

Abstract

BACKGROUND: Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.

Authors

  • Tito Bassani
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy.
  • Andrea Cina
    IRCCS Istituto Ortopedico Galeazzi, Via Riccardo Galeazzi 4, 20161, Milan, Italy.
  • Fabio Galbusera
    Laboratory of Biological Structures Mechanics, IRCCS Istituto Ortopedico Galeazzi, Via Galeazzi 4, 20161, Milan, Italy. fabio.galbusera@grupposandonato.it.
  • Andrea Cazzato
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Maria Elena Pellegrino
    IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
  • Domenico Albano
    IRCCS Istituto Ortopedico Galeazzi, Milano, Italy; Sezione di Scienze Radiologiche, Dipartimento di Biomedicina, Neuroscienze e Diagnostica Avanzata, Università degli Studi di Palermo, Palermo, Italy.
  • Luca Maria Sconfienza
    Unit of Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.

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