Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Journal: Neurosurgical focus
Published Date: Jul 1, 2025

Abstract

OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine from biplanar radiographs. The aim of the study was to identify the most robust and clinically viable approach for this potential intraoperative imaging technique.

Authors

  • Massimo Bottini
    1Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Olivier Zanier
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Raffaele Da Mutten
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Maria L Gandia-Gonzalez
    3Department of Neurosurgery, Hospital Universitario La Paz, Madrid, Spain.
  • Erik Edström
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Adrian Elmi-Terander
    Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
  • Luca Regli
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Carlo Serra
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Victor E Staartjes
    Department of Neurosurgery, Bergman Clinics, Naarden, The Netherlands; and.

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