AI-Generated Synthetic STIR of the Lumbar Spine from T1 and T2 MRI Sequences Trained with Open-Source Algorithms.

Journal: AJNR. American journal of neuroradiology
Published Date: Mar 4, 2025

Abstract

BACKGROUND AND PURPOSE: Lumbar spine MRIs can be time consuming, stressful for patients, and costly to acquire. In this work, we train and evaluate open-source generative adversarial network (GAN) to create synthetic lumbar spine MRI STIR volumes from T1 and T2 sequences, providing a proof-of-concept that could allow for faster MRI examinations.

Authors

  • Alice M L Santilli
    School of Computing, Queen's University, Kingston, ON, Canada. 14amls@queensu.ca.
  • Mark A Fontana
    Weill Cornell College of Medicine, New York, New York; Center for Analytics, Modeling, and Performance, Hospital for Special Surgery, New York, New York.
  • Erwin E Xia
    Department of Radiology and Imaging (E.E.X., Z.I., E.T.T., D.B.S., J.L.C.), Hospital for Special Surgery, New York, New York.
  • Zenas Igbinoba
    Department of Radiology, Columbia University Irving Medical Center/New York Presbyterian Hospital, 622 W 168th St., New York, NY, 10032, USA.
  • Ek Tsoon Tan
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
  • Darryl B Sneag
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70(th) Street, New York, NY 10021, United States of America. Electronic address: sneagd@hss.edu.
  • J Levi Chazen
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70th St, New York, NY, 10021, USA.

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