Deep-learning-reconstructed high-resolution 3D cervical spine MRI for foraminal stenosis evaluation.

Journal: Skeletal radiology
Published Date:

Abstract

OBJECTIVE: To compare standard-of-care two-dimensional MRI acquisitions of the cervical spine with those from a single three-dimensional MRI acquisition, reconstructed using a deep-learning-based reconstruction algorithm. We hypothesized that the improved image quality provided by deep-learning-based reconstruction would result in improved inter-rater agreement for cervical spine foraminal stenosis compared to conventional two-dimensional acquisitions.

Authors

  • Meghan Jardon
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70th St, New York, NY, 10021, USA.
  • Ek T Tan
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70(th) Street, New York, NY 10021, United States of America.
  • J Levi Chazen
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 E 70th St, New York, NY, 10021, USA.
  • Meghan Sahr
    Department of Radiology and Imaging, Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA.
  • Yan Wen
  • Brandon Schneider
    Biostatistics Core, Research Administration, Hospital for Special Surgery, 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.