Image quality and lesion detectability of deep learning-accelerated T2-weighted Dixon imaging of the cervical spine.

Journal: Skeletal radiology
PMID: 37233758

Abstract

OBJECTIVES: To validate the subjective image quality and lesion detectability of deep learning-accelerated Dixon (DL-Dixon) imaging of the cervical spine compared with routine Dixon imaging.

Authors

  • Geojeong Seo
    Department of Radiology, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Sun Joo Lee
  • Dae Hyun Park
    Department of Orthopaedic Surgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Sung Hwa Paeng
    Department of Neurosurgery, Busan Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Gregor Koerzdoerfer
    MR Applications Predevelopment, Siemens Healthcare GmbH, Allee am Roethelheimpark 2, 91052, Erlangen, Germany.
  • Marcel Dominik Nickel
    MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany.
  • JaeKon Sung
    Siemens Healthineers Ltd., Seoul, Republic of Korea.

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