Deep learning for quality assessment of axial T2-weighted prostate MRI: a tool to reduce unnecessary rescanning.

Journal: European radiology experimental
PMID: 40299162

Abstract

BACKGROUND: T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.

Authors

  • Jacob N Gloe
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • Eric A Borisch
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Adam T Froemming
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA.
  • Akira Kawashima
    Department of Radiology, Mayo Clinic in Arizona, Phoenix, Arizona.
  • Jordan D LeGout
    Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
  • Hirotsugu Nakai
    Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate, School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto 606-8507, Japan. Electronic address: nakai.hirotsugu.33x@kyoto-u.jp.
  • Naoki Takahashi
    1 Department of Radiology, Radiology Informatics Laboratory, Mayo Clinic, 3507 17th Ave NW, Rochester, MN 55901.
  • Stephen J Riederer
    Department of Radiology, Mayo Clinic, Rochester, MN, 55905, USA. riederer@mayo.edu.

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