Deep learning image enhancement for confident diagnosis of TMJ osteoarthritis in zero-TE MR imaging.

Journal: Dento maxillo facial radiology
PMID: 39989448

Abstract

OBJECTIVES: This study aimed to evaluate the effectiveness of deep learning method for denoising and artefact reduction (AR) in zero echo time MRI (ZTE-MRI). Also, clinical applicability was evaluated by comparing image diagnosis to the temporomandibular joint (TMJ) cone-beam CT (CBCT).

Authors

  • Chena Lee
    Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
  • Joonsung Lee
    From the Department of Radiology and Research Institute of Radiology (M.K., H.S.K., H.J.K., J.E.P., S.J.K.), Department of Clinical Epidemiology and Biostatistics (S.Y.P.), and Department of Neurosurgery (Y.H.K.), University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-Gu, Seoul 05505, South Korea; GE Healthcare Korea, Seoul, Korea (J.L.); GE Healthcare Canada, Calgary, Canada (M.R.L.); and Department of Radiology, University of Calgary, Calgary, Canada (M.R.L.).
  • Sagar Mandava
    GE Healthcare, Atlanta, GA, 30308, USA.
  • Maggie Fung
    GE Healthcare, Waukesha, Wisconsin, USA.
  • Yoon Joo Choi
    Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, 03722, Republic of Korea.
  • Kug Jin Jeon
    Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea.
  • Sang-Sun Han
    Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, Republic of Korea. Electronic address: sshan@yuhs.ac.

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