Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response.

Abstract

Authors

• Bona Kim
  Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea.
• Chul-Min Lee
  Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-Gu, Seoul, 05505, South Korea.
• Jong Keon Jang
  From the Department of Computer Science, Hanyang University, Seoul, Republic of Korea (K.J.C.); Department of Radiology and Research Institute of Radiology (J.K.J., S.S.L., Y.S.S., W.H.S., H.S.K., J.Y., J.H.K., S.Y.K.) and Department of Diagnostic Pathology (E.S.Y.), Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, South Korea; Department of Radiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (J.Y.C.); Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea (Y.L.); and Department of Radiology, Hanyang University Medical Center, Hanyang University School of Medicine, Seoul, Korea (B.K.K.).
• Jihun Kim
  School of Electronics Engineering, Kyungpook National University, 1370 Sankyuk-Dong, Puk-Gu, Taegu 702-701, Republic of Korea.
• Seok-Byung Lim
  Department of Surgery, University of Ulsan College of Medicine and Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 138-736, Korea.
• Ah Young Kim
  Medical Information Research Section, Electronics and Telecommunications Research Institute, Dajeon, Republic of Korea.

Keywords

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