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KJR Honors Most Impactful Article and Distinguished Reviewers for 2023

  • Seong Ho Park (Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 투고 : 2023.08.27
  • 심사 : 2023.08.27
  • 발행 : 2023.10.01

초록

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참고문헌

  1. Park SH. KJR ways to recognize most impactful articles and distinguished reviewers. Korean J Radiol 2021;22:1594-1596
  2. Kim JH, Yoon HJ, Lee E, Kim I, Cha YK, Bak SH. Validation of deep-learning image reconstruction for low-dose chest computed tomography scan: emphasis on image quality and noise. Korean J Radiol 2021;22:131-138
  3. Otgonbaatar C, Ryu JK, Shin J, Woo JY, Seo JW, Shim H, et al. Improvement in image quality and visibility of coronary arteries, stents, and valve structures on CT angiography by deep learning reconstruction. Korean J Radiol 2022;23:1044-1054
  4. Son W, Kim M, Hwang JY, Kim YW, Park C, Choo KS, et al. Comparison of a deep learning-based reconstruction algorithm with filtered back projection and iterative reconstruction algorithms for pediatric abdominopelvic CT. Korean J Radiol 2022;23:752-762
  5. Park J, Shin J, Min IK, Bae H, Kim YE, Chung YE. Image quality and lesion detectability of lower-dose abdominopelvic CT obtained using deep learning image reconstruction. Korean J Radiol 2022;23:402-412
  6. Yeoh H, Hong SH, Ahn C, Choi JY, Chae HD, Yoo HJ, et al. Deep learning algorithm for simultaneous noise reduction and edge sharpening in low-dose CT images: a pilot study using lumbar spine CT. Korean J Radiol 2021;22:1850-1857
  7. Yan C, Lin J, Li H, Xu J, Zhang T, Chen H, et al. Cycleconsistent generative adversarial network: effect on radiation dose reduction and image quality improvement in ultralow-dose CT for evaluation of pulmonary tuberculosis. Korean J Radiol 2021;22:983-993