Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.

Journal: European radiology
PMID: 35357541

Abstract

OBJECTIVES: To determine the diagnostic accuracy and image quality of ultra-low-dose computed tomography (ULDCT) with deep learning reconstruction (DLR) to evaluate patients with suspected urolithiasis, compared with ULDCT with hybrid iterative reconstruction (HIR) by using low-dose CT (LDCT) with HIR as the reference standard.

Authors

  • Gumuyang Zhang
    Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
  • Xiaoxiao Zhang
    Key Laboratory of Drug Quality Control&Pharmacovigilance (China Pharmaceutical University), Ministry of Education, Nanjing, China.
  • Lili Xu
    Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China.
  • Xin Bai
    School of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia 010021, China. 6530071@163.com.
  • Ru Jin
    Department of Radiology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No.1 Shuaifuyuan, Wangfujing Street, Dongcheng District, Beijing, 100730, China.
  • Min Xu
    Department of Gastroenterology, Shanghai First People's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
  • Jing Yan
    Department of Neurology, Shanghai Pudong New Area People's Hospital, Shanghai, China.
  • Zhengyu Jin
    Departments of Radiology, Peking Union Medical College Hospital, Beijing.
  • Hao Sun
    Department of Gastrointestinal Surgery, Harbin Medical University Cancer Hospital, Harbin, China.

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