Radiation dose reduction in pediatric computed tomography (CT) using deep convolutional neural network denoising.

Journal: Clinical radiology
Published Date: Sep 21, 2024

Abstract

AIM: We evaluated the quality of noncontrast chest computed tomography (CT) for pediatric patients at two dose levels with and without denoising using a deep convolutional neural network (CNN).

Authors

  • K K Horst
    Pediatric Radiology Division, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA. Electronic address: Horst.Kelly@mayo.edu.
  • Z Zhou
    Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • N C Hull
    Pediatric Radiology Division, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
  • P G Thacker
    Pediatric Radiology Division, Department of Radiology, Mayo Clinic, 200 1st St SW, Rochester, MN, 55905, USA.
  • B A Kassmeyer
    Department of Biomedical Statistics and Informatics, Mayo Clinic, 200 1(st) St SW, Rochester, MN, 55905, USA.
  • M P Johnson
    Department of Biomedical Statistics and Informatics, Mayo Clinic, 200 1(st) St SW, Rochester, MN, 55905, USA.
  • N Demirel
    Division of Pediatric Pulmonology, Department of Pediatrics and Adolescent Medicine, Mayo Clinic, 200 1(st) St SW, Rochester, MN, 55905, USA.
  • A D Missert
    Department of Radiology, Mayo Clinic, 200 1(st) St SW, Rochester, MN, 55905, USA.
  • K Weger
    Department of Radiology, Mayo Clinic, 200 1(st) St SW, Rochester, MN, 55905, USA.
  • L Yu
    Department of Otolaryngology-Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China.

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