Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Journal: F1000Research
PMID: 38725640

Abstract

BACKGROUND: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations.

Authors

  • Obhuli Chandran M
    Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
  • Saikiran Pendem
    Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India.
  • Priya P S
    Department of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Karnataka, Manipal, 576104, India.
  • Cijo Chacko
    Clinical Scientist, Philips Research and Development, Philips innovation campus, Yelahanka, Karnataka, 560064, India.
  • Priyanka -
    Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
  • Rajagopal Kadavigere
    Department of Radiodiagnosis, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, India.

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