Comparative evaluation of deep learning-based and conventional reconstruction techniques for image quality enhancement in low-dose chest computed tomography.

Journal: Journal of thoracic disease
Published Date: May 28, 2025

Abstract

BACKGROUND: With the increasing use of computed tomography (CT) in clinical practice, greater attention is being paid to the radiation dose. There are numerous methods available for reducing the CT radiation dose, but enhancement via a reconstruction algorithm is the most effective method. This study aimed to evaluate the performance of a proposed deep learning (DL) algorithm for low-dose chest CT image reconstruction among patients with pulmonary diseases and to compare it with several mainstream iterative reconstruction (IR) techniques.

Authors

  • Zhengyu Zhang
    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, 95 Yong'an Road, Xicheng District, Beijing, 100050, People's Republic of China.
  • Chong Liu
    * Department of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, P. R. China.
  • Shujuan Zhou
    Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, China.
  • Xianfeng Yang
    Department of Radiology, the First Affiliated Hospital of Soochow University, Suzhou, China.
  • Romaric Loffroy
    LE2I UMR6306, Centre national de la recherche scientifique, Arts et Métiers, Université Bourgogne Franche-Comté, Dijon, France; Department of Vascular, Oncologic and Interventional Radiology, Centre Hospitalier Régional Universitaire, Hôpital François Mitterrand, Dijon, France.
  • Hae Won Kim
    Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, Daegu, South Korea.
  • Lei Shan
    School of Rehabilitation, Capital Medical University, 10 Jiaomen North Road, Fengtai District, Beijing, 100068, China.

Keywords

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