The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography.

Journal: European journal of radiology
Published Date: Jun 16, 2025

Abstract

PURPOSE: To evaluate the value of deep learning image reconstruction (DLIR) in improving image quality of virtual non-hydroxyapatite (VNHAP) and virtual monoenergetic images (VMIs), and radiologists' performance in detecting acute vertebral compression fractures (VCFs).

Authors

  • Jiayi Tang
    Department of Chemical Engineering and Analytical Science, Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. alex.henderson@manchester.ac.uk.
  • Luyou Yan
    Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China.
  • Kun Zhang
    Philosophy Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
  • Suping Chen
    GE Healthcare, Computed Tomography Research Center, Beijing, 100176, People's Republic of China.
  • Ping Liu
    Department of Cardiology, the Second Hospital of Shandong University, 250033 Jinan, Shandong, China.
  • Jinling Wang
    College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, China.
  • Yewen He
    Department of Radiology, The First Hospital of Hunan University of Chinese Medicine, 95 Shaoshan Middle Road, Yuhua District, Changsha 410007, PR China.

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