Impact of Emerging Deep Learning-Based MR Image Reconstruction Algorithms on Abdominal MRI Radiomic Features.

Journal: Journal of computer assisted tomography
Published Date: Aug 22, 2024

Abstract

OBJECTIVE: This study aims to evaluate, on one MRI vendor's platform, the impact of deep learning (DL)-based reconstruction techniques on MRI radiomic features compared to conventional image reconstruction techniques.

Authors

  • Hailong Li
    College of Energy, Xiamen University, Xiamen, 361005 People's Republic of China.
  • Vinicius Vieira Alves
    Department of Radiology, Cincinnati Children's Hospital Medical Center.
  • Amol Pednekar
    Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Mary Kate Manhard
    Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts.
  • Joshua Greer
  • Andrew T Trout
    Department of Radiology, Division of Thoracoabdominal Imaging, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave., Cincinnati, OH, 45229-3039, USA.
  • Lili He
    Department of Food Science, University of Massachusetts Amherst, United States of America. Electronic address: lilihe@foodsci.umass.edu.
  • Jonathan R Dillman
    Department of Radiology, Division of Thoracoabdominal Imaging, Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave., Cincinnati, OH, 45229-3039, USA. jonathan.dillman@cchmc.org.

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