Deep learning reconstruction in biparametric prostate MRI: Impact on qualitative and radiomics analyses.

Journal: Research in diagnostic and interventional imaging
Published Date: Jun 1, 2025

Abstract

OBJECTIVE: To assess the impact of a commercially available deep learning reconstruction (DLR) algorithm on qualitative and radiomics analyses in prostate MRI.

Authors

  • Jérémy Dana
    IHU of Strasbourg, Strasbourg, France; Inserm & University of Strasbourg UMR-S1110, Strasbourg, France; Faculty of Medicine, University of Paris, Paris, France.
  • Evan McNabb
    Research Institute of the McGill University Health Centre, Augmented Intelligence & Precision Health Laboratory (AIPHL), Montréal, Canada.
  • Juan Castro
    McGill University, Department of Diagnostic Radiology, Montréal, Canada.
  • Ibtisam Al-Qanoobi
    McGill University, Department of Diagnostic Radiology, Montréal, Canada.
  • Yoshie Omiya
    Department of Radiology, University of Yamanashi, Shimokato, Chuo City, Yamanashi Prefecture, Japan.
  • Kenny Ah-Lan
    McGill University, Department of Diagnostic Radiology, Montréal, Canada.
  • Véronique Fortier
    McGill University, Department of Diagnostic Radiology, Montréal, Canada.
  • Giovanni Artho
    McGill University, Department of Diagnostic Radiology, Montréal, Canada.
  • Caroline Reinhold
    Department of Radiology, McGill University Health Center, Montréal, Québec, Canada.
  • Simon Gauvin
    Department of Diagnostic Radiology, McGill University, Montreal, Canada.

Keywords

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