Neural networks with personalized training for improved MOLLI T mapping.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: The aim of this study was to develop a method for personalized training of Deep Neural Networks by means of an MRI simulator to improve MOLLI native T estimates relative to conventional fitting methods.

Authors

  • Olympia Gkatsoni
    Laboratory of Computing, Medical Informatics and Biomedical - Imaging Technologies, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
  • Christos G Xanthis
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden. Electronic address: cxanthis@gmail.com.
  • Sebastian Johansson
    Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
  • Einar Heiberg
    Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skane University Hospital, Lund, Sweden.
  • Håkan Arheden
    Clinical Physiology, Department of Clinical Sciences Lund, Lund University, Skåne University Hospital, Lund, Sweden.
  • Anthony H Aletras
    Laboratory of Computing, Medical Informatics and Biomedical-Imaging Technologies, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece; Department of Clinical Physiology, Clinical Sciences, Lund University and Lund University Hospital, Lund, Sweden. Electronic address: aletras@hotmail.com.

Keywords

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