A neural network to create super-resolution MR from multiple 2D brain scans of pediatric patients.

Journal: Medical physics
Published Date: Dec 10, 2024

Abstract

BACKGROUND: High-resolution (HR) 3D MR images provide detailed soft-tissue information that is useful in assessing long-term side-effects after treatment in childhood cancer survivors, such as morphological changes in brain structures. However, these images require long acquisition times, so routinely acquired follow-up images after treatment often consist of 2D low-resolution (LR) images (with thick slices in multiple planes).

Authors

  • Jose Benitez-Aurioles
    Division of Informatics, Imaging and Data Sciences, University of Manchester, Manchester, UK.
  • Eliana M Vásquez Osorio
    Radiotherapy-Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Marianne C Aznar
    Radiotherapy-Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
  • Marcel van Herk
    Manchester Cancer Research Centre, Division of Molecular and Clinical Cancer Science, School of Medical Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Sciences Centre, UK; The Christie NHS Foundation Trust, Manchester Academic Health Sciences Centre, UK.
  • Shermaine Pan
    The Christie NHS Foundation Trust, Manchester, UK.
  • Peter Sitch
    The Christie NHS Foundation Trust, Manchester, UK.
  • Anna France
    The Christie NHS Foundation Trust, Manchester, UK.
  • Ed Smith
    The Christie NHS Foundation Trust, Manchester, UK.
  • Angela Davey
    Radiotherapy-Related Research Group, Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.

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