Application of pattern recognition techniques for classification of paediatric brain tumours by in vivo 3T 1H MRS – A multi centre study

Research output: Contribution to journalArticle

Authors

  • Laurence J. Abernethy
  • Shivaram Avula
  • Nigel Davies
  • Daniel Rodriguez Gutierrez
  • Tim Jaspan
  • Lesley Macpherson
  • Dipayan Mitra
  • Paul S. Morgan
  • Simon Bailey
  • Barry Pizer
  • Richard G. Grundy
  • Dorothee P. Auer

External organisations

  • Department of Radiology, Alder Hey Children's NHS Foundation Trust
  • The Children's Brain Tumour Research Centre, University of Nottingham
  • Birmingham Children's Hospital
  • Neuroradiology Department, Newcastle upon Tyne Hospitals
  • Paediatric Oncology Department, Great North Children's Hospital
  • Department of Paediatric Oncology, Alder Hey Children's NHS Foundation Trust
  • The Children's Brain Tumour Research Centre, University of Nottingham
  • Sir James Spence Institute of Child Health
  • Royal Victoria Infirmary

Abstract

3T magnetic resonance scanners have boosted clinical application of 1H-MR spectroscopy (MRS) by offering an improved signal-to-noise ratio and increased spectral resolution, thereby identifying more metabolites and extending the range of metabolic information. Spectroscopic data from clinical 1.5T MR scanners has been shown to discriminate between pediatric brain tumors by applying machine learning techniques to further aid diagnosis. The purpose of this multi-center study was to investigate the discriminative potential of metabolite profiles obtained from 3T scanners in classifying pediatric brain tumors.

Details

Original languageEnglish
Pages (from-to)2359-2366
JournalMagnetic Resonance in Medicine
Volume79
Issue number4
Early online date8 Aug 2017
Publication statusPublished - Apr 2018

Keywords

  • MR specroscopy , 3T , pediatric brain tumors , diagnosis classification