title: (1) H 2D MRSI tissue type analysis of gliomas.
abstract: OBJECTIVE
  To decompose 1H MR spectra of glioma patients into normal and abnormal tissue proportions for tumor classification and delineation.
  
  
  METHODS
  Anatomical imaging and 1H magnetic resonance spectroscopic imaging data have been acquired from 11 grade II and 13 grade IV glioma patients. LCModel was used to decompose the magnetic resonance spectroscopic imaging data into normal brain, grade II, and grade IV tissue proportions using a tissue type basis set. Simulations were conducted to evaluate the accuracy of the methodology. Results were visualized using colormaps and abnormality contours showing tumor grade and extent.
  
  
  RESULTS
  Simulations suggest that infiltrative tumor proportions as low as 20% can be identified at the typical 1H magnetic resonance spectroscopy signal-to-noise found in vivo. Tumor grading according to the highest estimated tumor grade within a lesion gave a classification accuracy of 86% discriminating between grade II and grade IV glioma. Voxels with significant proportions of tumor type spectra were found beyond the margins of contrast enhancement for most grade IV cases consistent with infiltration whereas the abnormality contours show that some tumors are confined within the hyperintensities shown by both post contrast T1 weighted and T2 weighted imaging.
  
  
  CONCLUSIONS
  LCModel can be used to decompose 1H MR spectra into proportions of normal and abnormal tissue to identify tumor extent, infiltration, and overall grade.
authors.affiliation: Neurosciences Research Centre, Cardiovascular and Cell Sciences Institute, St. George's University of London, London, UK.
authors.forename: Felix
authors.initials: F
authors.lastname: Raschke
authors.forename: Greg A
authors.initials: GA
authors.lastname: Fellows
authors.forename: Alan J
authors.initials: AJ
authors.lastname: Wright
authors.forename: Franklyn A
authors.initials: FA
authors.lastname: Howe
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D001932:Brain Neoplasms / Q000737:chemistry* / Q000145:classification / Q000473:pathology*; D005910:Glioma / Q000737:chemistry* / Q000145:classification / Q000473:pathology*; D006801:Humans; D060787:Neoplasm Grading; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor
keywords: LCModel; classification; decomposition; delineation; glioma; magnetic resonance spectroscopic imaging