Machine-Based Rejection of Low-Quality Spectra and Estimation of Brain Tumor Probabilities from Magnetic Resonance Spectroscopic Images

  • Bjoern H. Menze
  • B. Michael Kelm
  • Daniel Heck
  • Matthias P. Lichy
  • Fred A. Hamprecht
Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Abstract

Magnetic resonance spectroscopic images (MRSI) carry spatially resolved information about the in vivo metabolism, however, their evaluation is difficult. Problems arise especially from artifacts and noise, yielding non-evaluable signals in many voxels. We propose a two-step approach to the processing of MRSI. In the first step a non-linear classifier is employed in every voxel to determine whether the spectral signal is evaluable, and if so, the tumor probability is computed in the second step. Thus, the quality control is strictly separated from the diagnostic evaluation of the spectrum. For an assessment of the proposed approach we consider MRSI-based brain tumor detection and localization and a tumor probability mapping by pattern recognition. In a quantitative comparison against the standard operator-controlled processing our interaction-free approach shows similar to superior performance.

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References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Bjoern H. Menze
    • 1
  • B. Michael Kelm
    • 1
  • Daniel Heck
    • 1
  • Matthias P. Lichy
    • 2
  • Fred A. Hamprecht
    • 1
  1. 1.Interdisziplinäres Zentrum für Wissenschaftliches Rechnen (IWR)Universität HeidelbergHeidelberg
  2. 2.Abteilung RadiologieDeutsches Krebsforschungszentrum (dkfz)Heidelberg
  3. 3.Diagnostische RadiologieUniversität TübingenTübingen

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