Fast nosologic imaging of the brain (Articolo in rivista)

  • Fast nosologic imaging of the brain (Articolo in rivista) (literal)
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • De Vos M., Laudadio T., Simonetti A.W., Heerschap A., Van Huffel S. (2007)
    Fast nosologic imaging of the brain
    in Journal of magnetic resonance (San Diego, Calif., 1997 : Print)
  • De Vos M., Laudadio T., Simonetti A.W., Heerschap A., Van Huffel S. (literal)
Pagina inizio
  • 292 (literal)
Pagina fine
  • 301 (literal)
  • 184 (literal)
  • ISI Web of Science (WOS) (literal)
  • Istituto per le Applicazioni del Calcolo M. Picone, National Research Council, (IAC-CNR), Bari, Italy. Katholieke Universiteit Leuven, Department of Electrical Engineering, Division ESAT–SCD (SISTA), Kasteelpark Arenberg 10, 3001 Leuven-Heverlee, Belgium. University of Nijmegen, University Medical Center St. Radboud, Department of Radiology, Geert Grooteplein Z18, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands. (literal)
  • Fast nosologic imaging of the brain (literal)
  • Magnetic resonance spectroscopic imaging (MRSI) provides information about the spatial metabolic heterogeneity of an organ in the human body. In this way, MRSI can be used to detect tissue regions with abnormal metabolism, e.g. tumor tissue. The main drawback of MRSI in clinical practice is that the analysis of the data requires a lot of expertise from the radiologists. In this article, we present an automatic method that assigns each voxel of a spectroscopic image of the brain to a histopathological class. The method is based on Canonical Correlation Analysis (CCA), which has recently been shown to be a robust technique for tissue typing. In CCA, the spectral as well as the spatial information about the voxel is used to assign it to a class. This has advantages over other methods that only use spectral information since histopathological classes are normally covering neighbouring voxels. In this paper, a new CCA-based method is introduced in which MRSI and MR imaging information is integrated. The performance of tissue typing is compared for CCA applied to the whole MR spectra and to sets of features obtained from the spectra. Tests on simulated and in vivo MRSI data show that the new method is very accurate in terms of classification and segmentation. The results also show the advantage of combining spectroscopic and imaging data. (literal)
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