Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis

  • Authors:

  • Yen-Chun Chou;Chia-Feng Lu;Wan-Yuo Guo;Yu-Te Wu

  • Affiliations:

  • Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan and Integrated Brain Research Laboratory, Department of Medical Research and Education, Ta ...;Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan and Integrated Brain Research Laboratory, Department of Medical Research and Education, Ta ...;Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan and Faculty of Medicine, National Yang-Ming University, Taipei, Taiwan;Dept. of Biomed. Imaging and Radiological Sciences, National Yang-Ming Uni., Taipei, Taiwan and Integrated Brain Res. Lab., Dept. of Med. Res. and Edu., Taipei Veterans General Hospital, Taipei, T ...

  • Venue:
  • Journal of Biomedical Imaging - Special issue on mathematical methods for images and surfaces
  • Year:
  • 2010

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Abstract

Perfusion magnetic resonance brain imaging induces temporal signal changes on brain tissues, manifesting distinct blood-supply patterns for the profound analysis of cerebral hemodynamics. We employed independent factor analysis to blindly separate such dynamic images into different maps, that is, artery, gray matter, white matter, vein and sinus, and choroid plexus, in conjunction with corresponding signal-time curves. The averaged signal-time curve on the segmented arterial area was further used to calculate the relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), and mean transit time (MTT). The averaged ratios for rCBV, rCBF, and MTT between gray and white matters for normal subjects were congruent with those in the literature.