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

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

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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. Copyright © 2010 Yen-Chun Chou et al.
Original languageEnglish
JournalInternational Journal of Biomedical Imaging
Volume2010
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • Brain images
  • Brain imaging
  • Brain tissue
  • Cerebral blood flow
  • Cerebral blood volume
  • Cerebral hemodynamics
  • Dynamic images
  • Gray matter
  • Independent factor analysis
  • Magnetic resonance perfusions
  • Mean transit time
  • Temporal signals
  • Time curves
  • White matter
  • Blood
  • Brain
  • Hemodynamics
  • Hydrodynamics
  • Inductively coupled plasma
  • Magnetic resonance
  • Blind source separation

Fingerprint Dive into the research topics of 'Blind source separation of hemodynamics from magnetic resonance perfusion brain images using independent factor analysis'. Together they form a unique fingerprint.

Cite this