Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study.

Journal: Magnetic resonance in medicine
Published Date: May 14, 2017

Abstract

PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied using least-squares regression (LSR) to an analytical tissue model. However, the LSR method has not yet become clinically established due to the necessity for long acquisition times. Artificial neural networks (ANNs) recently have received increasing interest for robust curve-fitting and might pose an alternative to the conventional LSR method for reduced acquisition times. This study presents in vivo OEF mapping results using the conventional LSR and the proposed ANN method.

Authors

  • Sebastian Domsch
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
  • Bettina Mürle
    Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Germany.
  • Sebastian Weingärtner
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
  • Jascha Zapp
    Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Germany.
  • Frederik Wenz
    Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany. Frederik.Wenz@medma.uni-heidelberg.de.
  • Lothar R Schad

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