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

• Published in: Magnetic resonance in medicine Vol. 79; no. 2; pp. 890 - 899
• Main Authors: Domsch, Sebastian, Mürle, Bettina, Weingärtner, Sebastian, Zapp, Jascha, Wenz, Frederik, Schad, Lothar R.
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2018
• Subjects: Adult; Aged; analytical tissue model; artificial neural network; Artificial neural networks; Biomarkers; blood‐oxygenation‐level‐dependent (BOLD); Brain; Brain - diagnostic imaging; Brain - metabolism; Brain cancer; Brain Neoplasms - blood; Brain Neoplasms - diagnostic imaging; Brain tumors; Computer simulation; Curve fitting; Feasibility studies; Female; Functional magnetic resonance imaging; GESSE; Humans; Image Interpretation, Computer-Assisted; Image Processing, Computer-Assisted - methods; In vivo methods and tests; Least-Squares Analysis; least‐squares regression; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mapping; Neural networks; Neural Networks (Computer); Oxygen - blood; oxygen extraction fraction; Oxygenation; Protocol (computers); Regression analysis; Viability; Young Adult; artificial neural network; analytical tissue model; least-squares regression; machine learning; oxygen extraction fraction; GESSE; blood-oxygenation-level-dependent (BOLD)

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...