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)
• ISSN: 0740-3194; 1522-2594; 1522-2594
• DOI: 10.1002/mrm.26749

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. Methods In vivo data of five healthy volunteers and one patient with a primary brain tumor were acquired at 3T using a gradient‐echo sampled spin‐echo (GESSE) sequence. The ANN was trained with simulated BOLD data. Results In healthy subjects, the mean OEF was 36 ± 2% (LSR) and 40 ± 1% (ANN). The OEF variance within subjects was reduced from 8% to 6% using the ANN method. In the patient, both methods revealed a distinct OEF hotspot in the tumor area, whereas ANN showed less apparent artifacts in surrounding tissue. Conclusion In clinical scan times, the ANN analysis enables OEF mapping with reduced variance, which could facilitate its integration into clinical protocols. Magn Reson Med 79:890–899, 2018. © 2017 International Society for Magnetic Resonance in Medicine.