Using an artificial neural network for fast mapping of the oxygen extraction fraction with combined QSM and quantitative BOLD.

Overview

PURPOSE: To apply an artificial neural network (ANN) for fast and robust quantification of the oxygen extraction fraction (OEF) from a combined QSM and quantitative BOLD analysis of gradient echo data and to compare the ANN to a traditional quasi-Newton (QN) method for numerical optimization. METHODS: ν RESULTS: = CONCLUSION: ANNs allow faster and, with regard to initialization, more robust reconstruction of OEF maps with lower intersubject variation than QN approaches.