MRI-based mapping of the oxygen extraction fraction (OEF) is a valuable addition to diagnosis and treatment planning of various diseases; yet, it often lacks robustness and suffers from elaborate, time-consuming reconstructions. We trained an artificial neural network (ANN) on simulated QSM values and qBOLD data, tested it in 7 healthy volunteers and compared it to a standard quasi-Newton approach. The ANN reduced the intersubject variability of OEF by regularizing the reconstruction. Moreover, it lowered the reconstruction time from approximately one hour to one second and removed the necessity of accurate parameter initialization through an additional acquisition.
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