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Abstract #1867

Using an Artificial Neural Network for Fast Mapping of the Oxygen Extraction Fraction with Combined QSM and qBOLD

Simon Hubertus1, Sebastian Thomas1, Junghun Cho2, Shun Zhang3,4, Yi Wang2,3, and Lothar R. Schad1
1Computer Assisted Clinical Medicine, Heidelberg University, Mannheim, Germany, 2Department of Biomedical Engineering, Cornell University, Ithaca, NY, United States, 3Department of Radiology, Weill Cornell Medical College, New York, NY, United States, 4Department of Radiology, Tongji Hospital, Wuhan, China

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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