A deep neural network for Oxygen Extraction Fraction (OEF) mapping based on No Training

Motivation: Quantitative mapping of oxygen extraction fraction (OEF) is critical to evaluate brain tissue viability and function in neurologic disorders. A recent deep learning-based OEF technique, namely QQ-NET, provided OEF maps sensitive to disease-related abnormalities. However, QQ-NET suffers from training data dependency and requires extensive amount of training data. Goal(s): Our goal is to resolve the training data dependency issue. Approach: We developed a novel deep learning scheme, namely QQ-NTD, which minimizes the biophysics model fidelity on each single dataset. Results: The proposed QQ-NTD provided a more accurate OEF than QQ-NET. Impact: With no need for extensive training and independence from input imaging parameters, our novel deep learning approach, QQ-NTD, can be used readily used to obtain OEF maps in clinical setting.