A Generalized Approach to Solving Deep Learning‐Based Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level Dependent Magnitude ( QSM  +  qBOLD or QQ ) for Oxygen Extraction Fraction ( OEF ) Mapping Across Diverse Acquisition Schemes

PURPOSE: QQ, a recently proposed oxygen extraction fraction (OEF) mapping technique combining quantitative susceptibility mapping (QSM) and quantitative blood oxygen level-dependent (qBOLD) (QSM + qBOLD = QQ), generates OEF maps noninvasively from a single routine MRI sequence, without requiring vascular challenges used in other OEF approaches. A deep learning approach, QQ-NET, further enables rapid 3D OEF reconstruction (˜1.5 min), but it is trained on a fixed echo-time (TE) scheme and must be retrained whenever acquisition protocols differ, limiting its clinical applicability. This study introduces QQ-F, a novel deep learning approach designed to eliminate the need for retraining. METHODS: QQ-F incorporates a feature extraction unit that derives QQ model-related features as inputs, rather than relying directly on raw signals. For a fair comparison, QQ-F was trained using the same 3D multi-echo gradient echo (mGRE) dataset as QQ-NET, acquired from 26 ischemic stroke patients. Both models were tested using simulations and data from 24 multiple sclerosis (MS) and 30 dementia patients acquired with varying TE sequences. RESULTS: In simulations, QQ-F provided more accurate OEF maps than QQ-NET with lower mean absolute error. In patient datasets-particularly dementia datasets, where TE values differed substantially from QQ-NET's training protocol-QQ-F yielded significantly higher lesion-to-normal tissue contrast than QQ-NET, indicating superior robustness to acquisition variability. CONCLUSION: QQ-F enables deep learning-based QQ OEF mapping across diverse MR acquisition protocols without retraining, thereby enhancing the clinical scalability of QQ-based OEF mapping.