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.
Authors
Affiliations (4)
Affiliations (4)
- Department of Biomedical Engineering, George Washington University, Washington, DC, USA.
- Department of Biomedical Engineering, State University of New York at Buffalo, Buffalo, New York, USA.
- Department of Radiology, Weill Cornell Medicine, New York, New York, USA.
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Abstract
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. 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. 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. 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.