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Updated: Aug 6, 2026
Phase-Resolved Functional Lung MRI for Pulmonary Ventilation and Perfusion (V/Q) Assessment
Published on: August 9, 2024
A Generalized Approach to Solving Deep Learning-Based Quantitative Susceptibility Mapping and Quantitative Blood
Tian Qiu1, Ada Ally2, Arpita Misra1
1Department of Biomedical Engineering, George Washington University, Washington, DC, USA.
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.
