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Updated: Aug 5, 2026
Quantitative Mapping of Specific Ventilation in the Human Lung using Proton Magnetic Resonance Imaging and Oxygen as a Contrast Agent
Published on: June 5, 2019
MRI-based oxygen extraction fraction mapping using qBOLD, QSM, and hybrid QSM-qBOLD methods: Methodological advances,
T Arun Raj1, Allen Johnson2, K Karthik3
1Medical Image Computing and Signal Processing Laboratory, Kerala University of Digital Sciences Innovation & Technology, Trivandrum, India.
Abstract:
Oxygen extraction fraction (OEF) is a physiological parameter reflecting the fraction of delivered arterial oxygen extracted by cerebral tissue, providing information complementary to perfusion, structural MRI, and conventional BOLD contrast. Positron emission tomography (PET) remains the reference standard for quantitative assessment of OEF and cerebral metabolic rate of oxygen (CMRO₂), but limited availability, radiotracer requirements, and operational complexity have motivated MRI-based alternatives. This review focuses on MRI-derived OEF mapping using quantitative BOLD (qBOLD), quantitative susceptibility mapping (QSM), hybrid QSM-qBOLD (QQ) methods, constrained qBOLD, Bayesian/prior-based reconstruction, and artificial-intelligence-assisted approaches. Classical qBOLD provides a model-based route for estimating oxygenation-related parameters from deoxyhemoglobin-induced signal decay, but remains limited by parameter degeneracy, noise sensitivity, and assumptions regarding venous blood volume, hematocrit, relaxation, and vascular geometry. QSM provides complementary susceptibility information from gradient-echo phase data; however, total susceptibility cannot independently distinguish heme-related venous deoxygenation from non-heme sources such as iron, myelin, calcification, or tissue composition. Hybrid QSM-qBOLD methods address this limitation by jointly using magnitude and susceptibility information to improve susceptibility-source separation and reduce unrealistic assumptions. Recent developments, including constrained qBOLD, temporal clustering, multi-echo complex QQ, Bayesian reconstruction, ANN-based inference, and QQ-NET, aim to improve stability, computational efficiency, and clinical practicality. Validation remains a central challenge because direct MRI-PET comparison studies are limited. Clinical applications are most promising in disorders where oxygen delivery, extraction, and metabolism become uncoupled.
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