Quantitative functional BOLD (qfBOLD): A combined gradient-echo and spin-echo framework for oxygen extraction...

Area of Science:

• Neuroimaging
• Biophysics
• Medical Physics

Background:

• Accurate oxygen extraction fraction (OEF) mapping is crucial for understanding brain metabolism and function.
• Existing MRI methods like calibrated fMRI (cfMRI) often require complex physiological measurements (e.g., cerebral blood flow) and can be limited in applicability.
• Quantitative BOLD (qBOLD) methods typically rely on baseline signal values, which may not fully capture dynamic physiological changes.

Purpose of the Study:

• To introduce a novel MRI framework, quantitative functional BOLD (qfBOLD), for mapping OEF.
• To develop a method that isolates deoxyhemoglobin (dHb) effects by exploiting dynamic signal variations rather than baseline values.
• To enable simultaneous decoupling of OEF and cerebral blood volume (CBV) without requiring separate cerebral blood flow (CBF) measurements.

Main Methods:

• The qfBOLD framework utilizes temporal variations in Gradient Echo (GE) and Spin-Echo (SE) Blood-Oxygen-Level-Dependent (BOLD) signals.
• It leverages isometabolic modulations in CBF to induce dynamic changes in relaxation times.
• The method relies on the interaction between dHb-induced extravascular field distortions and water diffusion, analyzed via Monte Carlo simulations and in vivo feasibility studies.

Main Results:

• In vivo feasibility assessment during a hypercapnic breath-holding task yielded OEF values of 37.0±2.9% in gray matter (GM) and 41.6±2.9% in white matter (WM).
• qfBOLD demonstrated significant correlations with cfMRI in GM (r=0.71, p<10-3).
• Correlations were also observed with relaxometry-based measures in the superior sagittal sinus for both GM (r=0.51, p<0.05) and WM (r=0.61, p<0.01).

Conclusions:

• The qfBOLD framework offers a promising advancement for OEF mapping using MRI.
• This method enhances signal-to-noise ratio and spatiotemporal resolution, applicable to both GM and WM.
• Future work will focus on improving accuracy by attenuating intravascular signals and refining WM modeling.