Development and Validation of MRI Mapping of Brain Oxygen Metabolism for Clinical Use - The objective of this proposed research is to develop a noninvasive, challenge-free, and widely available method for quantitative mapping of cerebral oxygen extraction fraction (OEF). As the brain continuously consumes 20% of the total oxygen supply, oxygen deficiency easily causes severe brain tissue damages as in hypoxia in ischemia in stroke and Alzheimer's disease. Regional OEF is an essential, direct biomarker for tissue viability and function and highly desired for evaluating and stratifying treatments in these neurologic disorders. Widely distributed MRI provides the potential to overcome the 15O PET, the current reference standard but clinically not used due to its limited availability. In MRI, quantitative mapping of OEF requires estimating deoxyheme concentration [dH] from the MRI signal. Three major approaches have been proposed to estimate [dH] from MRI magnitude signal. However, they commonly suffer from poor sensitives and burdensome data acquisition schemes as the MRI magnitude signal which they utilize has a complex dependence on [dH]. Consequently, no MRI-based OEF mapping has been routinely used in clinical setting. Furthermore, none of these methods have been validated against the current reference standard, 15O PET. Recently, we developed a promising, novel, non-invasive MRI-based OEF method that requires no vascular challenge and utilizes a single routine MR sequence. By integrating quantitative susceptibility mapping (QSM) modeling of often neglected MRI phase signal and quantitative blood oxygenation level dependent (qBOLD) modeling of MRI magnitude signal, our model (QSM+qBOLD=QQ) can distinguish deoxyheme iron in venous vasculature from diffusive other susceptibility sources. In our preliminary data, QQ has been validated against 15O-PET in healthy adults and showed OEF abnormalities in ischemic stroke, multiple sclerosis, and brain tumor. However, for clinical use, data acquisition and processing scheme of QQ should be improved to ensure the accuracy of OEF as a lack of data at short echo time and a non-optimal signal modeling with gradient-based solvers in current setting hinders the accurate OEF estimation. In this K99/R00 project, we will establish a clinically readily applicable MRI toolset for quantitative OEF mapping, which is validated and available to every MRI scanner, by improving QQ. We will achieve this through 4 specific aims. Aim1. Develop optimal data acquisition for MRI-OEF mapping. Aim 2. Develop data processing algorithms for robust OEF estimation. Aim 3. Perform technical validation of MRI-OEF against 15O PET. Aim 4. Perform clinical validation of MRI-OEF in patients with intracranial stenosis. Our experience and preliminary data give us confidence that we will very likely succeed this this proposed project. In a timely fashion, the project will lead to a novel, validated, non-invasive, challenge-free, routinely usable and quantitative MRI OEF mapping, offering the potential to replace invasive, complicated current standard 15O PET OEF. This tool will lead to better understanding and management of neurovascular disorders, e.g. stroke.
