Regional Alveolar Partial Pressure ofOxygen Measurement with Parallel Accelerated Hyperpolarized Gas MRI

Rationale and Objectives: Alveolar oxygen tension (Pao₂) is sensitive to the interplay between local ventilation, perfusion, and alveolar-capillary membrane permeability, and thus reflects physiologic heterogeneity of healthy and diseased lung function. Several hyperpolarized helium (³He) magnetic resonance imaging (MRI)-based Pao₂ mapping techniques have been reported, and considerable effort has gone toward reducing Pao₂ measurement error. We present a new Pao₂ imaging scheme, using parallel accelerated MRI, which significantly reduces measurement error. Materials and Methods: The proposed Pao₂ mapping scheme was computer-simulated and was tested on both phantoms and five human subjects. Where possible, correspondence between actual local oxygen concentration and derived values was assessed for both bias (deviation from the true mean) and imaging artifact (deviation from the true spatial distribution). Results: Phantom experiments demonstrated a significantly reduced coefficient of variation using the accelerated scheme. Simulation results support this observation and predict that correspondence between the true spatial distribution and the derived map is always superior using the accelerated scheme, although the improvement becomes less significant as the signal-to-noise ratio increases. Paired measurements in the human subjects, comparing accelerated and fully sampled schemes, show a reduced Pao₂ distribution width for 41 of 46 slices. Conclusion: In contrast to proton MRI, acceleration of hyperpolarized imaging has no signal-to-noise penalty; its use in Pao₂ measurement is therefore always beneficial. Comparison of multiple schemes shows that the benefit arises from a longer time-base during which oxygen-induced depolarization modifies the signal strength. Demonstration of the accelerated technique in human studies shows the feasibility of the method and suggests that measurement error is reduced here as well, particularly at low signal-to-noise levels.