Enhanced QQ-based Oxygen Extraction Fraction Mapping by Incorporating Realistic Water Diffusion and Vasculature: QQ-MASTER

Motivation: Current gradient echo-based oxygen extraction fraction (OEF) techniques, including QQ, rely on restrictive assumptions to achieve an analytic biophysics model, specifically negligible water diffusion and infinitely long, numerous cylinders. Deviating from these assumptions can lead to inaccurate OEF estimations. Goal(s): To develop a novel, more accurate QQ-based OEF mapping technique, termed QQ-MASTER, by incorporating water diffusion and realistic vascular structures. Approach: We integrated Monte-Carlo (MC) simulations, allowing for water diffusion and realistic vasculature, into the current QQ approach. Results: Compared to QQ, the proposed QQ-MASTER provided more accurate OEF estimations in simulations and demonstrated improved sensitivity to OEF abnormalities in ischemic stroke patients. Impact: The proposed QQ-MASTER enables accurate OEF quantification for both healthy tissue and abnormal stroke lesion by considering comprehensive and realistic physiological scenarios. QQ-MASTER is therefore clinically applicable in various neurologic disorders, aiding in assessing disease severity and developing therapeutic targets.