Single-shot multiparametric MRI for separating T₂ effects from dynamic glucose-enhanced contrast

Background: Glucose is a central substrate in cellular metabolism and serves as a non-invasive biomarker for pathological processes. Dynamic glucose-enhanced (DGE) MRI based on chemical exchange saturation transfer (CEST) offers a promising tool for mapping glucose uptake, but its quantification is confounded by glucose-induced changes in T₂ relaxation in addition to glucose concentration. Methods: We developed a single-shot multiparametric CEST (MP-CEST) MRI sequence based on multi-echo spatiotemporal encoding (SPEN), enabling the simultaneous acquisition of T₂ and saturation-weighted proton density (PD) maps within a single scan. To correct for T₂ -related confounding effects in glucoCEST quantification, a two-step correction strategy was employed. First, the saturation-weighted PD maps, which mitigate T₂ -dependent signal attenuation during image acquisition, were used to reconstruct the Z-spectrum, thereby providing a more accurate representation of the true saturation signal amplitude. Second, calibration curves derived from Bloch-McConnell simulations were applied in combination with the simultaneously acquired T₂ maps to compensate for spillover effects in the Z-spectrum, thereby improving glucose-specific CEST contrast. The full framework was validated through phantom experiments and in vivo studies in rat brain and tumor xenograft models. Quantitative performance was evaluated by computing the Pearson correlation between DGE signals and T₂ values before and after correction, as well as by comparing fitted T₂ and PD values with reference maps. Results: Phantom experiments demonstrated high accuracy in PD and T₂ quantification (R² > 0.99). In vivo studies in rat brain and tumor xenografts showed that the proposed correction method significantly reduced the correlation between DGE signals and T₂ values, improving the specificity of glucose-related contrast. In addition, T₂ maps provided complementary structural and physiological information relevant to tumor heterogeneity and tissue microstructure. Conclusions: The proposed MP-CEST approach improves the robustness and accuracy of DGE quantification, offering a more comprehensive metabolic imaging framework applicable to both oncological and neurological research.