Chemical Exchange Saturation Transfer (CEST) MRI is sensitive to dilute metabolites with exchangeable protons, allowing tissue characterization in diseases such as acute stroke and tumor. CEST quantification using multi-pool Lorentzian fitting is challenging due to its strong dependence on image signal-to-noise ratio (SNR), initial values and boundaries. Herein we proposed an Image Downsampling Expedited Adaptive Least-squares (IDEAL) fitting algorithm that quantifies CEST images based on initial values from multi-pool Lorentzian fitting of iteratively less downsampled images until the original resolution. The IDEAL fitting in phantom data with superimposed noise provided smaller coefficient of variation and higher contrast-to-noise ratio at a faster fitting speed compared to conventional fitting. We further applied the IDEAL fitting to quantify CEST MRI in rat gliomas and confirmed its advantage for in vivo CEST quantification. In addition to significant changes in amide proton transfer and semisolid macromolecular magnetization transfer effects, the IDEAL fitting revealed pronounced negative contrasts of tumors in the fitted CEST maps at 2 ppm and −1.6 ppm, likely arising from changes in creatine level and nuclear overhauser effects, which were not found using conventional method. It is anticipated that the proposed method can be generalized to quantify MRI data where SNR is suboptimal.

pH-sensitive amide proton transfer (APT) MRI provides a surrogate metabolic biomarker that complements the widely-used perfusion and diffusion imaging. However, the endogenous APT MRI is often calculated using the asymmetry analysis (MTRasym), which is susceptible to an inhomogeneous shift due to concomitant semisolid magnetization transfer (MT) and nuclear overhauser (NOE) effects. Although the intact brain tissue has little pH variation, white and gray matter appears distinct in the MTRasym image. Herein we showed that the heterogeneous MTRasym shift not related to pH highly correlates with MT ratio (MTR) and longitudinal relaxation rate (R1w), which can be reasonably corrected using the multiple regression analysis. Because there are relatively small MT and R1w changes during acute stroke, we postulate that magnetization transfer and relaxation-normalized APT (MRAPT) analysis increases MRI specificity to acidosis over the routine MTRasym image, hence facilitates ischemic lesion segmentation. We found significant differences in perfusion, pH and diffusion lesion volumes (P<0.001, ANOVA). Furthermore, MRAPT MRI depicted graded ischemic acidosis, with the most severe acidosis in the diffusion lesion (−1.05±0.29%/s), moderate acidification within the pH/diffusion mismatch (i.e., metabolic penumbra, −0.67±0.27%/s) and little pH change in the perfusion/pH mismatch (i.e., benign oligemia, −0.04±0.14%/s), providing refined stratification of ischemic tissue injury.