Chemical exchange saturation transfer (CEST) method is an advanced MR imaging approach to detect the signal from metabolites with low concentration, such as protons from amine, amide or hydroxyl. GlucoCEST measurements have been proposed to successfully assess the discrepant concentrations of D-glucose or 2-deoxy-D-glucose (2-DG) in vitro [1]. Using asymmetric analysis, the asymmetric magnetization transfer ratio (MTRasym) and glucoCEST contrast maps can be derived directly from the z-spectrum, which may provide opportunities to quantify altered glucose levels in vivo. Although some previous reports demonstrated the potential role of glucoCEST in detecting the altered glucose assimilation in mouse tumor model, revealing comparable information to 18FDG-PET findings [2,3], poor reproducibility due to its high sensitivity to field inhomogeneity and possible contamination from asymmetric MT and signals from adjacent metabolites, which restrict its further application. More recently, Lorentzian function has been performed to fit the z-spectrum as a combination of multiple components [4], which in corporation with specific mathematical manipulation may provide some possibility for extracting the glucose signal with better accuracy and reliability. As a result, the purpose of this study is to develop the analytical scheme and assess the reliability in simulation as well as phantom study for glucoCEST imaging, and finally validate it in application of the rat brain in vivo. In vivo z-spectrum consists of multiple components including direct water proton saturation, asymmetric MT, aliphatic nuclear Overhauser effect(NOE), amide proton transfer(APT) and other CEST phenomena [5], leading to a challenge of deriving glucoCEST contrast by asymmetric analysis. Fortunately, the signals from the individual components could be approximated using Lorentzian function, which provides some room to calculate the individual signal from Lorentzian line fitting on the inverse z-spectrum more efficiently and accurately. Based on this concept, we first simulated the z-spectrum with signals from the five components at 0(Water), -1.5(MT), -3.2(NOE), 1(Glucose), and 3.5 ppm (APT), to mimic the situation. After testing the reliability of the developed fitting models, this approach was performed with a phantom of 50 mM D-glucose and in vivo rat scan. The glucoCEST imaging was performed on 3 Sprague Dawley rats at a 7.0T animal MR scanner (PharmaScan, Bruker, Erlangen, Germany) with a 72 mm transmitting coil and a quadrature surface coil for receiving. After manual shimming, a turbo rare based CEST sequence with a continuous form MT saturation pulse (duration =800 ms, B1=2.5 μT) was performed before and after administration of 0.2 ml 2-DG (6 mM) with TR/TE=1000/20 ms, FOV=20x20 mm2, matrix size=128x128, slice thickness=1 mm, rare factor=8, acquisition time=16min 16sec, and z-spectrum offset=±5 ppm with 0.167 ppm steps. After data acquisition, a 2D median filter was performed first to alleviate the noise effect. Inverse z-spectrum analysis was then applied to fit the 5 individual components. For glucoCEST contrast in rat brain, the signal was derived by subtracting the fitted pre-contrast z-spectrum from the post-contrast z-spectrum.The demonstration of the fitted inverse z-spectrum and the 5 individual components were in consistent with the simulated data(Figure 1(a)). With decreasing of the sampling rates, similar glucose profiles were obtained with less than 1% differences(Figure 1(b)). Moreover, MTRasym derived from the 50 mM glucose phantom using conventional asymmetric analysis shows a shift of the profile as compared with that from the inverse Lorentzian analytical scheme(Figure 2). The results became even worse when the glucose level decreased. Significant overestimation of the altered glucose contrast was shown in rat brain using asymmetric analysis(Figure 3(a)) as compared to that derived from the proposed method(Figure 3 (b)). This present study demonstrated the ability to extract glucose profile from CEST imaging using the inverse z-spectrum analytical scheme and indicated the feasibility of detecting the altered glucose assimilation in rat brain. Although the acquisition time is relative longer for the clinical application, results from the simulations suggest that there are still some rooms for further acceleration. In addition, greater than 6% increase of the glucoCEST enhancement was observed in vivo after administration of 2DG using the proposed analytical method. The finding also shows more reliable changes of glucose assimilation in both cortical and subcortical regions of the brain due to that the contamination from creatine signal at 2 ppm was eliminated by the subtraction. In conclusion, our preliminary finding demonstrated that the proposed analytical scheme provides an alternative to extract glucose profile and could be more robust to the field inhomogeneity, which may be helpful in further implementation of glucoCEST imaging in disease models.