Synopsis

Keywords: Data Analysis, CEST & MT

CEST quantification is challenging because the measurement depends on the scan protocols. Also, T1 normalization is not straightforward under non-equilibrium conditions. Using multi-pool simulations and phantom experiments, our study evaluated quasi-steady-state(QUASS) algorithm-boosted CEST quantification. The 3-pool CEST simulation showed significant T1 dependencies due to complex interactions among Ts, TR, and T1. Such dependencies were corrected with QUASS reconstruction. In addition, a multi-T1 phantom was used to evaluate the quantification. Whereas the apparent CEST MRI showed significant dependence on Ts, TR, and T1, accurate CEST quantification was demonstrated with the spinlock-model-based fitting of QUASS CEST MRI.

Introduction

CEST MRI is a sensitive means for detecting dilute labile protons and their exchange properties (1). It has increasingly been utilized in applications, including imaging acute stroke and tumors (2-5). However, T1 normalization is not straightforward when the assumption of long saturation and repetition times are not met because of the complex signal dependence on scan parameters (6). Because T1 change is common under pathological conditions such as acute ischemia and tumor, in vivo CEST-weighted contrast often reflects mixed contributions from multiple origins (7,8). Therefore, it is necessary to address the limitation of semiquantitative CEST analysis to further develop in vivo CEST MRI. Our study aimed to evaluate the accuracy of CEST quantification under practical experimental conditions. The recently developed quasi-steady-state (QUASS) algorithm reconstructs the equilibrium CEST scans from the CEST measurements based on the protocol parameters and parametric T1 map(9,10). Our study here evaluates the accuracy of QUASS CEST quantification in multi-pool CEST systems.

Methods

We simulated a representative 3-pool CEST effect with Bloch-McConnell equations. For the amide (3.5 ppm) and guanidyl (2 ppm) groups, we assumed their labile proton concentration and exchange rates were 1:1500 and 100 s-1, and 1:1000 and 50-1, respectively. We varied the bulk water T1 from 1 to 2 s, with intervals of 0.1 s, with a representative T2 of 50 ms. Z spectra were simulated, under serially and independently varied Ts and Td, from 1.5 to 5 s, with intervals of 0.5 s. We prepared a multi-vial phantom with 300 mM Nicotinamide (NAM) and 20 mM creatine (Cr) at pH of 6.86. We doped the sample with manganese chloride from 0 to 50 µM, with increments of 10 µM. MRI scans were performed on a 7 Tesla scanner using a CEST EPI sequence. We obtained Z-spectrum from -1 to 4 ppm with intervals of 0.05 ppm. We collected the data using five sets of Ts/TR: 1.5 s/ 4 s, 2 s /5 s, 2.5 / 6 s, 3 s/ 7.5 s, and 4 s/10 s (2 averages, B1= 1 µT). In addition, T1-weighted images were acquired with seven inversion delays from 0.1 s to 5 s.

Results

Figure 1a shows 3D plots of the amide and guanidyl CEST effects as functions of Ts and T1. To better illustrate the CEST effect, the amide (Figure 1b) and guanidyl (Figure 1c) signals were plotted separately against T1 for the illustrative condition of equal Ts and Td to avoid crowding the figure. It shows nonlinear and significant T1 dependence. In comparison, the QUASS CEST MRI was solved as functions of Ts and Td, which had little variation against such scan parameters (Figure 1d). The amide (Figure 1e) and guanidyl (Figure 1f) signals determined from QUASS reconstruction were plotted separately against T1 for Ts and Td, which overlapped well with little T1 dependence.The apparent- and QUASS-Z spectra showed three labile groups (two amide groups at 3.3 and 2.5 ppm and a guanidyl group at 1.9 ppm). In all vials, the spinlock model-based solution showed satisfactory fittings for apparent Z-spectral (Figure 2) and QUASS Z-spectral (Figure 3). We compared the NAM amide@ 3.3 (red), NAM amide@2.5 ppm (magenta), and Cr guanidyl @1.9 ppm (blue) CEST effects as a function of T1 in Figure 4. The mean and standard deviations for CEST signals at 3.3, 2.5, and 1.9 ppm were 4.42±0.76%, 1.83±0.32%, and 2.93±0.48%, respectively. The corresponding CEST peaks determined from the QUASS analysis were 5.6±0.20%, 2.33±0.10%, and 3.73±0.11%, respectively. In addition, the results determined from the apparent Z-spectra showed large standard deviations and significant correlations with T1. The confidence intervals for the slope were (-1.23, -0.75), (-0.51, -0.26), and (-0.78, -0.56) for 3.5, 2.5, and 1.9 ppm, respectively. In comparison, the solution from the QUASS Z-spectra had no significant correlation with T1 for all three chemical shift offsets.

Conclusion

Our study evaluated the accuracy of multi-pool CEST quantification under practical experimental conditions with numerical simulations and phantom experiments. Whereas the apparent CEST effects were subject to a complex T1 dependence, the QUASS reconstruction enabled CEST quantification independent of T1. Combining the spinlock model and QUASS reconstruction is advantageous for accurate multi-pool CEST quantification.

Acknowledgements

This study was supported in part by NIH grants R01NS083654, P51OD011132, and Emory Synergy Award.

References

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