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Accelerating Quantitative Chemical Exchange Saturation Transfer MRI using MR Fingerprinting and Synthetic CEST Analysis
Hye Young Heo1,2, Zheng Han1,2, Shanshan Jiang1, Michael Schar1, Peter C.M. van Zijl1,2, and Jinyuan Zhou1,2

1Radiology, Johns Hopkins University, Baltimore, MD, United States, 2Kennedy Krieger Institute, Baltimore, MD, United States

Synopsis

Current CEST-MRI approaches are qualitative in nature, producing contrast that has dependency on both exchange rate and the concentration of the exchangeable protons in metabolites. This limits pH or metabolite concentration specificity unless one of these two parameters can be varied individually in an experiment. Here, we developed a fast CEST imaging technique based on MR fingerprinting approach that can achieve such quantification using a saturation time and strength varied RF scheme. The approach is validated using simulations and ammonium chloride phantoms, and then demonstrated in-vivo for amide proton transfer (APT) MRI.

Introduction

Most current CEST-MRI protocols acquire saturation-weighted images that have multiple components, including magnetization transfer contrast (MTC) and direct water saturation (DS), the relative contribution of which varies with the experimental parameters used. This limits the assessment of quantitative proton exchange rates and concentrations of individual components [1-5]. As a consequence, inconsistent and controversial results have been reported by different research groups due to the choice of different saturation parameters and analysis methods [6-10]. One promising CEST quantification method is to acquire repeated and serial images with varied powers as a function of saturation frequency offset and fit CEST signals using the Bloch-McConnell equations. However, such an approach requires long scan times, which is a major obstacle to clinical translation. In this study, we developed a fast quantitative CEST imaging technique based on MR fingerprinting [11-13] by subgrouping proton exchange models (MRF-SPEM) and validated the approach in simulations and ammonium chloride phantoms. We then demonstrated in-vivo use to generate exchange rate and concentration images for amide proton transfer (APT) MRI.

Methods

In the MRF-SPEM framework, RF saturation frequency offsets (Ω), power (B1), duration (Ts), and repetition time (TR) were varied throughout the acquisition, generating unique CEST signal evolutions for compartments with different exchange rate and concentration (Fig. 1). MRF-SPEM images consisted of two distinct datasets: (1) MTC data with far off-resonance frequency offsets; and (2) APT-weighted data with saturation frequency offsets around 3.5 ppm. The two-pool parameters were incorporated into the three-pool model as prior known information, reducing the number of parameters and fitting uncertainty errors. For actual phantoms, ammonium chloride solutions with varied concentration and pH values were prepared and 1% agarose was added to mimic the MTC pool. We compared MRF-SPEM with a conventional Bloch equation fitting method of high-resolution Z-spectra. For in-vivo studies, five healthy volunteers were scanned on a 3T MRI scanner after informed consent was obtained in accordance with the IRB requirement. Our proposed method cannot be systematically assessed in-vivo due to the lack of an objective ground-truth. To enable effective validation, in lieu of ground-truth, synthetic Z-spectra under varied RF saturation powers were generated using CEST parameters obtained from the MRF-SPEM and then, were compared with experimental measurements. Virtual scanner settings that corresponded to the experimental measurements were used to generate such reference data.

Results and Discussion

For numerical phantom studies, excellent agreement was observed for MRF-SPEM and the ground truth (Fig. 1d). Fig. 2 shows ammonium chloride phantom results. In MTR asymmetry analysis (Figs. 2b and 2e), it is not clear what the contribution (proton exchange rate or concentration) is of the underlying contrast on the observed CEST-weighted images. Quantitative CEST parameter maps obtained from the conventional Bloch equation fitting method using the densely sampled Z-spectra and MRF-SPEM reconstruction are shown in Figs. 2f and 2g, respectively. The CEST parameters estimated by MRF-SPEM were in great agreement with values estimated using the reference quantification. For in-vivo validation at 3 T, Z-spectra were synthesized using estimated parameters from MRF-SPEM and compared with experimentally measured Z-spectra with three different RF saturation powers (Fig. 3). Synthesized and experimentally measured signals were in excellent agreement. As shown in Fig. 4 and Table 1, the semisolid macromolecular proton exchange rates (~40Hz for GM; ~29Hz for WM) and the concentrations (~6M for GM; ~11M for WM) were in good agreement with previous observations [14-16]. The amide proton concentration in gray matter (~266mM) was somewhat higher than that of the white matter (~212mM). In addition, the amide proton exchange rate (~365Hz) of gray matter was significantly faster than that of white matter (~162Hz). Our APT quantification values were in line with previous human studies [17-19] but somewhat faster than those from WEX spectroscopy [20], which is not unexpected in view of more limited detection range for the latter. However, the intrinsic water T1 map calculated from MRF-SPEM overall showed longer relaxation times than the observed T1 map obtained from a look-locker inversion recovery experiment, due to the inclusion of the effect of coupling to a semisolid macromolecular proton pool in the latter case [21].

Conclusions

A fast quantitative CEST imaging technique based on MRF-SPEM was developed, validated in numerical and real phantoms, and demonstrated in-vivo using a synthetic CEST analysis. The present MRF-SPEM scan took just 2 min 50 sec (including B0/B1 mapping) for quantitative APT parameter mapping (as compared to ~11 min for conventional image acquisition). This quantitative approach has the potential to enhance APT-MRI sensitivity to protein content and pH in normal human brain and in many pathologies, such as cancer, stroke, and various psychiatric and neurodegenerative diseases.

Acknowledgements

This work was supported in part by grants from the National Institutes of Health.

References

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Figures

Fig. 1. (a) An example of the saturation schedule for an MRF-SPEM acquisition. Black circles are for two-pool (MTC+DS) model-fitting (10 ppm<Ω<30 ppm and 1 μT<B1<3 μT) and red circles (3 ppm<Ω<4 ppm and 0.5 μT<B1<1.2μT) are for three-pool model-fitting (MTC+DS+CEST). (b) A digital phantom (SNR ≈ 100) consisting of five compartments with varied proton solute-water exchange rates (ksw) and solute concentrations. (c) unique MRF-SPEM signal profiles obtained from five compartments. (d) plots of proton exchange rate and concentration values estimated from MRF-SPEM reconstruction reflecting results from a horizontal profile (the red arrow in (b)). Square brackets indicate the concentration.

Fig. 2. CEST phantom experiments. (a) a phantom with four compartments: (1) pH 4.5, 0.5 M NH4Cl + 1% agarose + PBS, (2) pH 5.0, 0.5 M NH4Cl + 1% agarose + PBS, (3) pH 4.6, 1 M NH4Cl + 1% agarose + PBS, and (4) pH 7.0, 1% agarose + PBS. (b) Z-spectra and MTRasym curves, (c) densely sampled Z-spectra, and (d) MRF-SPEM profiles from four ROIs. (e) MTRasym(2.5 ppm) maps. (f) Quantitative CEST parameter maps from the conventional three-pool Bloch-equation fitting method using the densely sampled Z-spectra (c). (g) MRF-SPEM reconstruction using the MRF-SPEM signal profiles (d).

Fig. 3. (a) Average MRF-SPEM signal evolution profiles (+), and two-pool fitted (solid lines) and three-pool fitted (dashed lines) curves from white matter and gray matter (n = 5). Shaded areas indicate APT effects by subtracting Zlab from Zref. Black and red crosses are two-pool MTC and three-pool APT acquisitions, respectively. (b) Synthetic two-pool (solid lines) and three-pool (+) Z-spectra using parameters obtained from MRF-SPEM. (c) Experimentally measured Z-spectra as a standard for validating MRF-SPEM quantification. (d) Experimentally measured Z-spectra (black +) and two-pool MTC-fitted curves (black lines). Synthetic two-pool (red lines) and three-pool (red +) Z-spectra are shown for comparison.

Fig. 4. (a) Quantitative MTC (exchange rate, kmw and concentration, [semisolid proton]), APT (exchange rate, ksw and concentration, [amide proton]) maps of a representative healthy volunteer human brain. (b) experimentally measured T1 map (T1wobs) from a modified look-locker inversion recovery and APT#(meas) images with RF saturation powers of 1, .5, and 2 μT. (c) Synthetic T1 map (T1w(syn)) and APT#(syn) images with RF saturation powers of 1, .5, and 2 μT. APT# signals were calculated by subtracting the experimental Z-spectra (or synthesized three-pool Z-spectra) from the fitted two-pool Z-spectra (or synthesized two-pool Z-spectra) [2, 7].

Table 1. Estimated semisolid MTC and APT parameters (exchange rates and concentrations) for white matter and gray matter of the healthy volunteer human brain (n = 5). Square brackets indicate the concentration.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)
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