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A CEST signal quantification method for non-steady state
Yi Wang1, Bing Wu2, Yang Fan2, and Jia-Hong Gao1

1School of Physics, Peking University, Beijing, China, People's Republic of, 2MR Research group, GE Healthcare China, Beijing, China, People's Republic of

Synopsis

For quantitative analysis of CEST signal, it is crucial to decrease or eliminate the influence of parameters unrelated to chemical exchange thus emphasizing chemical exchange weight. Recently inverse Z-spectrum method realized analytical calibration but only in situation of steady state. We propose a novel analytical calibration method suitible to non-steady state situation, calculating new indexes which reflect chemical exchange weight better than those commonly used, and verifying its performance in phantom and in vivo experiment.This calibration method will be greatly helpful in quantitative CEST data analysis.

PURPOSE

One primary limitation of CEST is the quantification reproducibility, which is intrinsically challenging due to the simultaneous and competing spillover and T1 decay effects. Recently, a new metric1 has been proposed to eliminate these competing effects to deliver a CEST effect only quantification. However, this model is under the requirement of being in a steady state, which is difficult to satisfy in practice due to the restricting long saturation period needed (>3s). In this work, we extent this metric to transient state while eliminating the contaminating effects, and verify its performance in phantom and in vivo experiment.

METHOD

Zaiss2,3 used the Z value with a continuous wave rectangular pulse for quantification:

Z=(PzeffPzZiniPzcos(θ)R1R1ρ)exp(R1ρtsat)+Pzcos(θ)R1R1ρ(1)

θ is defined as θ=tan1(γBbΔω), R1 and R1ρ are reciprocal of T1 and T1ρ. Pz and Pzeff are projection factors dependent on the pulse sequence. The first part (weighted by exp(R1ρtsat) represents the transient effects of CEST saturation, which was neglected in previous reports [1].

As R1ρ=Rcalib1ρ+Rex, in a general case we can treat influence of exchangeable solutes Rex as a perturbation to R1ρ. As Z in Equ.(1) is a function of R1ρ, for small R1ρ it is natural that ΔR1ρ=ΔZ/ZR1ρ. Under the assumption of a continuous square wave, it can be expressed as:

ΔR1ρ=ΔZ/{[cos2θ(1exp((TRtsat)R1a)R1a/R1ρ)exp(R1ρtsat)(tsat)+cos2θR1aR21ρexp(tsatR1ρ)cos2θR1aR21ρ]/(1exp(R1aTR))}(2)

For proper ΔZ=ZrefZlab, direct water saturation could be excluded then ΔR1ρ=Rex. According to analytical solution of Rex [2], for high exchange rate (kbR2) solutes and small B1 (ω1kb), a simplification could be:

Rcalibex=Rexsin2θfbkbδω2k2b+Δω2b+ω21fbkbδω2k2b+Δω2b(3)

Then a new transient state CEST effect metric could be defined as ΔRcalib1ρ=ΔR1ρsin2θ=Rcalibex.

EXPERIMENTS

For verification of the proposed metric, CW rectangular saturation pulse followed by a SE-EPI sequence was implemented and used for CEST acquisition. Several experiments were designed to assess impacts of various experimental factors: a) phantoms with the same solutes densities and but different MnCl2 densities (hence different relaxation times) were prepared and used to test the impacts of T1 and T2; b) data acquisitions with different TR (2s, 3s, 5s) and lengths of saturation (0.5s, 0.75s, 1s, 1.25s, 1.5s) were performed to test impacts of TR and tsat; c) CEST saturations with differentmagnitudes (1uT, 1.25uT, 1.5uT, 1.75uT, 2uT, 3uT) were performed to test the impacts of B1. In vivo data was acquired on a healthy subject with varying TR/tsat: 2s/1.5s, 1.5s/1s, 7s/5s. The resulting CEST quantification using the proposed ΔRcalib1ρ for phantom and in vivo data were compared to those using conventional MTRasym.

RESULTS

As shown in Fig.1, for phantom 1 and 2 (Fig.1a) that have the same solutes densities (the same CEST effects) but different relaxation times, conventional were quite different; whereas ΔRcalib1ρ were nearly identical that better reflect the underlying CEST effects. Also MTRasym was also impacted by B1 inhomogeneity (Fig.1b) leading to heterogeneous CEST quantification within the phantoms, while ΔRcalib1ρ was not. In Fig.2, where the TR and saturation period were varied, conventional MTRasym were dramatically different with different scan parameter settings, whereas ΔRcalib1ρ measure stayed nearly unchanged as desired. In Fig.3, B1 of the CEST saturation, which has largest impacts on conventional CEST measurement, was varied. It can be seen that for a range of B1 used, the MTRasym measure not only changed in magnitude, the location of its spectral peak was also shifted; whereas ΔRcalib1ρ stayed fairly constant for small level of B1 variation (<3uT), and although the magnitude level changed for larger B1 the position of the spectral peak stayed unchanged. In in vivo experiments using different TR/tsat
(Fig.4), it was also observed that MTRasym varied considerably as compared to ΔRcalib1ρ when MTRasym is dramatically nonzero (arrowed).

DISCUSSION and CONCLUSION

Conventional CEST measurement is contaminated with both competing signal sources and transient signal state, although the former may may be eliminated via proper correction1,4 , steady state requires restrictingly long saturation period. In this work, an analytical metric for transient state that also eliminates spillover and T1 decay effects is proposed. This metric is analytical and hence eliminates the computation burden and error in empirical fitting. In the comparison with conventional CEST measure, this metric was seen to be largely immune to variation in relaxation rates, scan parameters as well as saturation pulse magnitude, hence better reflect the underlying unchanged CEST effects in transient states. The use of this metric would be beneficial for application where quantitative measure is needed, such as in tumor grading.

Acknowledgements

No acknowledgement found.

References

1. Moritz Zaiss, Junzhong Xu, Steffen Goerke et al., Inverse Z-spectrum analysis for spillover-,MT-, and T1-corrected steady-state pulsedCEST-MRI – application to pH-weightedMRI of acute stroke, NMR Biomed. 2014; 27(3): 240-252

2. Moritz Zaiss, Peter Bachert, Chemical exchange saturation transfer (CEST) andMR Z-spectroscopy in vivo: a review of theoreticalapproaches and methods, Phys. Med. Biol. 2013; 58(22): 221-269

3. Moritz Zaiss, Peter Bachert, Exchange-dependent relaxation in the rotatingframe for slow and intermediate exchange –modeling off-resonant spin-lock and chemicalexchange saturation transfer, NMR Biomed. 2013; 26(5): 507-518

4. Junzhong Xu, Moritz Zaiss, Zhongliang Zu et al., On the origins of chemical exchange saturationtransfer (CEST) contrast in tumors at 9.4 T, NMR Biomed. 2014; 27(4): 406-416

Figures

FIG.1 Phantom 1 and 2 share the same glucose and glutamate densities (both 10mmol/L). By adding 6mg/L MnCl2, T1 and T2 in phantom 2 are about 2s and 0.24s, while those in phantom 1 are about 3s and 0.8s. (c)(e) MTRasym image and curves, (d)(f)corresponding ΔRcalib1ρ image and curves.

FIG.2 Phantom with 5mmol/L D-glucose,10mmol/L glutamate and 8mg/L MnCl2 was prepared, pH was adjusted to about 5.8. (a) MTRasym curves from different B1, (b) ΔRcalib1ρ curves from different B1.

FIG.3 phantom with 10mmol/L D-glucose and 6mg/L MnCl2 was prepared, pH was adjusted to 7.0. (a)(b) MTRasym and ΔRcalib1ρ curves for different tsat.

FIG.4 (a) mask of a healthy subject’s brain. (b)(c) MTRasym and ΔR1ρ curves with different scan parameters and calibration methods.



Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)
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