Synopsis

Keywords: Quantitative Imaging, CEST & MT

In this study, we proposed a new data-postprocessing method to specifically quantify the APT and NOE effects based on two canonical CEST MRI acquisitions with double saturation powers (DSP). First, Numerical simulations based on Bloch equations is used to demonstrate that the proposed method can detect the APT and NOE effects with a removal of background signals from the direct water saturation (DS) and the semi-solid magnetization transfer (MT) and the CEST signals that originate from the fast-exchange pools. Then, an in vivo validation of the proposed method is conducted using an animal tumor model at a 4.7-T scanner.

Purpose

Detections of the APT effect and the NOE(-3.5) effect with a high specificity are still challenging since their CEST signals are overlapped with background signals from the direct water saturation (DS) and the semi-solid magnetization transfer (MT) and the CEST signals that originate from the fast-exchange pools. In this study, we propose a data-postprocessing method to quantify the APT and NOE effects with a high specificity.

Methods

In this study, we proposed a new data-postprocessing method to specifically quantify the APT and NOE(-3.5) effects based on two canonical CEST MRI acquisitions with double saturation powers (DSP), where the signal acquired at a low saturation power (ω_{1_L} = 0.5 µT) is directly used for the label signal (S_L) and the signal (S_H) acquired at a high saturation power (ω_{1_H} = 1 µT) is processed by a well-tailored mathematical transformation to create a reference signal (S_R). This transformation is derived as,
$$S_R\left(\omega_{R F}\right)=\frac{S_0}{1+\left(\frac{S_0}{S_H\left(\omega_{R F}\right)}-1\right) \frac{\omega_{1_L}^2}{\omega_{1_H}^2}} .$$
where ω_RF is the frequency offset of the saturation RF relative to the water, and S₀ is the control signal measured without the RF saturation. Apparent exchange-dependent relaxation (AREX) ¹ is used to process the label and reference signals to quantify the CEST effects of the APT and NOE(-3.5),
$$A R E X_{D S P}\left(\omega_{R F}\right)=\left(\frac{S_0}{S_L\left(\omega_{R F}\right)}-\frac{S_0}{S_R\left(\omega_{R F}\right)}\right) R_{1 w}$$
The specificity of the proposed method to the APT and NOE effects are demonstrated with numerical simulations based on Bloch equations. Then, an in vivo validation of the proposed method for quantifying the APT and NOE(-3.5) effects is conducted using an animal tumor model. In the DSP-CEST MRI, after the signal preparation based on a rectangular RF saturation pulse with a duration of 5 seconds, single-shot spin-echo echo planar imaging (SE-EPI) is used for a 2D image readout with parameters: matrix size = 64 × 64, field of view = 30 × 30 mm². DSP-CEST Z-spectra (ω₁ = 0.5 µT and 1 µT) were acquired with the frequency offsets from -10 ppm to 10 ppm. An inversion recovery method is used to measure R_1w, that was used for the calculation of the AREX_DSP metric. All measurements were performed on a Varian 4.7T magnet with a 38-mm receive coil. The proposed method was compared with the Lorentzian difference (LD) analysis ² that seek to obtain the reference signal by fitting the background DS and MT effects and has been developed to quantify the CEST and NOE effects.

Results and discussions

In Fig. 1, numerical simulations demonstrate how the background signals from the MT and fast-exchange amine (at 2.0 ppm) are suppressed in the DSP-CEST imaging of the APT and NOE(-3.5). The results show that AREX_DSP is not influenced by the concentration of the MT and the amine. Fig. 2 show that the specificity of the proposed method can be kept for different concentrations and exchange rates of the APT and NOE. Fig. 3 shows images of R_1w, MT pool size ratio, NOE(-3.5) and APT that are acquired from a typical rat bearing tumors. The maps of R_1w, MT pool size ratio, AREX_LD quantified NOE(-3.5) and AREX_DSP quantified NOE(-3.5) exhibit hypointense intensities. Furthermore, Fig. 4 shows results of statistical analysis based on ROIs that were delineated from the tumors and the contralateral normal tissues in the rat brains. The R1w, MT pool size ratio, AREX_LD and AREX_DSP quantified NOE(-3.5) have significant differences (p < 0.05) between the tumors and the normal tissues. The NOE(-3.5) signal changes detected with the established LD and the DSP-CEST method are consistent, which demonstrates a feasibility the DSP-CEST method for the in vivo experiments. Furthermore, the Z-spectra acquired from the tumors and the contralateral normal tissues are displayed in Fig. 5. Note that although the background signals (beyond ± 5ppm) in the CEST Z-spectra have considerable differences, the corresponding DSP-CEST signals match well in the Z-spectra, which also observed in the simulations and suggests that the MT effects can be suppressed by the two methods. In the AREX_LD and AREX_DSP spectra, the APT at 3.5 ppm, the guanidinium CEST at 2 ppm (a fast-exchange site), the NOE at -1.6 and -3.5 ppm can be observed. Note that the ratio of the signal at 3.5 ppm to the signal at 2.0 ppm in the DSP-CEST Z-spectra is smaller than in the LD-CEST Z-spectra, which should be due to the suppression of the fast-exchange CEST effects at 2.0 ppm in the DSP-CEST.

Conclusion

The proposed data-postprocessing method can quantify the APT and NOE effects with high specificities.

Acknowledgements

No acknowledgement found.