Introduction

Quantitative Magnetization Transfer (qMT) imaging aims at quantitatively characterizing the macromolecules exchanging with surrounding water. The most relevant quantitative parameter among those classically estimated using the established two-pool model and MT-prepared spoiled gradient-recalled (SPGR) experiments is the macromolecular proton fraction (MPF), which presents a fair sensitivity to brain tissue changes^1,2.

Two major drawbacks in the current qMT methodology can however bias the MPF value.

First, the free water longitudinal relaxation rate (R_1,f=1/T_1,f) is typically estimated using the Variable Flip Angle (VFA) method, with which it is most often assumed that R_1,f=R₁^3,4. However, VFA experiments are also sensitive to MT effects (both chemical exchange and on-resonance saturation^5,6).

Second, dipolar order effects are generally ignored in the two-pool qMT model in the scope of MT-SPGR sequences with long and single-offset frequency saturation (SOFS) pulses^7,8 (Figure 1a). However, given the saturation pulse durations and powers used in current qMT-SPGR studies along with relatively long dipolar order relaxation time (T_1D) values in the central nervous system^9–11, dipolar order contributions can be expected in SOFS-based acquisitions. Dipolar order effects can be cancelled out using simultaneous symmetric dual-offset frequency saturation (DOFS) pulses that fully decouple the dipolar order from its associated macromolecular Zeeman order^12,13 (Figure 1b).

In this work, we evaluate the bias of on-resonance saturation and dipolar order effects on T_1,f and MPF derived from qMT analyses of VFA- and MT-SPGR data in the human brain.

Methods

Experiments were performed on one healthy volunteer on a 3T clinical system (MAGNETOM Vida, Siemens Healthcare, Erlangen, Germany) with body coil transmission and a 32-channel receive head coil. The protocol included anatomical MPRAGE, B₁⁺ mapping¹⁴, and prototype 3D non-selective VFA-/MT-SPGR sequences (Table 1).

The two-pool model was adapted into general matrix equations in steady-state¹⁵ accounting for any on/off-resonance saturation, relaxation and exchanges, and allows for a joint fitting of VFA- and MT-SPGR data. On-/off-resonance saturation rates of the bound pool were calculated using the super-Lorentzian absorption lineshape including a residual broadening term as proposed by Pampel et al.¹⁶, which is a more suitable lineshape model as compared to the extrapolation method for on-resonance saturation rate estimations^17,18.

Quantitative MT parameters T_2,f, T_2,b, R_1,f (assuming R_1,b=R_1,f), MPF and exchange rate R were estimated from joint B₁⁺-corrected VFA and Z-spectra data¹⁹ using three distinct frameworks: (A) SOFS data and neglecting on-resonance saturation (similar to the framework from Mossahebi et al.¹⁹), (B) SOFS data and accounting for on-resonance saturation, and (C) DOFS data and accounting for on-resonance saturation.

To assess the quantitative parameters in selected regions of interest (ROI), the MPRAGE image was used to retrieve white matter (WM) masks from the JHU probabilistic atlas²⁰ using ANTs²¹ and deep grey matter (DGM) masks using FreeSurfer. Linear regressions and Bland-Altman analyses were performed, and average parameter values and root-mean-square error (RMSE) of the fit were reported.

Results

Representative axial views of T_1,f and MPF maps derived from the three frameworks are presented in Figure 2.

Figure 3 shows agreements and Bland-Altman plots comparing T_1,f and MPF estimated from the three frameworks. First, not accounting for on-resonance saturation leads to an over-estimation of T_1,f and an under-estimation of MPF, especially in WM (Figure 3a/b). Second, whereas the type of MT saturation pulse has minimal impact on the T_1,f outcome (~9.3 to 14.5 ms absolute difference in average between SOFS and DOFS; Figure 3c), the use of DOFS enhances the MPF value prominently in WM (up to 3.4%-MPF increase in absolute value; Figure 3d).

Quantitative MT parameters in selected WM and DGM structures are reported in Table 2. Accounting for on-resonance saturation and using DOFS-prepared data improve the fitting procedure as emphasized by the decreasing RMSE of the fit (Table 2).

Discussion and conclusion

We demonstrated the importance of using DOFS pulses and accounting for on-resonance saturation effects to derive more accurately T_1,f and MPF from the two-pool qMT model applied jointly on VFA- and MT-SPGR data.

Since DOFS pulses remove the coupling between Zeeman and dipolar orders, DOFS data are adequately described by the classical two-pool qMT model, which does not include any dipolar order reservoir¹². This is particularly evident in WM and translated into an increase of the MPF value. For a dipolar order effect to exist, a SOFS pulse power such that R_RFB×T_1D>0.01 is required²² (with R_RFB the saturation rate of the preparation pulse, proportional to the pulse peak B₁). Hence, long and relatively low-power SOFS pulses predominantly create dipolar order effects in WM, associated with long T_1Ds^10,11. Conversely, SOFS pulses induce less dipolar order effects in GM as it is characterized by shorter T_1D values, and hence a lower difference between MPF values derived from SOFS and DOFS data was observed.

Modelling on-resonance saturation remains complex because the super-Lorentzian absorption lineshape varies rapidly around the resonance. Readout pulse durations typically do not exceed one millisecond in 3D non-selective SPGR experiments, corresponding to a large spectral profile. It hence leads to a varying macromolecular saturation as a function of the pulse duration at fixed power⁶. As such, the assumption of a unique frequency saturation may not be highly accurate, calling for further investigations on the modelling of such feature for the establishment of a general model.

Acknowledgements

This work was supported by the French National Research Agency, ANR [ANR‐17‐CE18‐0030] and ARSEP 2020.

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