Synopsis

Keywords: Quantitative Imaging, Relaxometry, Glympathics, CSF

T2* relaxation in the brain covers a broad range of values, which can be grouped in three, roughly logarithmically spaced, intervals (short, intermediate, very long). Using a multi-echo GRE sequence to quantify T2* is time-efficient for brain parenchyma, but accurate quantification of slow-relaxing water pools, such as CSF, lengthens the acquisition time. In this work, we propose a novel sequence (ES-QUTE) that combines multi-echo acquisition with echo shifting techniques to effectively quantify the whole range of T2* relaxation times in the brain without increasing the scan time. In addition, ES-QUTE simultaneously characterises fast diffusion.

Introduction

Accurate mapping of CSF T2* relaxation has not yet been significantly exploited in vivo, especially with respect to its relevance to the glymphatic system¹. Only scarce, but promising, results are available regarding quantitative T1, T2 and diffusion properties in CSF^2-5. In this study, we propose a new multi-echo gradient echo (GRE) sequence, named Echo-Shifted Quantitative T2* imagE (ES-QUTE), for the acquisition of long TE images and for the T2* quantification of CSF and brain tissues without increasing the scan time. It is further shown that the sequence can provide simultaneous diffusion information for slow-relaxing, fast-diffusing water pools, such as those relevant to glymphatics.

Methods

Figure 1 depicts schematic diagrams of the conventional multi-echo GRE (i.e., QUTE^6,7) and the proposed ES-QUTE sequences. ES-QUTE combines multi-echo GRE with an echo-shifting technique. In contrast to previously proposed echo-shifting approaches^8,9, additional gradients are not immediately provided after the radiofrequency (RF) pulse. The first additional gradient (-A') is applied following the acquisition of p echoes. The dephased spins are rephased by the second additional gradient (+2A') and the first additional gradient in the subsequent sub-TR interval, thereby generating q echoes at long TEs. As a result, ES-QUTE produces T2*-weighted images acquired at both short and long TE.

To validate the proposed sequence, QUTE and ES-QUTE were compared for phantom and in vivo experiments (imaging parameters given in Table 1). Experiments were carried out on a 3T MR scanner (PRISMA, Siemens Healthineers, Erlangen, Germany) using the 20-channel head/neck coil. Flow compensation gradients were introduced along the slice-select and readout directions. In vivo, readout-resolved FID navigator echoes were acquired every 16 echoes to correct the field variation caused by physiological fluctuations¹⁰. For ES-QUTE, the additional (echo-shifting) gradients were applied along the slice-select direction, with the area of the first additional gradient set to 1.25 times that of the slice selection gradient. Half of the acquired echoes were shifted (32 out of 64 in the present data set, including navigator echoes).

Quantification of T2* relaxation time was performed using a mono-exponential decay with Rician noise correction¹¹. Since echo-shifting gradients also produce diffusion weighting, an additional term was included in the fit model for the shifted echoes: $$s(TE)=\left\{\begin{array}{c}M_0\exp\left(-TE/T2^*\right)\quad\text{for unshifted echoes}\\M_0\exp\left(-TE/T2^*\right)\exp(-b \cdot D)=M_0^{\prime}\exp\left(-TE/T2^*\right)\quad\text{for shifted echoes}\end{array}\right.$$ where s(TE) is the acquired signal, M₀ is the signal intensity at zero TE, b describes the diffusion weighting from the additional gradients and D is the diffusivity. The factor exp(-b∙D) is a dimensionless number and was taken into account in the fit by replacing M₀ with M₀'.

Results

Figures 2 and 3 display different TE images and the T2* maps obtained from QUTE and ES-QUTE. Even at very late echoes, the phantom images are devoid of significant artefacts (Fig. 2). Field variations from physiological fluctuations, which give rise to image artefacts in GRE sequences, were corrected using the multiple navigator echoes, fully restoring image quality (Fig. 3). The T2* maps of ES-QUTE were visually similar to those of QUTE in both phantom and in vivo images. Nevertheless, the calculated T2* values in each phantom ROI had a lower standard deviation for ES-QUTE, especially for longer T2* relaxation times. Also, in vivo values showed subtle differences in the quantitative results provided by the two methods, which can be seen in the histograms of grey matter, white matter, and CSF relaxation times (Fig. 3d). ES-QUTE exhibited a greater proportion of higher T2* values (~ around 300ms) in CSF. Figure 4a shows the signal decay from a voxel containing CSF. The TE range in ES-QUTE follows the signal decaying by a factor of two, compared to ~20% signal reduction in QUTE. Figure 4b shows the image derived from the ratio of M₀(QUTE) and M₀'(ES-QUTE) compared to the diffusivity map obtained from EPI-DWI. Using the mean value for D(CSF), derived from DWI, the effective diffusion weighting in ES-QUTE was described by b=357s/mm².

Discussion and Conclusions

The multi-step navigator echo correction was found to be important for image quality and accurate T2* quantification with both QUTE and ES-QUTE. For identical acquisition times, ES-QUTE was able to more than double the TE range of QUTE while identically sampling the short and medium T2* decay. The sequences delivered identical results over the common TE interval. The small differences between the two methods for T2* quantification in WM and GM, seen in Fig. 3d, are due to the fact that all echoes (with Rician noise correction¹¹) were included in the fit. Voxel-specific echo-time limitation based on phase information and SNR¹² will be applied in the future. Most interestingly, differences in the zero TE signal intensity derived from unshifted and shifted echoes (M₀/M₀' in Fig. 4) show a pronounced effect in CSF, which can be attributed to the diffusion weighting by echo shifting gradients. This effect can be further modulated by modifying the strength and orientation of the echo-shifting gradients and will be investigated further. Furthermore, distributing the echo-shifted echoes over several TRs should allow for a more accurate diffusion quantification and/or TR reduction.

In conclusion, ES-QUTE offers a new tool for the quantitative characterisation of slow-relaxing, fast-diffusing water pools with intriguing applications to the study of brain clearance pathways.

Acknowledgements

This work was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 764513.

References

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