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Analysis of magnetization transfer (MT) influence on quantitative mapping of T2 relaxation time
Dvir Radunsky1, Tamar Blumenfeld-Katzir1, Osnat Volovyk2, Tal Assaf2, Daniel Barazany3, Galia Tsarfaty4, and Noam Ben-Eliezer1,5,6

1Department of Biomedical Engineering, Tel Aviv University, Tel Aviv, Israel, 2Department of Chemical Physics, The Weizmann Institute, Rehovot, Israel, 3Strauss computational neuroimaging center, Faculty of Life sciences, Tel Aviv University, Tel Aviv, Israel, 4Department of Diagnostic Imaging, Sheba Medical Center, Ramat Gan, Israel, 5Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 6Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States

Synopsis

Quantification of T2 values is valuable for a broad range of pathologies, yet, highly challenging due to the contamination of rapid multi-echo spin-echo (MESE) protocols by stimulated and indirect echoes. Bloch-simulations based methods, such as the echo modulation curve (EMC) algorithm, take these signal fluctuations into account and produce accurate, precise, and reproducible T2 values. This work provides detailed analysis of magnetization transfer (MT) effect on MESE signal and on the ensuing T2 values, investigating different protocol settings, and using three models: in vitro urea phantom, ex vivo horse brain and in vivo human brain.

Target audience

Introduction

Quantitative MRI techniques aim to produce accurate and reproducible parameter values. Quantitative T2 (qT2) evaluation is particularly valuable, with proven applicability for a wide range of pathologies1–5. Accurate T2 mapping, however, is highly challenging in clinical settings due to the long scan times of full spin-echo (SE) acquisitions, and the inherent contamination of fast Multiecho-SE (MESE) protocols by stimulated and indirect echoes.

Further to that, the measurement of T2 can also be biased by the transfer of magnetization (MT) between macromolecules and free water molecules. MT is associated with two types of signal loss; The first is MT saturation (MTSAT) mediated by cross-relaxation or chemical-exchange between the water and the macromolecular pool (MMP)6,7. This type of MT is instigated by the large number of RF pulses employed in MESE6–11, which affect both the slice-of-interest and other slices owing to the MMP’s broad spectral width. The second mechanism relates to the direct attenuation (MTDIR) of the water pool by incomplete T1 recovery or inter-slice interactions.

This work analyzes the effect of MT on MESE qT2 values, processed using the Bloch simulations based echo-modulation-curve (EMC) algorithm12–14. Three models were investigated: in vitro urea phantom having a well-defined spectral content; ex vivo horse brain presenting a physiological MMP content, yet free of flow or motion artefacts; and in vivo human brain. Attention was given to the different influence of MT on the acquired signal vs. its influence on the ensuing T2 values, and under various protocol settings.

Methods

Urea phantom: six caulked 100ml tubes were prepared for a range of urea concentrations: 0, 0.5, 1, 2, 3 and 4 Molars. Tubes were doped with 0.25mM MnCl2, reducing their T2 / T1 ratio to physiological values.

Horse brain: fixed horse brain was induced in PBSx1 solution for 24h prior to scanning, removing formaldehyde residues and negating the fixation effects on T2 and MT15. During scan, brain was induced in proton-free FC-770 Fluorinert fluid.

Human brain: healthy 30 y/o male, with informed consent.

MRI scans: experiments were performed on a whole-body 3T scanner (Siemens Prisma). Scans included single-voxel spectroscopic (SVS) MRS, MESE, IR and SSE. Scans parameters are detailed in Table 1.

Data post-processing: quantitative T1 maps were generated using standard fitting to IR signal model16; qT2 maps were reconstructed from MESE data using the EMC algorithm13, and from SSE data using conventional fitting to exponential model; MT ratio (MTR) was calculated using: $$$\frac{m_0-m_S}{m_0}\cdot100\%$$$. Mean ± SD of quantitative values were calculated for selected regions of interest (ROIs) (see Fig. 1).

Results

SSE vs. MESE: Fig. 2a illustrates the reduction of T1 and T2 with increased urea concentration – caused by increased dipolar coupling. While consistent reduction is observed for both SSE (-6.4%) and MESE (-12.2%), a dispersive pattern of T2 emerged between the two protocols due to two competing effects: diffusion in SSE vs. MTSAT in MESE. Similar behavior was observed for the horse brain, where MESE T2 values were consistently underestimated compared to SSE (P<0.001), with an average underestimation of 8.9±2.9% for all ROIs (see Fig. 1b). These results suggest that MT-related underestimation of T2 occurs already in single-slice MESE acquisition. Fig. 2b shows a water-suppressed spectrum of the 4M urea tube.

MESE – ETL dependence: Fig. 3 presents the MTR and T2 in single-slice MESE acquisition for different echo train lengths (ETL). Longer ETL increases MTR due to shorter effective TR (P<0.001), limiting full T1 recovery. The corresponding T2 values, however, are not affected by the increased ETL.

MESE – multi-slice effect: MT bias when shifting to multi-slice scans is presented in Fig. 4 for the three models. MTR (a-c) correlated positively with the number of slices and with the MMP content, while the corresponding T2 values (d-f) were stable. Increasing the inter-slice gap resulted in reduced MTR which also does not translate into a bias in T2 values (not shown).

Discussion and Conclusions

The effect of MT on measured T2 depends on macromolecular content and on scan parameters. Our results show that MT leads to underestimation of MESE T2 values already in single-slice acquisitions, due to gradual and accumulated attenuation of signal along the echo-train. Shifting to multi-slice acquisitions causes further MT-related attenuation of the signal, without changing T2, as this attenuation occurs uniformly along the echo-train.

The broad MMP spectral-range of the brain tissues increased the measured MTR, while conclusive evidence regarding the effect on T2 cannot be drawn in the absence of MT-free baseline. Future work will investigate the effect of MT in multi-T2 models, and ways to negate or compensate these effects.

Acknowledgements

ISF 2009/17

References

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6. Majumdar S, Orphanoudakis SC, Gmitro A, O’Donnell M, Gore JC. Errors in the measurements of T2 using multiple-echo MRI techniques. II. Effects of static field inhomogeneity. Magn Reson Med. 1986;3(4):562-574.

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Figures

Figure 1: T2 weighted images (TE=50 ms) of the three models: (a) In vitro urea phantom, (b) ex vivo horse brain, and (c) in vivo human brain. Quantitative values were estimated for the ROIs, consisting of 150, 15, and 10 respectively. ROIs in the physiological samples contain homogeneous area as possible. ROIs 1-6 in: (a) correspond to urea concentrations of 0, 0.5, 1, 2, 3 and 4M; (b) correspond to the cortical GM, right CN, thalamus, genu of CC, midbrain, and right putamen; (c) correspond to the dura matter, WM fascicles, left CN, thalamus, left GP, and genu of CC.

Figure 2: Urea phantom results. (a) Relaxation parameters dependence on the urea concentration: top- T1 values calculated from IR protocol, bottom- T2 values of the SSE (blue) and MESE (black) protocols. While the SSE T2 is lower at zero concentration due to diffusion effects, MT dominates at higher concentrations, causing MESE values to be underestimated. (b) Water suppressed SVS for the 4M urea tube (tube #6 in Fig. 1a) showing a urea peak at 5.7 ppm and a residual water peak at 4.7 ppm.

Figure 3: MTR and T2 dependence on the ETL in a single-slice MESE scan of the urea phantom. (a) MTR between the 1st echo of SSE and MESE increases with longer ETLs (**P <0.0001). (b) T2 values, on the other hand, remain stable for increasing ETLs, indicating that uniform MT attenuation occurs across the entire MESE echo train. Error bars represent SD within the ROI marked in Fig. 1a.

Figure 4: Multi-slice MESE MTR (a-c) and T2 values (d-f) as a function of increasing number of slices. Values are presented for all models: (a,d) urea phantom; (b,e) horse brain; and (c,f) human brain. MTR was measured between single-slice acquisition and the middle slice in a multi-slice series. MTR exhibits positive correlation with the number of slices, and with the higher amount of macromolecules. No trend was seen in corresponding T2 values (d-f). We note the relatively low MTR baseline in the ex vivo scans (b) since 50% inter-slice gap was used vs. 0% in the other models.

Table 1: MRS and MRI scans parameters

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