Synopsis

In this study, we developed multi-parameter mapping including T1, R2*, and proton density fat fraction with a single breath-hold, to evaluate liver disease and liver function. Six-echo spoiled gradient echo sequence with dual flip angles was used to acquire a 12-set MRI volume dataset. To shorten the scan time, undersampling and multi-contrast compressed sensing reconstruction were used. Quantitative values were validated by performing phantom and volunteer studies.

INTRODUCTION

METHODS

Phantom and volunteer studies were performed using a 3-Tesla clinical MR system (Discovery MR750; GE Healthcare, Waukesha, WI) with a whole-body coil and 32-channel torso array. The hand-made phantoms consisted of 10 acrylic containers (inner diameter = 30 mm, outer diameter = 40 mm, height = 35 mm), each filled with agarose gel mixed with gadolinium and peanut oil, as shown in Fig. 1. One patient was recruited after approval from our institutional review board.

DFA multi-echo SPGR sequences (flip angle (FA) = 3° and 19°) were used for the acquisition. To achieve accurate T1 mapping, the set of FAs was optimized using a method proposed by Deoni et al⁶. The MR images were acquired in four-dimensional space ($x, y, z, t$), where $x$, $y$, $z$, and $t$ were the readout, phase encode, slice encode, and echo time (TE), respectively. The sequence parameters for the studies are shown in Table 1.

Multi-contrast CS reconstruction was implemented with the variable density Cartesian trajectory (reduction factor = 4.5). The MR images were obtained by solving the equation below:

$$X = {\rm arg} \min_{x} \parallel AX-y \parallel^2 + \lambda\parallel \mathcal{F}_{t}X \parallel_{TV},$$

where $X$ is the MR image to be reconstructed, $y$ is the acquired k-space dataset, $A$ is the observation matrix, $\parallel x \parallel_{TV}$ is the total variation (TV) operator, and $\mathcal{F}_{t}$ is the Fourier transform along the TE dimension, respectively. $A$ is expressed as $A= PFTS$, where $S$ is the sensitivity map of the receiver, $T$ is the spatial phase variation, $\mathcal{F}$ is the Fourier transform, and $P$ is the undersampling operator. The TV operator was defined as follows:

$$\parallel \mathcal{F}_{t} X \parallel_{TV} = \sum_i^N\sqrt{(\nabla_x(\mathcal{F}_{t}X))^2 + (\nabla_y(\mathcal{F}_{t}X))^2 + (\nabla_z(\mathcal{F}_{t}X))^2},$$

where $N$ is the number of the voxel and $\nabla_x$, $\nabla_y$, and $\nabla_z$ are the finite difference operators along $x$, $y$, and $z$. The equation was solved using the split Bregman method⁷.The PDFF and R2* mapping, and water–fat separation were performed using the iterative least-squares with multi-peak model proposed by Yu et al⁸. The T1 map was calculated using the separated water images and DESPOT-1⁶.For the comparison of the mapping results, PDFF, R2*, and water T1 were measured with the Dixon-based clinical sequence (IDEAL-IQ, GE Healthcare, Waukesha, WI) and multi-TR/TE single-voxel stimulated echo acquisition mode⁹ (STEAM; TR varied from 150 to 2000 ms, TE varied from 10 to 110 ms, FA= 90°, voxel size=(2 cm)³, 32 spectra).

RESULTS and DISCUSSION

Fig. 2 shows the PDFF and R2* maps of the phantoms measured using the IDEAL-IQ sequence and the proposed method (Fig. 2 a–d), and the T1 map using the proposed method (Fig. 2 e). The values of PDFF, R2*, and T1 agreed well among the 3 methods (Fig. 3 a–c).

Quantitative maps, and water images for the volunteer study are shown in Fig. 4. The quantitative values (PDFF = 11.9 ± 4.5 [%], R2* = 45.9 ± 10.3 [1/s], T1 = 1163 ± 212 [ms]) of PDFF, R2*, and T1 in the right lobe were similar to those measured with the IDEAL-IQ sequence (PDFF = 11.0 ± 3.6 [%], R2* = 43.5 ±8.7 [1/s]) and STEAM (PDFF = 10.3 [%], T1 =1170 [ms]). A limitation of this study was a potential bias in T1 due to the iron accumulation in the liver. It may be corrected using the R2* map in future studies³.

CONCLUSION

Acknowledgements

References

1. Katsube T, et al. Invest Radiol. 2011;46(4):277-283.

2. Haimerl M,et al. PLoS One. 2013;8(12):e85658.

3. Banerjee R, et al. J Hepatol. 2014;60(1):69-77.

4. Mozes FE, et al. J Magn Reson Imaging. 2016;44(1):105-111.

5. Hernando D, et al. Magn Reson Med. 2013;70(5):1319-1331.

6. Deoni SCL, et al. Magn Reson Med. 2003;49(3):515-526.

7. Goldstein T, et al. SIAM J Imaging Sci. 2009;2(2):323-343.

8. Yu H, et al. Magn Reson Med. 2008;60(5):1122-1134.

9. Hamilton G, et al. J Magn Reson Imaging. 2015;42(6):1538-1543.