Synopsis

Keywords: Liver, Preclinical, MEX

Quantitative MRI can elucidate the complex microstructural changes in liver disease. The MEX method estimates macromolecular fraction, such as collagen, and can potentially aid in this task.

Rats and mice were given CCl₄ to induce liver fibrosis and canned using a 7T scanner with MEX sequence (selective suppression and magnetization exchange) and macromolecular fraction (F) and T₁ were extracted. Histology quantitative evaluation of collagen and inflammation scoring was conducted.

F-values of fibrotic rats and mice were significantly increased and correlated with quantitative collagen percentage. T₁ was significantly elevated with higher inflammation scores.

Introduction

Liver fibrosis is the result of excessive deposition of collagen, and other macromolecules in the extracellular matrix (1–3). It may be concomitant with inflammation and steatosis. Liver fibrosis is expected to become the leading indication for liver transplantation and liver-related mortality (4).
MRI scans are increasingly used for liver disease diagnosis. The challenge lies in the complexity of combination of fibrosis, steatosis and inflammation. Thus, different MRI contrasts and techniques are available, each with its drawbacks: T₂ and T₁ relaxation (5, 6), proton density fat fraction (7, 8), MR elastography (5, 9), and T_1⍴, diffusion weighted imaging (10) and Amide proton transfer contrast (11).
Magnetization EXchange (MEX) pulse sequence allows the acquisition of a signal generated from macromolecular components of the tissue with quantification analysis of the volumetric fraction of macromolecules (12, 13). The MEX signal is acquired using selective saturation pulses and spoiling gradients, followed by a recovery period. The magnetization detected is divided into two contributions: free water interaction (the fitted T₁ value), and the magnetization transferred from non-aqueous protons. MEX was previously used to quantify the myelin fraction in normal and demyelination pathology(12, 13).
The purpose of this study is to test the MEX sequence and its derived quantitative parameter of macromolecular fraction with association to collagen deposition.

Methods

Animal Model
Sixteen Sprague-Dawley rats and 13 C57BL⁄6 mice given carbon tetrachloride (CCl₄) twice-weekly. Rats were evaluated after 6 (n_treat=10, n_control=6) and 8 weeks (n_treat=6, n_control=3). Mice treatment (n=9) and control (n=4) groups were scanned after 8 weeks. Following the scans, histological assessment, automated spectral segmentation and inflammation scoring were conducted. See Ref (14).

MRI Scans
All scans were performed using a 7T Bruker BioSpec scanner (Bruker Biospin, Germany). MEX sequence is described in Fig.1, which was repeated with multiple delay times. The entire MEX block was performed before each signal acquisition, such that the rows of the k-space are uniformly weighted by t_LM. A reference spin-echo or gradient-echo scan was additionally performed.
Rats: SE, 9 delay-times (15-3000[ms]), 5 slices, TE/TR=7.2/1500[ms], 128x128 matrix, and FOV=57x55mm².
Mice: GE, 9 delay-times (15-2500[ms]), 2 slices, TE/TR=3.2/3400[ms], 64x64 matrix, and FOV=40x30mm².

MEX Analysis
Analysis is based on Eq.[1], with fast exchange assumption and normalization by the longest t_LM
$$\frac{S(t_{LM} )}{(S(t_{LM}=t_{LM}max))}=\frac{(F(1-exp⁡(t_{LM}⁄\tau_{exc} ) )+(1-F)(1-exp⁡(t_{LM}⁄T_1 ) ))}{(F(1-exp⁡((t_{LM} max)⁄\tau_{exc} ) )+(1-F)(1-exp⁡((t_{LM} max)⁄T_1 ) ) )}$$
F is the fraction semisolid protons, τ_exc is the exchange time, and T₁ is the longitudinal relaxation time. Fitting was conducted using nonlinear least-squares Trust-Region algorithm with R²>0.95 threshold. Group statistical analysis were based on ROI segmentation with R²>0.999 requirement. See Ref (14).
Welch's t-test was used for group analysis and paired t-test for time-points progression. Correlations were evaluated using Pearson’s correlation.

Results

The percentage fibrotic tissue was significantly different between CCl₄ rats and controls, 11.87%±0.03 and 1.972%±0.003, respectively (See Fig.2a-e). Mice results were 4.08%±0.02 fibrotic tissue for CCl₄ mice, and 0.573%±0.006 for controls, see Fig.2f-j.

Rats
Fig.3a,b are F maps of a control rat after 6 and 8 weeks with ROIs values of 0.083±0.005 and 0.084±0.005. Fig.3e,f are the F maps for a CCl₄ treated rat, with fitted values of 0.125±0.006 and 0.131±0.008. The ROI T₁ values in Fig.3c,d,g,h are 1474±63[ms], 1591±38[ms], 1844±12[ms] and 1833±14[ms], respectively.

Fig.4a,b displays the group analysis at the 6 weeks' time-point. Mean F value is significantly increased by 24.7% in the treated group (CCl₄: 0.099±0.016; control: 0.080±0.003). Fig.4c is the fitted F values comparison between the two time-points, were values increased by 7.9% for fibrosis, while controls show 1.8% non-significant increase (P=0.08). In Fig.4d, F values are correlated to collagen percentage (R=0.87). In Fig.4e, T₁ vs. inflammation show non-significant change between 0 and 1 scores (P=0.79), and significant 33.7% increase 1 to 2 (0: 1481±177[ms]; 1: 1332±224[ms]; 2: 2007±464[ms]).

Mice
Fig.5a-b,d-e are F and T₁ maps for representative animals, with ROI F value of 0.034±0.009 for control and 0.072±0.003 for the CCl₄. The group analysis appears in Fig. 5c,f. The F value increased significantly by 49.7% (CCl₄: 0.062±0.006, control: 0.042±0.006), while T₁ values show an insignificant rise.

Correlation of F to spectral analysis (not shown) yielded R=0.88. T₁ comparison to the inflammation (not shown) was not significant between 0 to 1 (P=0.77), while significantly increasing by 17.1% within the fibrosis group (0: 1436±54[ms], 1: 1366±99[ms], 2: 1648±45[ms]).
Full results in Ref (14).

Conclusions

The F value indicates the relative percentage of collagen in the liver. F is not sensitivite to inflammation, which is represented by the T₁, thus pose as a promising biomarker for fibrosis.
Following this study and the previous ones, the MEX sequence has proven its relevance to various tissues, making it a possible quantitative imaging method to many other pathologies.

Acknowledgements

This work is dedicated to the memory of our beloved colleague and NMR mentor, Dr. Uzi Eliav. Dr. Eliav's role over the years in developing multiple NMR methods, including the MEX method, and his role in this particular work were pivotal. His friendship and wisdom will be missed.

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