INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) represents a spectrum of disorders characterized by the accumulation of fat in liver, and NAFLD is the most common etiology of chronic liver disease (1-4). Chemical shift encoded MRI (CSE-MRI) can discriminate between fat and water spins based on their different resonance frequencies, and multi-echo CSE-MRI techniques has been validated with excellent correlation with proton magnetic resonance spectroscopy (1H-MRS) and histological methods (5-10). The purpose of the present study was to validate the quantification of liver fat content by CSE-MRI on a group of geese, using biochemical extracted Triglyceride as the reference.

METHODS

Twenty-two geese with a wide range of hepatic steatosis were collected. CSE-MRI examinations (mDixon quant) were performed on a 3T MRI unit (Ingenia 3.0T TX, Philips, Best, the Netherlands) for all geese, and then liver of each goose was removed and samples were taken from the left lobe, upper and lower half of the right lobe for biochemical measurement and histology (Figure. 1). Triglyceride content (g) of the dry samples were determined by Soxhlet extraction (11), and the triglyceride mass percent of the goose liver were then obtained using the triglyceride content (g) and the wet weight (g) of the sample. According to the percentage of cells affected by fat vacuoles, the histological results were interpreted as: grade 0 for less than 5%, grade 1 for 5–30%, grade 2 for 31–50%, grade 3 for 51–75%, and grade 4 for more than 75% (12). Fat percentages by proton density fat fraction by MRI (MRI-PDFF) were measured within the sample regions of biochemical measurement. The intra-observer agreement of MRI-PDFF measurements was assessed using two ROI measurements by the same radiologist with an interval of one month. The accuracy of MRI-PDFF measurement was assessed through Spearman correlation coefficient (r) and Passing and Bablok regression equation using biochemical measurement as the gold standard. To detect the variability of fat distribution, we compare the values of the three ROIs derived from the same goose liver, using mixed model repeated measurements analysis.

RESULTS

Gross visual evaluation demonstrated the wide range of hepatic steatosis of the geese livers, and these differences of hepatic steatosis were clearly observed by MRI-PDFF and histology (Figure. 2). The mean value (± standard deviation (SD), range) of biochemical triglyceride mass percentage and MRI-PDFF was 24.13% (± 21.1%, 0.04–58.7%) and 33.81% (± 27.53%, 0.88–79.41%), respectively. Bland-Altman analysis demonstrated a very high intra-observer agreement for MRI-PDFF ROI measurement, the intra-class correlation coefficient of was 0.998 (p ＜ 0.001) (Figure. 3). High correlation was detected between MRI-PDFF and triglyceride mass percentage (r = 0.949, p ＜ 0.001). Passing and Bablok regression indicated that triglyceride mass percentage can be predicted by MRI-PDFF (Figure. 4). The mean value of difference between MRI-PDFF and triglyceride mass percentage was 9.68% (95%CI: -5.28–24.63%, p ＜ 0.001). Figure 5 showed the distribution of triglyceride percentage at each division of histological grading. The biochemical result of each group defined by histology was 0.04-6.95% for grade 0, 2.69-7.09% for grade 1, 5.08-9.86% for grade 2, 11.39-25.85% for grade 3, and 10.2-60.54% for grade 4, respectively. No statistically significant differences of fat percentage among the three sampled regions were detected by MRI-PDFF (p = 0.995), biochemical extraction (p = 0.998), or histological grading (p = 0.416).

DISCUSSION

In previous studies, the correlation coefficient between MRI measured liver fat content and chemically estimated liver fat varied from 0.74 to 0.96 (13-17). Our study demonstrated comparable results regarding to the correlation coefficient between MRI and biochemical extraction (0.949 vs. 0.74–0.96). The MR mDixon-quant technique is complicated and the results can be influenced by many factors. First, MRI-PDFF measures the ratio of number of hydrogen protons in fat comparing to the number in both fat and water, while biochemical extraction measures the triglyceride content in the liver. Another issue is that mDixon MRI (using relatively longer TE than in solid state physics spectroscopy methods) cannot measure the signal from hydrogen protons that are closely connected to large proteins, or in a solid or semi-solid state. The “dry” mass of tissue could account for 3~15% of total mass. These factors generate a systematic shift in the final output, and may explain the slight bigger MRI-PDFF output than biochemical extraction observed in this study.

CONCLUSION

CSE-MRI methods can measure liver fat content accurately and reliably in comparison with chemical methods, and these results justify the use of CSE-MRI in the clinical setting to assess and monitor liver steatosis.

Acknowledgements

We thank our study participants for contributing their time and efforts. We wish to thankPhilips Healthcare for its technical supports.

References

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