Effect of Multipeak Spectral Modeling of Fat for Liver Iron and Fat Quantification: Correlation of Biopsy with MR Imaging Results

Purpose: To investigate the effect of the multipeak spectral modeling of fat on R2* values as measures of liver iron and on the quantification of liver fat fraction, with biopsy as the reference standard. Materials and Methods: Institutional review board approval and informed consent were obtained. Patients with liver disease (n = 95; 50 men, 45 women; mean age, 57.2 years +/- 14.1 [standard deviation]) underwent a nontargeted liver biopsy, and 97 biopsy samples were reviewed for steatosis and iron grades. MR imaging at 1.5 T was performed 24-72 hours after biopsy by using a three-echo three-dimensional gradient-echo sequence for water and fat separation. Data were reconstructed off-line, correcting for T1 and T2* effects. Fat fraction and R2* maps (1/T2*) were reconstructed and differences in R2* and steatosis grades with and without multipeak modeling of fat were tested by using the Kruskal-Wallis test. Spearman rank correlation coefficient was used to assess fat fractions and steatosis grades. Linear regression analysis was performed to compare the fat fraction for both models. Results: Mean steatosis grade at biopsy ranged from 0% to 95%. Biopsy specimens in 26 of 97 patients (27%) showed liver iron (15 mild, six moderate, and five severe). In all 71 samples without iron, a strong increase in the apparent R2* was observed with increasing steatosis grade when single-peak modeling of fat was used (P = .001). When multipeak modeling was used, there were no differences in the apparent R2* as a function of steatosis grading (P = .645), and R2* values agreed closely with those reported in the literature. Good correlation between fat fraction and steatosis grade was observed (r(S) = 0.85) both without and with spectral modeling. Conclusion: In the presence of fat, multipeak spectral modeling of fat improves the agreement between R2* and liver iron. Single-peak modeling of fat leads to underestimation of liver fat. (C) RSNA, 2012