Comparison of R2* correction methods for accurate fat quantification in fatty liver

Abstract

Purpose: To compare the performance of fat fraction quantification using single-R(2)* and dual-R(2)* correction methods in patients with fatty liver, using MR spectroscopy (MRS) as the reference standard.

Materials and methods: From a group of 97 patients, 32 patients with hepatic fat fraction greater than 5%, as measured by MRS, were identified. In these patients, chemical shift encoded fat-water imaging was performed, covering the entire liver in a single breathhold. Fat fraction was measured from the imaging data by postprocessing using 6 different models: single- and dual-R(2)* correction, each performed with complex fitting, magnitude fitting, and mixed magnitude/complex fitting to compare the effects of phase error correction. Fat fraction measurements were compared with co-registered spectroscopy measurements using linear regression.

Results: Linear regression demonstrated higher agreement with MRS using single-R(2)* correction compared with dual-R(2)* correction. Among single-R(2)* models, all 3 fittings methods performed similarly well (slope = 1.0 ± 0.06, r(2) = 0.89-0.91).

Conclusion: Single-R(2)* modeling is more accurate than dual-R(2)* modeling for hepatic fat quantification in patients, even in those with high hepatic fat concentrations.

Figure 1

Dual-$R_2^{*}$ modeling is associated with greater noise than single-$R_2^{*}$ modeling. Axial $R_2^{*}$ maps shows noticeably more noise using dual-$R_2^{*}$ modeling than single-$R_2^{*}$ modeling. Similarly, FF maps estimated using single-$R_2^{*}$ correction show markedly higher noise performance than those obtained using dual-$R_2^{*}$ correction.

Figure 2

Single-$R_2^{*}$ correction shows greater agreement with spectroscopy measurements than dual-$R_2^{*}$ correlation for quantification of fat fraction. FF estimated using three different single-$R_2^{*}$ (a.–c.) and dual-$R_2^{*}$ (d.–f.) models of complex, mixed, and magnitude fitting are plotted against FF co-localized and measured with spectroscopy. The dual-$R_2^{*}$ fits are very noisy at low FF: for example, the complex fit has some negative-valued FF estimates between 5–10%. Linear regression was performed, and the slope (m), intercept (b), and r² of the fit are shown. The slope and intercept are displayed as the average value plus/minus the standard deviation.

Figure 3

The $R_2^{*}$ of fat and water are very similar. This figure plots the $R_2^{*}$ of fat and water measured in the liver using MRS data fit to a two-peak Lorentzian model (water peak and methylene peak). The fat and water $R_2^{*}$ values are fitted linearly (a.), and also displayed as a histogram of the difference between the fat and water $R_2^{*}$ values (b.). The slope and intercept for the linear fit in (a.) are not significantly different from 1 and 0 respectively, as shown by their P-values (0.78 and 0.71 respectively), indicating that on average, the $R_2^{*}$ of water and fat are very similar in the liver.

Figure 4

Even the largest differences between $R_{2,F}^{*}$ and $R_{2,W}^{*}$ that were found in our data leads to a relatively small bias in fat fraction. The apparent single-$R_2^{*}$ values used at different fat fractions (a.), the estimated FF using these values (b.), and the resultant FF error (c.) are shown. The ‘worst-case’ (furthest from linear fit) point was used here to estimate error ($R_{2,W}^{*}=56{\mathrm{s}}^{-1}$ and $R_{2,F}^{*}=36{\mathrm{s}}^{-1}$).