Prediction of MRI R 2 * $$ {\mathrm{R}}_2^{\ast } $$ relaxometry in the presence of hepatic steatosis by Monte Carlo simulations.

To develop Monte Carlo simulations to predict the relationship of $R_2^{*}$ with liver fat content at 1.5 T and 3.0 T. For various fat fractions (FFs) from 1% to 25%, four types of virtual liver models were developed by incorporating the size and spatial distribution of fat droplets. Magnetic fields were then generated under different fat susceptibilities at 1.5 T and 3.0 T, and proton movement was simulated for phase accrual and MRI signal synthesis. The synthesized signal was fit to single-peak and multi-peak fat signal models for $R_2^{*}$ and proton density fat fraction (PDFF) predictions. In addition, the relationships between $R_2^{*}$ and PDFF predictions were compared with in vivo calibrations and Bland-Altman analysis was performed to quantitatively evaluate the effects of these components (type of virtual liver model, fat susceptibility, and fat signal model) on $R_2^{*}$ predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, and $R_2^{*}$ and PDFF values were predicted by Monte Carlo simulations at 1.5 T and 3.0 T. $R_2^{*}$ predictions were linearly correlated with PDFF, while the slope was unaffected by the type of virtual liver model and increased as fat susceptibility increased. Compared with in vivo calibrations, the multi-peak fat signal model showed superior performance to the single-peak fat signal model, which yielded an underestimation of liver fat. The $R_2^{*}$ -PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi-peak fat signal model were $R_2^{*}=0.490\times \text{PDFF}+28.0$ ( $R^2=0.967$ , ) at 1.5 T and $R_2^{*}=0.928\times \text{PDFF}+39.4$ ( $R^2=0.972$ , ) at 3.0 T. Monte Carlo simulations provide a new means for $R_2^{*}$ -PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. Accurate $R_2^{*}$ -PDFF calibration has the potential to correct the effect of fat on $R_2^{*}$ quantification, and may be helpful for accurate $R_2^{*}$ measurements in liver iron overload. In this study, a Monte Carlo simulation of hepatic steatosis was developed to predict the relationship between $R_2^{*}$ and PDFF. Furthermore, the effects of fat droplet morphology, fat susceptibility, fat signal model, and magnetic field strength were evaluated for the $R_2^{*}$ -PDFF calibration. Our results suggest that Monte Carlo simulations provide a new means for $R_2^{*}$ -PDFF prediction and this means can be easily generated for various regimes, such as simulations with higher fields and different echo times, as well as correction of magnetic susceptibility measurements for liver iron quantification.