Multi-spectral fat-water models assume either single or independent R2* for water (R2*W) and fat (R2*F) for assessment of steatosis. However, any incorrect assumptions in the signal model will produce errors in R2* and fat fraction (FF) calculations. In this study, we developed a Monte Carlo–based approach for generating steatosis models by characterizing fat morphology from histology and synthesizing MRI signal to estimate independent R2*W and R2*F and compare with in-vivo studies. Our results show that R2*W and R2*F are slightly different at low FFs and both demonstrate a positive correlation with FF with slopes of R2*W-FF similar to in-vivo calibrations.
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