• noise analysis;
  • chemical-shift imaging;
  • T2* correction;
  • hepatic steatosis;
  • Cramér-Rao bound analysis for biased estimators


Nonalcoholic fatty liver disease is the most prevalent chronic liver disease in Western societies. MRI can quantify liver fat, the hallmark feature of nonalcoholic fatty liver disease, so long as multiple confounding factors including T2* decay are addressed. Recently developed MRI methods that correct for T2* to improve the accuracy of fat quantification either assume a common T2* (single-T2*) for better stability and noise performance or independently estimate the T2* for water and fat (dual-T2*) for reduced bias, but with noise performance penalty. In this study, the tradeoff between bias and variance for different T2* correction methods is analyzed using the Cramér-Rao bound analysis for biased estimators and is validated using Monte Carlo experiments. A noise performance metric for estimation of fat fraction is proposed. Cramér-Rao bound analysis for biased estimators was used to compute the metric at different echo combinations. Optimization was performed for six echoes and typical T2* values. This analysis showed that all methods have better noise performance with very short first echo times and echo spacing of ∼π/2 for single-T2* correction, and ∼2π/3 for dual-T2* correction. Interestingly, when an echo spacing and first echo shift of ∼π/2 are used, methods without T2* correction have less than 5% bias in the estimates of fat fraction. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.