title: On the performance of T2* correction methods for quantification of hepatic fat content.
abstract: 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 T(2)* decay are addressed. Recently developed MRI methods that correct for T(2)* to improve the accuracy of fat quantification either assume a common T(2)* (single-T(2)*) for better stability and noise performance or independently estimate the T(2)* for water and fat (dual-T(2)*) for reduced bias, but with noise performance penalty. In this study, the tradeoff between bias and variance for different T(2)* 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 T(2)* 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-T(2)* correction, and ∼2π/3 for dual-T(2)* correction. Interestingly, when an echo spacing and first echo shift of ∼π/2 are used, methods without T(2)* correction have less than 5% bias in the estimates of fat fraction.
authors.affiliation: Department of Radiology, University of Wisconsin, Madison, Wisconsin 53792-3252, USA. sreeder@wisc.edu
authors.forename: Scott B
authors.initials: SB
authors.lastname: Reeder
authors.forename: Emily K
authors.initials: EK
authors.lastname: Bice
authors.forename: Huanzhou
authors.initials: H
authors.lastname: Yu
authors.forename: Diego
authors.initials: D
authors.lastname: Hernando
authors.forename: Angel R
authors.initials: AR
authors.lastname: Pineda
mesh_terms: D000465:Algorithms; D016477:Artifacts; D017352:Echo-Planar Imaging / Q000379:methods; D005234:Fatty Liver / Q000175:diagnosis*; D006486:Hemosiderosis / Q000175:diagnosis; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D050152:Intra-Abdominal Fat / Q000473:pathology*; D008099:Liver / Q000473:pathology; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008962:Models, Theoretical; D009010:Monte Carlo Method; D012680:Sensitivity and Specificity; D012985:Software Design