Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise

Abstract

Quantification of hepatic steatosis is a significant unmet need for the diagnosis and treatment of patients with nonalcoholic fatty liver disease (NAFLD). MRI is capable of separating water and fat signals in order to quantify fatty infiltration of the liver (hepatic steatosis). Unfortunately, fat signal has confounding T(1) effects and the nonzero mean noise in low signal-to-noise ratio (SNR) magnitude images can lead to incorrect estimation of the true lipid percentage. In this study, the effects of bias from T(1) effects and image noise were investigated. An oil/water phantom with volume fat-fractions ranging linearly from 0% to 100% was designed and validated using a spoiled gradient echo (SPGR) sequence in combination with a chemical-shift based fat-water separation method known as iterative decomposition of water and fat with echo asymmetry and least squares estimation (IDEAL). We demonstrated two approaches to reduce the effects of T(1): small flip angle (flip angle) and dual flip angle methods. Both methods were shown to effectively minimize deviation of the measured fat-fraction from its true value. We also demonstrated two methods to reduce noise bias: magnitude discrimination and phase-constrained reconstruction. Both methods were shown to reduce this noise bias effectively from 15% to less than 1%.