Prewhitening and Maximum Likelihood Estimation for Root-Sum-Of-Squares Images

Ryan Fobel¹, Greg J. Stanisz¹

Magnitude bias presents a serious challenge for quantitative imaging techniques that require data points close to the noise floor (e.g. DTI, qT2, MT, etc.). This bias becomes increasingly worse with the number of coils in a sum-of-squares reconstruction. This work presents a fitting strategy based on Maximum Likelihood theory to significantly reduce this bias. It relies on prior knowledge of the noise distribution from a reference scan and can account for correlation between coils with a prewhitening approach. Results from Monte Carlo simulations and a T2-decay experiment are presented.