A Systematic Evaluation of an Auto Regressive Moving Average (ARMA) Model for Fat-Water Quantification and Simultaneous T2* Mapping

Axel J. Krafft¹, Brian Allen Taylor¹, Hannah Lin¹, ², Ralf B. Loeffler¹, Claudia M. Hillenbrand¹

Iron overload assessment is one of the most prominent applications of multi-echo GRE-based quantitative T₂^* mapping. One of the major confounding factors arises in the presence of fat due to additional modulations of the mGRE signal. However, these modulations can be modeled and have led to dedicated techniques for fat-water quantification with T₂^* estimation. Here, we systematically analyze a recently proposed autoregressive moving average (ARMA) model for its ability to simultaneously quantify fat-water concentrations and the associated T₂^* times. The ARMA model is compared to conventional fitting approaches and evaluated in phantoms and volunteer data.