Phase-Sensitive Modeling Improves Fat DESPOT Multiparametric Relaxation Mapping in Fat-Water Mixtures

To improve on a multiparametric technique-known as Fat DESPOT-to map the fat- and water-specific $R_1$ ( $R_{1f}$ , $R_{1w}$ ), $R_2^{*}$ , and proton density fat fraction (PDFF) by upgrading the parameter initialization and introducing explicit model sensitivity to the complex phase of the water and fat signals.

We compared three-point Dixon and Graph Cut (GC) approaches to initial guesses for Fat DESPOT in experiments at 3 T in fat emulsion phantoms. Also in the phantom, we compared magnitude-based Fat DESPOT (Fat DESPOT_m) to a magnitude approach modeling the phases of fat and water separately (Fat DESPOT_mϕ), and two models for complex data (Fat DESPOT_c, Fat DESPOT_cϕ). The complex approaches, which performed best, were used in the lower leg and abdomen of healthy human participants.

In phantoms, Fat DESPOT using three-point Dixon and GC returned similar parameter estimates and precision, though the former deviated from the overall trend at 50% nominal fat fraction. The complex models showed the best agreement with reference PDFF (average error 1.2% ± 1.2%) and the lowest combined standard deviation across ROIs, for PDFF, $R_{1f}$ , and $R_{1w}$ (maximum $\sigma$  = 0.13%, 0.19 s^-1, and 0.009 s^-1, respectively).

With more precise $R_{1f}$ and $R_{1w}$ , more accurate PDFF, and more versatile echo time selection, this work demonstrates the advantages of Fat DESPOT multiparametric mapping with complex fitting and GC-based parameter initialization.