Phase‐Sensitive Modeling Improves Fat <scp>DESPOT</scp> Multiparametric Relaxation Mapping in Fat‐Water Mixtures

ABSTRACT Purpose To improve on a multiparametric technique—known as Fat DESPOT—to map the fat‐ and water‐specific (, ), , 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. Methods 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. Results 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, , and (maximum = 0.13%, 0.19 s −1 , and 0.009 s −1 , respectively). Conclusion With more precise and , 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.