Magnetic resonance in medicine · 2026
Phase-Sensitive Modeling Improves Fat DESPOT Multiparametric Relaxation Mapping in Fat-Water Mixtures.
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Bider Renée-Claude, Ciobanu Cristian, Campos Pazmiño Jorge, Fortier Véronique, McNabb Evan, Levesque Ives R
Аннотация
PURPOSE: To improve on a multiparametric technique-known as Fat DESPOT-to map the fat- and water-specific R 1 ( R 1 f , R 1 w ), 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.
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 DESPOTm) to a magnitude approach modeling the phases of fat and water separately (Fat DESPOTmϕ), and two models for complex data (Fat DESPOTc, Fat DESPOTcϕ). 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, R 1 f , and R 1 w (maximum σ = 0.13%, 0.19 s-1, and 0.009 s-1, respectively).
CONCLUSION: With more precise R 1 f and R 1 w , 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.
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 DESPOTm) to a magnitude approach modeling the phases of fat and water separately (Fat DESPOTmϕ), and two models for complex data (Fat DESPOTc, Fat DESPOTcϕ). 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, R 1 f , and R 1 w (maximum σ = 0.13%, 0.19 s-1, and 0.009 s-1, respectively).
CONCLUSION: With more precise R 1 f and R 1 w , 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.