Comparative review of algorithms and methods for chemical-shift-encoded quantitative fat-water imaging
Pierre Daudé, Tangi Roussel, Thomas Troalen, Patrick Viout, Diego Hernando, Maxime Guye, Frank Kober, Sylviane Confort Gouny, Monique Bernard, Stanislas Rapacchi
Abstract
Purpose
To propose a standardized comparison between state-of-the-art open-source fat-water separation algorithms for proton density fat fraction (PDFF) and R2* quantification using an open-source multi-language toolbox.
Methods
Eight recent open-source fat-water separation algorithms were compared in silico, in vitro, and in vivo. Multi-echo data were synthesized with varying fat-fractions, B0 off-resonance, SNR and TEs. Experimental evaluation was conducted using calibrated fat-water phantoms acquired at 3T and multi-site open-source phantoms data. Algorithms’ performances were observed on challenging in vivo datasets at 3T. Finally, reconstruction algorithms were investigated with different fat spectra to evaluate the importance of the fat model.
Results
In silico and in vitro results proved most algorithms to be not sensitive to fat-water swaps and B0 offsets with five or more echoes. However, two methods remained inaccurate even with seven echoes and SNR = 50, and two other algorithms’ precision depended on the echo spacing scheme (p < 0.05). The remaining four algorithms provided reliable performances with limits of agreement under 2% for PDFF and 6 s−1 for R2*. The choice of fat spectrum model influenced quantification of PDFF mildly (<2% bias) and of R2* more severely, with errors up to 20 s−1.
Conclusion
In promoting standardized comparisons of MRI-based fat and iron quantification using chemical-shift encoded multi-echo methods, this benchmark work has revealed some discrepancies between recent approaches for PDFF and R2* mapping. Explicit choices and parameterization of the fat-water algorithm appear necessary for reproducibility. This open-source toolbox further enables the user to optimize acquisition parameters by predicting algorithms’ margins of errors.