(1) Background: Open-source software tools are available to estimate proton density
fat fraction (PDFF). (2) Methods: We compared four algorithms: complex-based with
graph cut (GC), magnitude-based (MAG), magnitude-only estimation with Rician noise
modeling (MAG-R), and multi-scale quadratic pseudo-Boolean optimization with graph
cut (QPBO). The accuracy and reliability of the methods were evaluated in phantoms
with known fat/water ratios and a patient cohort with various grades (S0-S3) of steatosis.
Image acquisitions were performed at 1.5 Tesla (T). (3) Results: The PDFF estimates
showed a nearly perfect correlation (Pearson r = 0.999, p < 0.001) and inter-rater
agreement (ICC = from 0.995 to 0.999, p < 0.001) with true fat fractions. The absolute
bias was low with all methods (0.001-1%), and an ANCOVA detected no significant difference
between the algorithms in vitro. The agreement across the methods was very good in
the patient cohort (ICC = 0.891, p < 0.001). However, MAG estimates (-2.30% +/- 6.11%,
p = 0.005) were lower than MAG-R. The field inhomogeneity artifacts were most frequent
in MAG-R (70%) and GC (39%) and absent in QPBO images. (4) Conclusions: The tested
algorithms all accurately estimate PDFF in vitro. Meanwhile, QPBO is the least affected
by field inhomogeneity artifacts in vivo.
