  Multi-echo Chemical Shift Encoded methods for Fat-Water quantification are
growing in clinical use due to their ability to estimate and correct some
confounding effects. State of the art CSE water-fat separation approaches rely
on a multi-peak fat spectrum with peak frequencies and relative amplitudes kept
constant over the entire MRI dataset. However, the latter approximation
introduces a systematic error in fat percentage quantification in patients
where the differences in lipid chemical composition are significant, such as
for neuromuscular disorders, because of the spatial dependence of the peak
amplitudes. The present work aims to overcome this limitation by taking
advantage of an unsupervised clusterization-based approach offering a reliable
criterion to carry out a data-driven segmentation of the input MRI dataset into
multiple regions. The idea is to apply the clusterization for partitioning the
multi-echo MRI dataset into a finite number of clusters whose internal voxels
exhibit similar distance metrics. For each cluster, the estimation of the fat
spectral properties are evaluated with a self-calibration technique and finally
the fat-water percentages are computed via a non-linear fitting. The method is
tested in ad-hoc and public datasets. The overall performance and results in
terms of fitting accuracy, robustness and reproducibility are compared with
other state-of-the-art CSE algorithms. This approach provides a more accurate
and reproducible identification of chemical species, hence fat-water
separation, when compared with other calibrated and non-calibrated approaches.
