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Multi-echo Chemical Shift Encoded methods for Fat-Water quantification aregrowing in clinical use due to their ability to estimate and correct someconfounding effects. State of the art CSE water-fat separation approaches relyon a multi-peak fat spectrum with peak frequencies and relative amplitudes keptconstant over the entire MRI dataset. However, the latter approximationintroduces a systematic error in fat percentage quantification in patientswhere the differences in lipid chemical composition are significant, such asfor neuromuscular disorders, because of the spatial dependence of the peakamplitudes. The present work aims to overcome this limitation by takingadvantage of an unsupervised clusterization-based approach offering a reliablecriterion to carry out a data-driven segmentation of the input MRI dataset intomultiple regions. The idea is to apply the clusterization for partitioning themulti-echo MRI dataset into a finite number of clusters whose internal voxelsexhibit similar distance metrics. For each cluster, the estimation of the fatspectral properties are evaluated with a self-calibration technique and finallythe fat-water percentages are computed via a non-linear fitting. The method istested in ad-hoc and public datasets. The overall performance and results interms of fitting accuracy, robustness and reproducibility are compared withother state-of-the-art CSE algorithms. This approach provides a more accurateand reproducible identification of chemical species, hence fat-waterseparation, when compared with other calibrated and non-calibrated approaches.

A data-Oriented based Self-Calibration And Robust chemical-shift  encoding by using clusterization (OSCAR) - Theory, Optimization and Clinical  Validation in Neuromuscular disorders