Siracusano G, et al. - This study is performed to overcome theory, optimization and clinical validation limitation by taking advantage of an unsupervised clusterization-based approach offering a reliable criterion to complete an information-driven segmentation of the input MRI dataset into multiple regions. Results established that the introduced algorithm can recognize at least 4 different partitions from MRI dataset under which to perform independent self-calibration routines and was found robust in neuromuscular disorder (NMD) imaging studies (as assessed on a cohort of 24 subjects) against latest chemical Shift–Encoded (CSE) techniques with either calibrated or non-calibrated approaches. Especially, the PDFF of the thigh was more reproducible for the quantitative estimation of pathological muscular fat infiltrations, which may be promising to assess disease progression in clinical practice.

• Multi-echo Chemical Shift-Encoded (CSE) methods for Fat-Water quantification are growing in clinical use because of 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 presents a systematic error in fat percentage quantification in patients where the differences in lipid chemical composition are important (for example, for neuromuscular disorders) due to the spatial dependence of the peak amplitudes.