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

TL;DR

This paper introduces OSCAR, a data-driven, clusterization-based self-calibration method for chemical shift encoding MRI, improving fat-water quantification accuracy especially in neuromuscular disorder patients by accounting for spatial lipid composition variations.

Contribution

OSCAR presents a novel unsupervised clustering approach for MRI data segmentation, enabling adaptive calibration of fat spectral properties for improved fat-water separation.

Findings

Enhanced accuracy in fat-water quantification compared to existing methods.

Robustness and reproducibility demonstrated across multiple datasets.

Better identification of chemical species in neuromuscular disorder imaging.

Abstract

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.