Validation of a data-driven multicomponent T2 analysis for quantifying myelin content in the cuprizone mouse model of multiple sclerosis

Background: Myelin quantification is essential for understanding a wide range of neurodegenerative pathologies. Voxel-wise multicomponent T2 (mcT2) analysis is the common approach for this purpose, yet no gold standard technique exist that can overcome the ambiguity of fitting several T2 components to a single-voxel signal. This challenge is further exacerbated in preclinical scan settings due to the addition of spurious diffusion encoding, resulting from the use of imaging gradients that are at least an order of magnitude larger than on typical clinical scanners. Purpose: Assess the utility of a new data-driven approach for mcT2 analysis, which utilizes information from the entire tissue to analyze the signal from each voxel in healthy and demyelinated tissues. Specifically, this algorithm uses statistical analysis of the entire anatomy to identify tissue-specific multi-T2 signal combinations, and then uses these as basis-functions for voxel-wise mcT2 fitting. Methods: Data-driven mcT2 analysis was performed on N = 7 cuprizone mice and N = 7 healthy mice. Myelin water fraction (MWF) values at six brain regions were evaluated. Correlation with reference immunohistochemical (IHC) staining for myelin basic protein was done in the corpus callosum. To demonstrate the added value of the data-driven approach the analysis was performed twice – with and without the data-driven preprocessing step. Results: Strong agreement was obtained between data-driven MWF values and histology. Applying the data-driven analysis prior to the voxel-wise fitting improved the mapping accuracy vs. non data-driven analysis, producing statistically significant separation between the two mice groups, good groupwise linear correlation with histology (cuprizone: R² = 0.64, p