Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of spatially global tissue features

Purpose Multicomponent analysis of MRI T-2 relaxation time (mcT(2)) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T-2 values. This voxel-based approach is challenging due to the large ambiguity in the multi-T-2 space and the low SNR of MRI signals. Herein, we present a data-driven mcT(2) analysis, which utilizes the statistical strength of identifying spatially global mcT(2) motifs in white matter segments before deconvolving the local signal at each voxel. Methods Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT(2) motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps. Results Validations are shown for computer-generated signals, uniquely designed subvoxel mcT(2) phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT(2) phantoms, exhibiting excellent agreement between calculated myelin water fraction and ground truth. Proof-of-concept in vivo validation is done by calculating myelin water fraction maps for white matter segments of the human brain. Interscan stability of myelin water fraction values was also estimated, showing good correlation between scans. Conclusion We conclude that studying global tissue motifs prior to performing voxel-wise mcT(2) analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T-2 space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.