Data-driven algorithm for myelin water imaging: Probing subvoxel compartmentation based on identification of...

Multicomponent analysis of MRI T₂ relaxation time (mcT₂ ) is commonly used for estimating myelin content by separating the signal at each voxel into its underlying distribution of T₂ values. This voxel-based approach is challenging due to the large ambiguity in the multi-T₂ space and the low SNR of MRI signals. Herein, we present a data-driven mcT₂ analysis, which utilizes the statistical strength of identifying spatially global mcT₂ motifs in white matter segments before deconvolving the local signal at each voxel.

Deconvolution is done using a tailored optimization scheme, which incorporates the global mcT₂ motifs without additional prior assumptions regarding the number of microscopic components. The end results of this process are voxel-wise myelin water fraction maps.

Validations are shown for computer-generated signals, uniquely designed subvoxel mcT₂ phantoms, and in vivo human brain. Results demonstrated excellent fitting accuracy, both for the numerical and the physical mcT₂ 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.

We conclude that studying global tissue motifs prior to performing voxel-wise mcT₂ analysis stabilizes the optimization scheme and efficiently overcomes the ambiguity in the T₂ space. This new approach can improve myelin water imaging and the investigation of microstructural compartmentation in general.