Tensor encoded diffusion weighting improves model parameter estimation of SMEX/NEXI

Motivation: SMEX is a model of diffusion which may enable gray matter (GM) microstructural mapping in-vivo. However, clinical translation necessitates reliable parameter estimation with short scan time and limited gradient strength. Goal(s): Resolve degeneracies for SMEX parameter estimation and enable more accurate and precise GM microstructural mappingApproach: We complement the Single Diffusion Encoding measurement with Double Diffusion Encoding. We analyze the signal theoretically for low b-values in terms of the cumulant expansion, and for high b-values with numerical simulations. Results: Planar diffusion encoding resolves an intrinsic degeneracy in SMEX at low b, and generally provides higher accuracy and precision in model parameter estimation. Impact: The improvement in parameter estimation afforded by tensor encoded diffusion may enable shorter sequences and lower gradient strengths, thereby facilitating clinical translation of SMEX.