Abstract #0938

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

Nayereh Ghazi¹, Santiago Coelho², Noam Shemesh³, and Sune Nørhøj Jespersen^1,4

¹Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark, ²Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University, School of Medicine, New York, NY, United States, ³Champalimaud Research, Champalimaud Center for the Unknown, Lisbon, Portugal, ⁴Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

Synopsis

Keywords: Microstructure, Microstructure, Gray Matter, Exchange, Double Diffusion Encoding

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 mapping

Approach: 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.

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