Soma and Neurite Density Imaging (SANDI) was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges were identified; First, the Rician noise floor biases estimation of soma properties. Second there is an empirical lower bound on the spherical signal fraction and pore-size. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, estimation of spherical pore-size is challenging.
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