SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI
Year: 2020
Type: article
Abstract: This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging through DW-MRI presents water diffusion in white (WM) and gray (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b ≤ 3,000 s/mm2 (or 3 ms/μm2), it has been also shown to fail in GM at high b values (b≫3,000 s/mm2 or 3 ms/μm2). Here we hypothesise that the unmodelled soma compartment (i.e. cell body of any brain cell type: from neuroglia to neurons) may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma of any brain cell type is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40,000 s/mm2, or 40 ms/μm2) and human (bmax = 10,000 s/mm2, or 10 ms/μm2) brain. We show that SANDI defines new contrasts representing complementary information on the brain cyto- and myelo-architecture. Indeed, we show maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.
Source: NeuroImage
Institution: University College London
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,000
Awards: G1433481316, Anatomy-Driven Brain Connectivity Mapping, DTP - University College London, G2272555850, G2810537366, A biophysical simulation framework for magnetic resonance microstructure imaging, G3018663809, Medical image computing for next-generation healthcare technology, G4266322423, G434337789, G4606663280, G4714631627, G5264968607, G591385842, G6353617916, G7974379690, G8057189669, G8248977208, G8390508323, G8427053510, G844244329, G8662128564
OpenAlex ID: W3015618497