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 …

Purpose Supervised machine learning (ML) provides a compelling alternative to traditional
model fitting for parameter mapping in quantitative MRI. The aim of this work is to …

Specific features of white matter microstructure can be investigated by using biophysical
models to interpret relaxation-diffusion MRI brain data. Although more intricate models have …

Abstract The Soma and Neurite Density Imaging (SANDI) three-compartment model was
recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite …

Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively.
Today, model-based techniques are widely available and used for white matter …

Diffusion magnetic resonance imaging is a non-invasive technique to probe the
microstructural features of tissue. Conventional diffusion encoding is unable to disentangle …

Abstract The Soma and Neurite Density Imaging (SANDI) three-compartment model was
recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite …

In in-vivo B-tensor encoding measurements, linear tensor encoding (LTE) and spherical
tensor encoding (STE) waveforms estimate the same mean diffusivity in brain white and grey …

This work demonstrates the feasibility of using deep learning (DL) to accelerate revised-
NODDI parameter estimation with data acquired using tensor-valued diffusion encoding …

Conventional wisdom suggests that it is necessary to average the diffusion signal over the
gradient directions to map microstructural features in the presence of orientational …