44 citations · 83 across the 9 of their papers we have counts for
9 papers
Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion MRI
Ting Gong, Francesco Grussu, Claudia A. M. Gandini Wheeler-Kingshott +2
Purpose: We address the challenge of inaccurate parameter estimation in diffusion MRI when the signal-to-noise ratio (SNR) is very low, as in the spinal cord. The accuracy of conve…
MAGORINO: Magnitude-only fat fraction and R2* estimation with Rician noise modelling
Timothy JP Bray, Alan Bainbridge, Margaret A Hall-Craggs +1
Purpose: Magnitude-based fitting of chemical shift-encoded data enables proton density fat fraction (PDFF) and R2* estimation where complex-based methods fail or when phase data is…
Task-driven assessment of experimental designs in diffusion MRI: a computational framework
Sean C. Epstein, Timothy J. P. Bray, Margaret A. Hall-Craggs +1
This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly meas…
Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution
Ross Callaghan, Daniel C. Alexander, Marco Palombo +1
Axons in white matter have been shown to have varying geometries within a bundle using ex vivo imaging techniques, but what does this mean for diffusion MRI (dMRI) based spherical…
Mapping complex cell morphology in the grey matter with double diffusion encoding MR: a simulation study
Andrada Ianus, Daniel C. Alexander, Hui Zhang +1
This paper investigates the impact of cell body (soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard s…
ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation
Ross Callaghan, Daniel C. Alexander, Marco Palombo +1
This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, fo…
Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination
Ioana Hill, Marco Palombo, Mathieu Santin +14
The intra-axonal water exchange time τi, a parameter associated with axonal permeability, could be an important biomarker for understanding demyelinating pathologies such as Multip…
SANDI: a compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI
Marco Palombo, Andrada Ianus, Daniel Nunes +4
This work introduces a compartment-based model for apparent soma and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in…
A generative model of realistic brain cells with application to numerical simulation of diffusion-weighted MR signal
Marco Palombo, Daniel C. Alexander, Hui Zhang
In this work, we introduce a novel computational framework that we developed to use numerical simulations to investigate the complexity of brain tissue at a microscopic level with…