Coelho, S orcid.org/0000-0002-1039-4803, Pozo, JM, Jespersen, SN et al. (2 more authors) (2019) Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding. Magnetic Resonance in Medicine, 82 (1). pp. 395-410. ISSN 0740-3194
Abstract
Purpose:
Biophysical tissue models are increasingly used in the interpretation of diffusion MRI (dMRI) data, with the potential to provide specific biomarkers of brain microstructural changes. However, it has been shown recently that, in the general Standard Model, parameter estimation from dMRI data is ill‐conditioned even when very high b‐values are applied. We analyze this issue for the Neurite Orientation Dispersion and Density Imaging with Diffusivity Assessment (NODDIDA) model and demonstrate that its extension from single diffusion encoding (SDE) to double diffusion encoding (DDE) resolves the ill‐posedness for intermediate diffusion weightings, producing an increase in accuracy and precision of the parameter estimation.
Methods:
We analyze theoretically the cumulant expansion up to fourth order in b of SDE and DDE signals. Additionally, we perform in silico experiments to compare SDE and DDE capabilities under similar noise conditions.
Results:
We prove analytically that DDE provides invariant information non‐accessible from SDE, which makes the NODDIDA parameter estimation injective. The in silico experiments show that DDE reduces the bias and mean square error of the estimation along the whole feasible region of 5D model parameter space.
Conclusions:
DDE adds additional information for estimating the model parameters, unexplored by SDE. We show, as an example, that this is sufficient to solve the previously reported degeneracies in the NODDIDA model parameter estimation.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2019 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | biophysical tissue models; diffusion MRI; double diffusion encoding; microstructure imaging; parameter estimation; single diffusion encoding; white matter |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/N026993/1 EPSRC EP/N026993/1 EPSRC EP/M006328/2 |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Feb 2019 11:57 |
Last Modified: | 30 May 2023 22:23 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/mrm.27714 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141141 |
Download
Filename: FINAL VOR Coelho_et_al-2019-Magnetic_Resonance_in_Medicine.pdf
Licence: CC-BY 4.0