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Cited by 76Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Resolving degeneracy in diffusion MRI biophysical model parameter estimation using double diffusion encoding

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Coelho, Santiago, Pozo, Jose M., Jespersen, Sune N., Jones, Derek K., Frangi, Alejandro F.·2019·Magnetic Resonance in Medicine
DOI 10.1002/mrm.27714PMC PMC6593681
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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.

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  • The data may not uniquely support the biological interpretation one wishes to make, though subsequent theoretical work has characterized this degeneracy and shown that the model parameters can be distinguished with sufficiently informative acquisition protocols or additional measurements.

    “In other words, the data may not uniquely support the biological interpretation one wishes to make, though subsequent theoretical work has characterized this degeneracy and shown that the model parameters can be distinguished with sufficiently informative acquisition protocols or additional measurements (Coelho et al., 2019; Reisert et al., 2019).”

    From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Coelho et al., 2019

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Cited by

Sorted by citations

Authors
Coelho, S., Pozo, J. M., Jespersen, S. N., Jones, D. K., & Frangi, A. F.
Affiliations
  • University of Leeds (GB)
  • Aarhus University (DK)
  • Cardiff University (GB)
Corresponding author
Coelho, Santiago
ORCID
Venue
Magnetic Resonance in Medicine
Publisher
Wiley
Published
2019-03-13
Volume
82
Issue
1
Pages
395-410
ISSN
  • 0740-3194 (ISSN-L)
  • 1522-2594
DOI
10.1002/mrm.27714
PMC
PMC6593681
OpenAlex
W3105097233
Cited by
76
References
6
Citations by year
2026: 4 · 2025: 11 · 2024: 9 · 2023: 4 · 2022: 10 · 2021: 7 · 2020: 20 · 2019: 11
FWCI
7.93
Type
journal article
Language
en
Open access
Licence
Locations
  • Magnetic Resonance in Medicine (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
  • Lirias (KU Leuven) (acceptedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Research Bank (Australian Catholic University) (submittedVersion, cc-by)
  • Research Explorer (The University of Manchester) (submittedVersion, other-oa)
  • arXiv (Cornell University)
Funders
  • Wellcome Trust (104943/Z/14/Z, 096646/Z/11/Z)
  • European Commission (FP7 ICT, 601055, ICT-2011, FP7-ICT-2011-9, FP7‐ICT‐2011)
  • Engineering and Physical Sciences Research Council (EP/M006328/1, EP/M006328/2, EP/M00855X/1, EP/N026993/1, FP7-ICT-2011-9-601055, EP/M029778/1, 104943/Z/14/Z, 096646/Z/11/Z)
  • FP7 Information and Communication Technologies (FP7‐ICT‐2011‐9‐601055)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
Keywords
diffusion MRI, parameter estimation, degeneracy, double diffusion encoding, ill-posed problems, cumulant expansion, brain microstructure, estimation bias
MeSH terms
Algorithms, Computer Simulation, Image Processing, Computer-Assisted, Models, Neurological, Diffusion Magnetic Resonance Imaging

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