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Cited by 200Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsNMR spectroscopy and applications

Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding

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Lampinen, Björn, Szczepankiewicz, Filip, Mårtensson, Johan, van Westen, Danielle, Sundgren, Pia C., Nilsson, Markus·2017·NeuroImage
DOI 10.1016/j.neuroimage.2016.11.053
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Abstract

In diffusion MRI (dMRI), microscopic diffusion anisotropy can be obscured by orientation dispersion. Separation of these properties is of high importance, since it could allow dMRI to non-invasively probe elongated structures such as neurites (axons and dendrites). However, conventional dMRI, based on single diffusion encoding (SDE), entangles microscopic anisotropy and orientation dispersion with intra-voxel variance in isotropic diffusivity. SDE-based methods for estimating microscopic anisotropy, such as the neurite orientation dispersion and density imaging (NODDI) method, must thus rely on model assumptions to disentangle these features. An alternative approach is to directly quantify microscopic anisotropy by the use of variable shape of the b-tensor. Along those lines, we here present the 'constrained diffusional variance decomposition' (CODIVIDE) method, which jointly analyzes data acquired with diffusion encoding applied in a single direction at a time (linear tensor encoding, LTE) and in all directions (spherical tensor encoding, STE). We then contrast the two approaches by comparing neurite density estimated using NODDI with microscopic anisotropy estimated using CODIVIDE. Data were acquired in healthy volunteers and in glioma patients. NODDI and CODIVIDE differed the most in gray matter and in gliomas, where NODDI detected a neurite fraction higher than expected from the level of microscopic diffusion anisotropy found with CODIVIDE. The discrepancies could be explained by the NODDI tortuosity assumption, which enforces a connection between the neurite density and the mean diffusivity of tissue. Our results suggest that this assumption is invalid, which leads to a NODDI neurite density that is inconsistent between LTE and STE data. Using simulations, we demonstrate that the NODDI assumptions result in parameter bias that precludes the use of NODDI to map neurite density. With CODIVIDE, we found high levels of microscopic anisotropy in white matter, intermediate levels in structures such as the thalamus and the putamen, and low levels in the cortex and in gliomas. We conclude that accurate mapping of microscopic anisotropy requires data acquired with variable shape of the b-tensor.

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  • The fixed diffusivity assumption, but not the tortuosity model, is the primary cause of NODDI's incompatibility with b-tensor encoding.

    “A very recent report (Guerreri et al., 2020) suggested that the fixed diffusivity assumption, but not the tortuosity model, is the primary cause of NODDI's incompatibility with b-tensor encoding (Lampinen et al., 2017).”

    From NODDI in clinical research (2020) · cites this paper as Lampinen et al., 2017

    Section 2.3 describes the fixed neurite diffusivity and the tortuosity model as linked NODDI constraints, not competing explanations. Sections 4.3 and 5 attribute the LTE–STE inconsistency to invalid constraints connecting neurite density with tissue mean diffusivity. The paper therefore does not support excluding tortuosity as a cause. (LLM, conf=0.84)

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  • With NODDI, the T2-weighted hyperintense lesion of gliomas exhibits smaller NDI and greater f_fw compared with normal-appearing WM.

    “With NODDI, the T2-weighted hyperintense lesion of gliomas exhibit a smaller NDI and greater f fw compared with normal-appearing WM (Lampinen et al., 2017;Maximov et al., 2017;Wen et al., 2015).”

    From NODDI in clinical research (2020) · cites this paper as Lampinen et al., 2017

    The Fig. 5 caption reports generally higher free-water fractions and mostly lower anisotropic-water fractions in gliomas than in normal tissue. It also notes that NODDI’s f_IC parameter was elevated in parts of the tumors. The paper does not establish the claim specifically for T2-hyperintense lesions compared with normal-appearing white matter, and the unqualified lower-NDI statement omits those exceptions. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Calculated NODDI metrics are parametrically dependent on the Din value.

    “Since obtaining the ground truth of Din value is still challenging, the calculated NODDI metrics are parametrically dependent on this value (Jelescu et al. 2016; Lampinen et al. 2017).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Lampinen et al. 2017

  • The validity of NODDI's parameter constraints is a matter of debate, even in the normal brain, with several works pointing out incompatibility of NODDI with extended data acquisitions.

    “The validity of such constraints is a matter of debate, even in the normal brain, with several works (Lampinen et al., 2020(Lampinen et al., , 2017;;Novikov et al., 2018b) pointing out incompatibility of NODDI with extended data acquisitions.”

    From NODDI in clinical research (2020) · cites this paper as Lampinen et al., 2017

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Authors
Lampinen, B., Szczepankiewicz, F., Mårtensson, J., van Westen, D., Sundgren, P. C., & Nilsson, M.
Affiliations
Lund University (SE)
Corresponding author
Lampinen, Björn
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2017-02
Volume
147
Pages
517-531
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2016.11.053
OpenAlex
W2549744279
Cited by
200
References
7
Citations by year
2026: 12 · 2025: 7 · 2024: 8 · 2023: 10 · 2022: 22 · 2021: 31 · 2020: 29 · 2019: 28 · 2018: 34 · 2017: 18 · 2016: 1
FWCI
23.48
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
Funders
  • Swedish Cancer Society (CAN 2013/321)
  • Swedish Research Council (K2011-52X-21737-01-3)
  • Swedish Brain Foundation (FO2014-0133)
  • ALF (F2014/354)
  • Swedish Foundation for Strategic Research (AM13-0090)
  • CR Award (MN15)
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, neurite density, NODDI, orientation dispersion, parameter bias, glioma, white matter, gray matter, mean diffusivity
MeSH terms
Adult, Brain, Brain Neoplasms, Cerebral Cortex, Computer Simulation, Glioma, Humans, Models, Theoretical, Putamen, Thalamus, Neurites, Anisotropy, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, White Matter, Gray Matter

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