MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times

Gong, Ting; Tong, Qiqi; He, Hongjian; Sun, Yi; Zhong, Jianhui; Zhang, Hui

doi:10.1016/j.neuroimage.2020.116906

Cited by 52 publications

(83 citation statements)

References 57 publications

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“…However, the validity of this tortuosity constraint in densely packed axons has been questioned ( Jelescu and Budde, 2017 ). Common to all models is the fact that they are measuring signal fractions, which are not corrected for potentially different T2 relaxation times, e.g., in the intra- and extra-axonal water ( Veraart et al, 2018 ; Lampinen et al, 2019 ; McKinnon and Jensen, 2019 ; Gong et al, 2020 ). If the tortuosity constraint were indeed valid, it should relate extra-axonal diffusivities to the extra-cellular space (

1

rather than signal fraction of the hindered compartment,

( Jelescu et al, 2015 ).…”

Section: Challenges For Aggregated G-ratio Mappingmentioning

confidence: 99%

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

Mohammadi

Callaghan

2021

Journal of Neuroscience Methods

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1

rather than signal fraction of the hindered compartment,

( Jelescu et al, 2015 ).…”

Section: Challenges For Aggregated G-ratio Mappingmentioning

confidence: 99%

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

Mohammadi

Callaghan

2021

Journal of Neuroscience Methods

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“…Using multiple MRI modalities one can obtain an estimate of the average voxel-wise g -ratio in a voxel in- vivo by combining measurements of myelin and axonal volume fractions 10-13 . The axonal volume fraction can be estimated from diffusion MRI, using a multi-compartment fit to the diffusion-weighted signal 14-18 . A wide variety of different MRI modalities have been proposed to estimate the myelin volume fraction 19,20 .…”

Section: Introductionmentioning

confidence: 99%

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

Cottaar

Tendler

et al. 2020

Preprint

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PurposeMyelin has long been the target of neuroimaging research due to its importance in brain development, plasticity, and disease. However, most available techniques can only provide a voxel-averaged estimate of myelin content. In the human brain, white matter fibre pathways connecting different brain areas and carrying different functions often cross each other in the same voxel. A measure that can differentiate the degree of myelination of crossing fibres would provide a more specific marker of myelination.Theory & MethodsOne MRI signal property sensitive to myelin is the phase accumulation, which to date has also been limited to voxel-averaged myelin estimates. We use this sensitivity by measuring the phase accumulation of the signal remaining after diffusion weighting, which we call DIffusion-Prepared Phase Imaging (DIPPI). Including diffusion weighting before estimating the phase accumulation has two distinct advantages for estimating the degree of myelination: (1) it increases the relative contribution of intra-axonal water, whose phase is related linearly to the amount of myelin surrounding the axon (in particular the log g-ratio) and (2) it gives directional information, which can be used to distinguish between crossing fibres.ResultsUsing simulations and phantom data we argue that other sources of phase accumulation (i.e., movement-induced phase shift during the diffusion gradients, eddy currents, and other sources of susceptibility) can be either corrected for or are sufficiently small to still allow the g-ratio to be reliably estimated.ConclusionsThis new sequence is capable of providing a g-ratio estimate per fibre population crossing within a voxel.

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“…The TE-dependent Diffusion Imaging (TEdDI) technique proposed by Veraart et al (2018) technique considers a rewriting of the MC equation that directly includes the contribution of the T 2 time of the tissue modelled by the compartment and the TE of the acquisition into the volume fraction of each compartment. The same principles are followed in the more recent works of Lampinen et al (2019, 2020) and of Gong et al (2020). For the sake of coherence, we adapted the original notation used in the articles.…”

Section: Theorymentioning

confidence: 99%

“…Given the known interdependence between the T 2 times of tissues and the TE of the acquisition, some attempts at addressing this issue have been formulated making use of multi-TE multi-shell dMRI acquisitions (Veraart et al, 2018; Lampinen et al, 2020; Gong et al, 2020). Despite allowing to increase the signal-to-noise ratio (SNR) (Eichner et al, 2020), these techniques require a complete re-design of the experiments from acquisition to post-processing, posing severe limitations in terms of usability of already acquired data.…”

Section: Introductionmentioning

confidence: 99%

Multi-Tissue Multi-Compartment models of diffusion MRI

Frigo¹,

Fick²,

Zucchelli³

et al. 2021

Preprint

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State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classical multi-compartment models that relies on more common single-TE acquisitions and can be employed in the analysis of previously acquired datasets. We show how modelling multiple tissues provides a new interpretation of the concepts of signal fraction and volume fraction in the context of multi-compartment modelling. The software that allows to inspect single-TE diffusion MRI data with multi-tissue multi-compartment models is included in the publicly available Dmipy Python package.

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MTE-NODDI: Multi-TE NODDI for disentangling non-T2-weighted signal fractions from compartment-specific T2 relaxation times

Cited by 52 publications

References 57 publications

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

Towards in vivo g-ratio mapping using MRI: Unifying myelin and diffusion imaging

DIffusion-Prepared Phase Imaging (DIPPI): quantifying myelin in crossing fibres

Multi-Tissue Multi-Compartment models of diffusion MRI

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