Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI

Novikov, Dmitry S.; Veraart, Jelle; Jelescu, Ileana O.; Fieremans, Els

doi:10.1016/j.neuroimage.2018.03.006

Cited by 201 publications

(403 citation statements)

References 87 publications

(194 reference statements)

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“…The decision to fix the axial intra- and extra-axonal diffusivities equal to one another is another assumption that can lead to unpredictable effects. Recent work has shown that fixing D a,‖ = D e,‖ masks a fundamental property of multi-compartment models, namely the multiplicity of mathematical solutions (Jelescu et al, 2016a; Novikov et al, 2016b). Indeed, it has been shown that, if all parameters in the fitting procedure are released, namely f intra , D a,‖ , D e,‖ , D e,⊥ and κ (ignoring the CSF compartment), there are two distinct solutions to the parameter estimation problem, both within biologically plausible ranges (Figure 2).…”

Section: Modelsmentioning

confidence: 99%

“…Recently, (Novikov et al, 2016b) and (Reisert et al, 2017) have exploited this property to estimate the scalar parameters of a two-compartment kernel separately from the ODF:

𝒦 false(b, boldg \cdot boldn false) = f_{intra} e^{- {bD}_{normala, false‖} {false(boldg \cdot boldn false)}^{2}} + false(1 - f_{intra} false) e^{- {bD}_{normale, false‖} {false(boldg \cdot boldn false)}^{2} - {bD}_{normale, ⊥} false(1 - {false(boldg \cdot boldn false)}^{2} false)}

…”

Section: Modelsmentioning

confidence: 99%

“…The approach of (Novikov et al, 2016b) can be decomposed into two steps. The first step relies on solving an algebraic system of equations which relates the kernel parameters to the signal moments.…”

Section: Modelsmentioning

confidence: 99%

“…Spatial regularization of voxel parameters is also used. While the goodness of fit has been extensively used in the past to choose which model is most appropriate for describing the data (Ferizi et al, 2014; Panagiotaki et al, 2012), recent insights into the topology of the fitting landscape for a two-compartment model (Figure 2) advises against relying solely on such metrics for model selection (Jelescu et al, 2016a; Novikov et al, 2016b). …”

Section: Modelsmentioning

confidence: 99%

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Design and Validation of Diffusion MRI Models of White Matter

2017

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Diffusion MRI is arguably the method of choice for characterizing white matter microstructure in vivo. Over the typical duration of diffusion encoding, the displacement of water molecules is conveniently on a length scale similar to that of the underlying cellular structures. Moreover, water molecules in white matter are largely compartmentalized which enables biologically-inspired compartmental diffusion models to characterize and quantify the true biological microstructure. A plethora of white matter models have been proposed. However, overparameterization and mathematical fitting complications encourage the introduction of simplifying assumptions that vary between different approaches. These choices impact the quantitative estimation of model parameters with potential detriments to their biological accuracy and promised specificity. First, we review biophysical white matter models in use and recapitulate their underlying assumptions and realms of applicability. Second, we present up-to-date efforts to validate parameters estimated from biophysical models. Simulations and dedicated phantoms are useful in assessing the performance of models when the ground truth is known. However, the biggest challenge remains the validation of the “biological accuracy” of estimated parameters. Complementary techniques such as microscopy of fixed tissue specimens have facilitated direct comparisons of estimates of white matter fiber orientation and densities. However, validation of compartmental diffusivities remains challenging, and complementary MRI-based techniques such as alternative diffusion encodings, compartment-specific contrast agents and metabolites have been used to validate diffusion models. Finally, white matter injury and disease pose additional challenges to modeling, which are also discussed. This review aims to provide an overview of the current state of models and their validation and to stimulate further research in the field to solve the remaining open questions and converge towards consensus.

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Section: Modelsmentioning

confidence: 99%

“…Recently, (Novikov et al, 2016b) and (Reisert et al, 2017) have exploited this property to estimate the scalar parameters of a two-compartment kernel separately from the ODF:

𝒦 false(b, boldg \cdot boldn false) = f_{intra} e^{- {bD}_{normala, false‖} {false(boldg \cdot boldn false)}^{2}} + false(1 - f_{intra} false) e^{- {bD}_{normale, false‖} {false(boldg \cdot boldn false)}^{2} - {bD}_{normale, ⊥} false(1 - {false(boldg \cdot boldn false)}^{2} false)}

…”

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

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Design and Validation of Diffusion MRI Models of White Matter

2017

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“…Parameters are estimated by fitting dMRI data, often using nonlinear optimization methods. In WM, one such parameter, intrinsic intra‐axonal diffusivity ( D a ), has proven especially challenging to measure . This is because D a estimates are sensitive to modeling assumptions and tend to be only loosely constrained by the data, although some recently proposed approaches seem to be more promising …”

Section: Introductionmentioning

confidence: 99%

Triple diffusion encoding MRI predicts intra‐axonal and extra‐axonal diffusion tensors in white matter

Ramanna

Moss

McKinnon

et al. 2019

Magnetic Resonance in Med

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Purpose To demonstrate how triple diffusion encoding (TDE) MRI can be applied to separately estimate the intra‐axonal and extra‐axonal diffusion tensors in white matter (WM). Methods Using a TDE pulse sequence with an axially symmetric b‐matrix, diffusion MRI data were acquired at 3T for 3 healthy adults with an axial b‐value of 4000 s/mm2, a radial b‐value of 307 s/mm2, and 64 diffusion encoding directions. This acquisition was then repeated with the radial b‐value set to 0. A previously proposed theory was applied to these data in order to estimate the intra‐axonal diffusivity and axonal water fraction for each WM voxel. Conventional single diffusion encoding data were also obtained with b‐values of 1000 and 2000 s/mm2, which provided additional information sufficient for determining both the intra‐axonal and extra‐axonal diffusion tensors. Results From the TDE data, the average intra‐axonal diffusivity in WM was found to be 2.24 ± 0.18 µm2/ms, and the average axonal water fraction was found to be 0.60 ± 0.11. From the 2 diffusion tensors, average WM values were estimated for several compartment‐specific diffusion parameters. In particular, the extra‐axonal mean diffusivity was 1.09 ± 0.19 µm2/ms, the intra‐axonal fractional anisotropy was 0.50 ± 0.14, and the extra‐axonal fractional anisotropy was 0.23 ± 0.13. Conclusion By using a simple TDE pulse sequence with an axially symmetric b‐matrix, the diffusion tensors for the intra‐axonal and extra‐axonal spaces can be separately estimated in adult WM. This allows one to determine compartment‐specific diffusion properties for these 2 water pools.

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Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

Lampinen

Szczepankiewicz

Novén

et al. 2019

Human Brain Mapping

110

137

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In vivo mapping of the neurite density with diffusion MRI (dMRI) is a high but challenging aim. First, it is unknown whether all neurites exhibit completely anisotropic (“stick‐like”) diffusion. Second, the “density” of tissue components may be confounded by non‐diffusion properties such as T2 relaxation. Third, the domain of validity for the estimated parameters to serve as indices of neurite density is incompletely explored. We investigated these challenges by acquiring data with “b‐tensor encoding” and multiple echo times in brain regions with low orientation coherence and in white matter lesions. Results showed that microscopic anisotropy from b‐tensor data is associated with myelinated axons but not with dendrites. Furthermore, b‐tensor data together with data acquired for multiple echo times showed that unbiased density estimates in white matter lesions require data‐driven estimates of compartment‐specific T2 values. Finally, the “stick” fractions of different biophysical models could generally not serve as neurite density indices across the healthy brain and white matter lesions, where outcomes of comparisons depended on the choice of constraints. In particular, constraining compartment‐specific T2 values was ambiguous in the healthy brain and had a large impact on estimated values. In summary, estimating neurite density generally requires accounting for different diffusion and/or T2 properties between axons and dendrites. Constrained “index” parameters could be valid within limited domains that should be delineated by future studies.

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Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI

Cited by 201 publications

References 87 publications

Design and Validation of Diffusion MRI Models of White Matter

Design and Validation of Diffusion MRI Models of White Matter

Triple diffusion encoding MRI predicts intra‐axonal and extra‐axonal diffusion tensors in white matter

Searching for the neurite density with diffusion MRI: Challenges for biophysical modeling

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