Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

Pizzolato, Marco; Andersson, Mariam; Canales-Rodríguez, Erick Jorge; Thiran, Jean‐Philippe; Dyrby, Tim B.

doi:10.1016/j.mri.2021.11.012

Cited by 5 publications

(18 citation statements)

References 46 publications

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“…However, susceptibility effects can lead to directional differences of the measured transverse relaxation time along the bundle trajectory depending on the local angle between the bundle and the direction of the main field in the MRI scanner (Sati, Silva, van Gelderen, Gaitan, Wohler, Jacobson, Duyn and Reich, 2012). This was confirmed for the axonal compartment by McKinnon and Jensen (2019) and Pizzolato et al (2022a) although attributed to the extra-axonal space by Tax, Kleban, Chamberland, Baraković, Rudrapatna and Jones (2021). The presence of these susceptibility dependencies may lead to a scenario where, within a voxel, eventual differences between two or more bundles' transverse relaxation times might be confounded by the additional susceptibility effects or, conversely, perfectly equal bundles can appear to have slightly different 2 .…”

Section: Orientational Invariancementioning

confidence: 88%

“…The proposed method theoretically enables estimating axonal diffusivities that are free from partial volume bias such as that due to the coexistence of gray and white matter tissue within a voxel. This is enabled by the fact that zonal harmonic ratios with > 0 are insensitive to the spherical mean of the signal which includes contributions from not only from axons but also from other isotropic compartments (Zucchelli et al, 2017;Pizzolato et al, 2022a). In fact, as illustrated in fig.…”

Section: Discussionmentioning

confidence: 99%

“…5 indicate that, although to a lesser extent, also the estimates obtained including the spherical mean of the signal suffer from this bias. We point to the synthetic data analysis in Pizzolato et al (2022a) for a more specific analysis of the error where the effects of the number of fibers within a voxels is also analyzed, although in a different context. It is possible to conclude that the results of the estimates we report are more reliable in areas of the white matter where the voxel contains only one fiber direction and that display a low amount of axonal dispersion, e.g., in the corpus callosum.…”

Section: Discussionmentioning

confidence: 99%

“…While it is safe to assume that such a scenario is unrealistic in WM, where some other spherical harmonics coefficients will be non-zero, the effect of noise will cause more error whenever the values of these coefficients is low. To assess this, we computed the spherical variance of the FOD used to simulate the data in all WM voxels, similarly to what done in Pizzolato et al (2022a). This metric is defined as the sum of the squares of the spherical harmonics coefficients with zonal order > 0 (Zucchelli, Deslauriers-Gauthier and Deriche, 2020).…”

Section: Bias Due To Axonal Dispersion and Multiple Bundlesmentioning

confidence: 99%

“…We demonstrate this on the Human Connectome MGH Adult Diffusion dataset. In particular, we introduce a method that is general and can, in theory, also account for the presence of bias due to residual partial volume with gray matter or with isotropically restricted compartments (Pizzolato, Andersson, Canales-Rodríguez, Thiran and Dyrby, 2022a) such as those arising from cell-nuclei, vacuoles and possibly other types of structures as presented by Andersson, Kjer, Rafael-Patino, Pacureanu, Pakkenberg, Thiran, Ptito, Bech, Dahl, Dahl et al (2020). Additionally, the use of single diffusion encoding PGSE offers a higher efficiency in achieving high b-values while minimizing the echo time.…”

Section: Introductionmentioning

confidence: 99%

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Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Pizzolato¹,

Canales‐Rodríguez²,

Andersson³

et al. 2022

Preprint

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We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown orientation distribution of axons. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated. After revising existing theory, we introduce a new general method for the estimation of both axonal diffusivities at strong diffusion weightings based on zonal harmonics modeling. We additionally show how this could lead to estimates that are free from partial volume bias with, for instance, gray matter. We test the method on publicly available data from the MGH Adult Diffusion Human Connectome project dataset. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii. We address the estimation problem also from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

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Section: Orientational Invariancementioning

confidence: 88%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Bias Due To Axonal Dispersion and Multiple Bundlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Pizzolato¹,

Canales‐Rodríguez²,

Andersson³

et al. 2022

Preprint

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Pore size estimation in axon‐mimicking microfibers with diffusion‐relaxation MRI

Canales‐Rodríguez,

Pizzolato,

Zhou

et al. 2024

Magnetic Resonance in Med

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PurposeThis study aims to evaluate two distinct approaches for fiber radius estimation using diffusion‐relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power‐law approach and a T2‐based pore size estimation technique.Theory and MethodsA general diffusion‐relaxation theoretical model for the spherical mean signal from water molecules within a distribution of cylinders with varying radii was introduced, encompassing the evaluated models as particular cases. Additionally, a new numerical approach was presented for estimating effective radii (i.e., MRI‐visible mean radii) from the ground truth radii distributions, not reliant on previous theoretical approximations and adaptable to various acquisition sequences. The ground truth radii were obtained from scanning electron microscope images.ResultsBoth methods show a linear relationship between effective radii estimated from MRI data and ground‐truth radii distributions, although some discrepancies were observed. The spherical mean power‐law method overestimated fiber radii. Conversely, the T2‐based method exhibited higher sensitivity to smaller fiber radii, but faced limitations in accurately estimating the radius in one particular phantom, possibly because of material‐specific relaxation changes.ConclusionThe study demonstrates the feasibility of both techniques to predict pore sizes of hollow microfibers. The T2‐based technique, unlike the spherical mean power‐law method, does not demand ultra‐high diffusion gradients, but requires calibration with known radius distributions. This research contributes to the ongoing development and evaluation of neuroimaging techniques for fiber radius estimation, highlights the advantages and limitations of both methods, and provides datasets for reproducible research.

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Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture

Wu,

Liu,

Zhang

et al. 2023

Lecture Notes in Computer Science

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Axonal T2 estimation using the spherical variance of the strongly diffusion-weighted MRI signal

Cited by 5 publications

References 46 publications

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Axial and radial axonal diffusivities from single encoding strongly diffusion-weighted MRI

Pore size estimation in axon‐mimicking microfibers with diffusion‐relaxation MRI

Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture

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