The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

Tax, Chantal M. W.; Szczepankiewicz, Filip; Nilsson, Markus; Jones, Derek K.

doi:10.1016/j.neuroimage.2020.116534

Cited by 75 publications

(70 citation statements)

References 82 publications

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“…Spectral component 1 represents a microscopically isotropic population with very low mobility that was observed in all regions of the spinal cord, with increased intensities in WM (average signal fractions across both spinal cord specimens of 0.35 ± 0.10 and 0.15 ± 0.09 in WM and GM, respectively). A subpopulation of water molecules with very slow and isotropic diffusion in the brain was observed and explicitly modeled in previous studies (Alexander et al, 2010;Dhital et al, 2018;Palombo et al, 2019;Tax et al, 2020). Generally, these studies reported lower fractions of low mobility water in WM than the values we found here, however, they were all performed in vivo and with clinical MRI scanners using considerably lower b-values and low spatial resolution that is prone to partial volume effects.…”

Section: Discussionsupporting

confidence: 60%

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

Benjamini

Hutchinson

Komlosh

et al. 2020

Preprint

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We describe a practical two-dimensional (2D) diffusion MRI framework to deliver specificity and improve sensitivity to axonal injury in the spinal cord. This approach provides intravoxel distributions of correlations of water mobilities in orthogonal directions, revealing sub-voxel diffusion components. Here we use it to investigate water diffusivities along axial and radial orientations within spinal cord specimens with confirmed, tract-specific axonal injury. First, we show using transmission electron microscopy and immunohistochemistry that tract-specific axonal beading occurs following Wallerian degeneration in the cortico-spinal tract as direct sequelae to closed head injury. We demonstrate that although some voxel-averaged diffusion tensor imaging (DTI) metrics are sensitive to this axonal injury, they are non-specific, i.e., they do not reveal an underlying biophysical mechanism of injury. Then we employ 2D diffusion correlation imaging (DCI) to improve discrimination of different water microenvironments by measuring and mapping the joint water mobility distributions perpendicular and parallel to the spinal cord axis. We determine six distinct diffusion spectral components that differ according to their microscopic anisotropy and mobility. We show that at the injury site a highly anisotropic diffusion component completely disappears and instead becomes more isotropic. Based on these findings, an injury-specific MR image of the spinal cord was generated, and a radiological-pathological correlation with histological silver staining % area was performed. The resulting strong and significant correlation (r = 0.70, p < 0.0001) indicates the high specificity with which DCI detects injury-induced tissue alterations. We predict that the ability to selectively image microstructural changes following axonal injury in the spinal cord can be useful in clinical and research applications by enabling specific detection and increased sensitivity to injury-induced microstructural alterations. These results also encourage us to translate DCI to higher spatial dimensions to enable assessment of traumatic axonal injury, and possibly other diseases and disorders in the brain.

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Section: Discussionsupporting

confidence: 60%

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

Benjamini

Hutchinson

Komlosh

et al. 2020

Preprint

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“…The diffusivity bounds were enforced through penalties ensuring axial and radial diffusivities between 0.2 and 4 μm 2 /ms. A lower bound above zero is the “zeppelin” compartment’s main characteristic and avoids representing water at the “dot‐compartment” limit of zero isotropic diffusivity, which is presumably negligible in healthy white matter . For T 2 values, the lower bound avoids representing water presumably fully attenuated at our echo times (e.g., myelin water), and the upper bounds were considered safe assumptions for water within “stick‐like” structures and in tissue without major contamination with cerebrospinal fluid (CSF), respectively.…”

Section: Methodsmentioning

confidence: 99%

Towards unconstrained compartment modeling in white matter using diffusion‐relaxation MRI with tensor‐valued diffusion encoding

Lampinen

Szczepankiewicz

Mårtensson

et al. 2020

Magnetic Resonance in Med

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111

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Purpose:To optimize diffusion-relaxation MRI with tensor-valued diffusion encoding for precise estimation of compartment-specific fractions, diffusivities, and T 2 values within a two-compartment model of white matter, and to explore the approach in vivo. Methods: Sampling protocols featuring different b-values (b), b-tensor shapes (b Δ ), and echo times (TE) were optimized using Cramér-Rao lower bounds (CRLB).Whole-brain data were acquired in children, adults, and elderly with white matter lesions. Compartment fractions, diffusivities, and T 2 values were estimated in a model featuring two microstructural compartments represented by a "stick" and a "zeppelin." Results: Precise parameter estimates were enabled by sampling protocols featuring seven or more "shells" with unique b/b Δ /TE-combinations. Acquisition times were approximately 15 minutes. In white matter of adults, the "stick" compartment had a fraction of approximately 0.5 and, compared with the "zeppelin" compartment, featured lower isotropic diffusivities (0.6 vs. 1.3 μm 2 /ms) but higher T 2 values (85 vs. 65 ms). Children featured lower "stick" fractions (0.4). White matter lesions exhibited high "zeppelin" isotropic diffusivities (1.7 μm 2 /ms) and T 2 values (150 ms). Conclusions: Diffusion-relaxation MRI with tensor-valued diffusion encoding expands the set of microstructure parameters that can be precisely estimated and therefore increases their specificity to biological quantities. K E Y W O R D Sbrain microstructure, diffusion-relaxation MRI, Fisher information, tensor-valued diffusion encoding 1606 | LAMPINEN Et AL. S (u) = (K ⊛ P) (u) = ∫ |n|=1 K(u ⋅ n) P(n) dn,(2) K(u ⋅ n) = S 0 J

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“…It can also inform biophysical models. [31][32][33] Waveforms that yield tensorvalued encoding have been proposed in both symmetric and asymmetric variants. [34][35][36][37] Recently, Sjölund et al 5 proposed a numerical optimization technique that can generate arbitrary b-tensor shapes with asymmetric waveforms that enabled a significant reduction of encoding times and improved SNR compared to previous designs.…”

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confidence: 99%

Maxwell‐compensated design of asymmetric gradient waveforms for tensor‐valued diffusion encoding

Szczepankiewicz

Westin

Nilsson

2019

Magnetic Resonance in Med

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127

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Purpose Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor‐valued diffusion encoding that is not sensitive to concomitant gradients. Methods The “Maxwell index” was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of “Maxwell‐compensated” waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. Results Maxwell‐compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. Conclusion Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b‐tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi‐slice acquisition, and large FOVs.

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The dot-compartment revealed? Diffusion MRI with ultra-strong gradients and spherical tensor encoding in the living human brain

Cited by 75 publications

References 82 publications

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

Direct and specific assessment of axonal injury and spinal cord microenvironments using diffusion correlation imaging

Towards unconstrained compartment modeling in white matter using diffusion‐relaxation MRI with tensor‐valued diffusion encoding

Maxwell‐compensated design of asymmetric gradient waveforms for tensor‐valued diffusion encoding

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