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

Lampinen, Björn; Szczepankiewicz, Filip; Novén, Mikael; Westen, Danielle van; Hansson, Oskar; Englund, Elisabet; Mårtensson, Johan; Westin, Carl Fredrik; Nilsson, Markus

doi:10.1002/hbm.24542

Cited by 102 publications

(147 citation statements)

References 113 publications

(245 reference statements)

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“…The total MRI signal is then given by the weighted sum of the signals from water molecules diffusing in each compartment. Although very successful in describing the DW-MRI signal in WM and GM at relatively low b values (b3 ms/m 2 ) in both healthy and diseased conditions (2)(3)(4)(5)(6)(7)(8)(17)(18)(19)(20)(21)(22)(23)(24)(25) (73), this technique has the potential for translation into the clinic, opening a promising avenue for more in-depth assessment of cellular microstructure in-vivo in human brain.…”

Section: Resultsmentioning

confidence: 99%

“…We note that more advanced DW-MRI acquisition schemes such as B-tensor encoding (64) parcellation of the brain cyto-architecture (Figure 9 and 10). As shown in Figure 10 (18,19), while recent works using DDE showed that this acquisition scheme can help disentangling different sources of DW-MRI signal that can be linked to different features of the underpinning tissue microstructure (69)(70)(71)(72). Future works will focus on harnessing the orthogonal information offered by these advanced acquisition schemes in order to maximize the sensitivity and specificity of the measured DW-MRI signal to the soma contribution.…”

Section: Discussionmentioning

confidence: 99%

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SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

et al. 2020

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This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging trough DW-MRI presents water diffusion in white (WM) and grey (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b3 ms/m 2 , it has been also shown to fail in GM at high b values (b>>3 ms/m 2 ). Here we hypothesize that the unmodelled soma compartment may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40 ms/m 2 ) and human (bmax = 10 ms/m 2 ) brain. We show that SANDI defines new contrasts representing new complementary information on the brain cyto-and myelo-architecture. Indeed, we show for the first-time maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto-and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

et al. 2020

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“…Due to flat fitting landscapes, several assumptions and constraints are required to stabilize fits (Assaf, Freidlin, Rohde, & Basser, 2004;Fieremans et al, 2011;Jelescu, Veraart, Fieremans, & Novikov, 2016;Jespersen, Kroenke, Østergaard, Ackerman, & Yablonskiy, 2007;Stanisz, Szafer, Wright, Henkelman, & Szafer, 1997;Zhang, Schneider, Wheeler-Kingshott, & Alexander, 2012). Although these techniques might provide very appealing maps, recent studies showed that the required assumptions and constraints can compromise the specificity of the measures extracted (Henriques et al, 2019;Lampinen et al, 2017Lampinen et al, , 2019Novikov, Veraart, Jelescu, & Fieremans, 2018).…”

Section: Introductionmentioning

confidence: 99%

Correlation tensor magnetic resonance imaging

2020

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Diffusion Kurtosis MRI (DKI) quantifies the degree of non-Gaussian water diffusion, which has been shown to be a very sensitive biomarker for microstructure in health and disease.However, DKI is not specific to any microstructural property per se since kurtosis might emerge from several different sources. Q-space trajectory encoding schemes have been proposed to decouple kurtosis related with the variance of different diffusion magnitudes (isotropic kurtosis) from kurtosis related with microscopic anisotropy (anisotropic kurtosis), under explicit assumptions of vanishing intra-compartmental kurtosis and diffusion time independence. Here, we introduce correlation tensor imaging (CTI) an approach that can be used to more generally resolve different kurtosis sources. CTI exploits the versatility of the double diffusion encoding (DDE) sequence and its associated Z tensor to resolve the isotropic and anisotropic components of kurtosis; in addition, CTI also disentangles these two measures from restricted, time-dependent kurtosis, thereby providing an index for intra-compartmental kurtosis. The theoretical foundations of CTI are presented, as well as predictive numerical simulations. The first, proof-of-concept CTI ex vivo experiments were performed in mouse brain specimens revealing the underlying sources of diffusion kurtosis. We find that anisotropic kurtosis dominates in white matter regions, while isotropic kurtosis is low for both white and grey matter; by contrast, areas with substantial partial volume effects show high isotropic kurtosis. Intra-compartmental kurtosis estimates were found to have positive values suggesting that non-Gaussian, time-dependant restricted diffusion effects are not negligible, at least for our acquisition settings. We then performed in vivo CTI in heathy adult rat brains, and found the results to be consistent with the ex vivo findings, thereby demonstrating that CTI is readily incorporated into preclinical scanners. CTI can be thus used as a powerful tool for resolving kurtosis sources in vivo.

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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.…”

mentioning

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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121

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

Cited by 102 publications

References 113 publications

SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI

Correlation tensor magnetic resonance imaging

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

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