Relevance of time‐dependence for clinically viable diffusion imaging of the spinal cord

Grussu, Francesco; Ianuş, Andrada; Tur, Carmen; Prados, Ferrán; Schneider, Torben; Kaden, Enrico; Ourselin, Sébastien; Drobnjak, Ivana; Zhang, Hui; Alexander, Daniel C.; Wheeler‐Kingshott, Claudia A. M.

doi:10.1002/mrm.27463

Cited by 30 publications

(34 citation statements)

References 78 publications

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“…(Cheung et al, 2012;Falangola et al, 2008;Henriques, 2018;Lin et al, 2018;Rudrapatna et al, 2014;Sun et al, 2015). Clinical applications of diffusion kurtosis MRI abound, and deeper investigations into kurtosis features, such as time dependence, are being vigorously studied (Grussu et al, 2019;Jespersen, Olesen, Hansen, & Shemesh, 2018;Pyatigorskaya, Le Bihan, Reynaud, & Ciobanu, 2014). Nearly invariably, these measurements are performed using single diffusion encoding pulses sequences; however, these SDE methods cannot separate different sources of kurtosis, which would clearly benefit the field by assigning a degree of specificity to such measurements.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

Correlation tensor magnetic resonance imaging

2020

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“…The model consists of a superposition of a Gaussian component and a series of components exhibiting restricted diffusion in infinite cylindrical geometry. Very recently Grussu et al, demonstrated that modeling the axons as small radii cylinders is a better approximation than the stick model axons. Note, however, that our model neglects any effect of exchange or orientation dispersion.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the relatively good agreement between the SDE and DDE MRI results and the general agreement between MRI and histology, the detailed comparison between MRI and histology in the specific ROIs shows only good to fair agreement depending on the ROI analyzed. This may be the result of several potential impacting factors like the limitations of the modeling of the MRI data which neglect the diffusion time variations and assumes that restriction originates mainly from the intra axonal space although it is clear that it may originate also from the extra axonal space . Additionally, the model does not consider dispersion, which might have some impact even in SC.…”

Section: Discussionmentioning

confidence: 99%

Single‐ and double‐Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter

et al. 2019

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Mapping average axon diameter (AAD) and axon diameter distribution (ADD) in neuronal tissues non‐invasively is a challenging task that may have a tremendous effect on our understanding of the normal and diseased central nervous system (CNS). Water diffusion is used to probe microstructure in neuronal tissues, however, the different water populations and barriers that are present in these tissues turn this into a complex task. Therefore, it is not surprising that recently we have witnessed a burst in the development of new approaches and models that attempt to obtain, non‐invasively, detailed microstructural information in the CNS. In this work, we aim at challenging and comparing the microstructural information obtained from single diffusion encoding (SDE) with double diffusion encoding (DDE) MRI. We first applied SDE and DDE MR spectroscopy (MRS) on microcapillary phantoms and then applied SDE and DDE MRI on an ex vivo porcine spinal cord (SC), using similar experimental conditions. The obtained diffusion MRI data were fitted by the same theoretical model, assuming that the signal in every voxel can be approximated as the superposition of a Gaussian‐diffusing component and a series of restricted components having infinite cylindrical geometries. The diffusion MRI results were then compared with histological findings. We found a good agreement between the fittings and the experimental data in white matter (WM) voxels of the SC in both diffusion MRI methods. The microstructural information and apparent AADs extracted from SDE MRI were found to be similar or somewhat larger than those extracted from DDE MRI especially when the diffusion time was set to 40 ms. The apparent ADDs extracted from SDE and DDE MRI show reasonable agreement but somewhat weaker correspondence was observed between the diffusion MRI results and histology. The apparent subtle differences between the microstructural information obtained from SDE and DDE MRI are briefly discussed.

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“…Changing the observational time-scale may very well lead to a different set of exponential decays as a result of restricted diffusion 35 and exchange, 36 which implies that the measured DTD may depend on the time-scales or spectral contents of the diffusion-encoding gradients. Even though such time-dependent effects have been measured in human brain white matter, [37][38][39][40][41][42] spinal cord, 43,44 and prostate, 45,46 using stimulated echo or oscillating gradient sequences specifically designed for varying the observational time-scale over extended ranges, the above DTD description holds for the limited range of time-scales probed by clinical dMRI experiments in the brain. 44,[47][48][49][50][51][52][53][54] While (D) provides a simple, and experimentally accessible, description of intravoxel tissue heterogeneity, the inversion of Equation 1constitutes a challenging ill-posed problem, where clearly distinct distributions may result in signal decays that are indistinguishable within the experimental noise, thus complicating the detailed interpretation of dMRI data.…”

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confidence: 99%

Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms

et al. 2020

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In biological tissues, typical MRI voxels comprise multiple microscopic environments, the local organization of which can be captured by microscopic diffusion tensors. The measured diffusion MRI signal can, therefore, be written as the multidimensional Laplace transform of an intravoxel diffusion tensor distribution (DTD). Tensor-valued diffusion encoding schemes have been designed to probe specific features of the DTD, and several algorithms have been introduced to invert such data and estimate statistical descriptors of the DTD, such as the mean diffusivity, the variance of isotropic diffusivities, and the mean squared diffusion anisotropy. However, the accuracy and precision of these estimations have not been assessed systematically and compared across methods. In this article, we perform and compare such estimations in silico for a one-dimensional Gamma fit, a generalized two-term cumulant approach, and two-dimensional and four-dimensional Monte-Carlo-based inversion techniques, using a clinically feasible tensor-valued acquisition scheme. In particular, we compare their performance at different signal-to-noise ratios (SNRs) for voxel contents varying in terms of the aforementioned statistical descriptors, orientational order, and fractions of isotropic and anisotropic components. We find that all inversion techniques share similar precision (except for a lower precision of the two-dimensional Monte Carlo inversion) but differ in terms of accuracy. While the Gamma fit exhibits infinite-SNR biases when the signal deviates strongly from monoexponentiality and is unaffected by orientational order, the generalized cumulant approach shows infinite-SNR biases when this deviation originates from the variance in isotropic diffusivities or from the low orientational order of anisotropic diffusion components. The two-dimensional Monte Carlo inversion shows remarkable accuracy in all systems studied, given that the acquisition scheme possesses enough directions to yield a rotationally invariant powder average. The four-dimensional Monte Carlo inversion presents no infinite-SNR bias, but suffers significantly from noise in the data, while preserving good contrast in most systems investigated. KEYWORDS diffusion MRI, in silico validation, Laplace inversion, microstructure, tensor-valued diffusion encoding 1 INTRODUCTION Diffusion magnetic resonance imaging (dMRI) allows the characterization of living tissues below the millimeter scale set by typical imaging voxel sizes. The basic principle of dMRI techniques is to use magnetic field gradients to encode the acquired signal with information about the

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Relevance of time‐dependence for clinically viable diffusion imaging of the spinal cord

Cited by 30 publications

References 78 publications

Correlation tensor magnetic resonance imaging

Correlation tensor magnetic resonance imaging

Single‐ and double‐Diffusion encoding MRI for studying ex vivo apparent axon diameter distribution in spinal cord white matter

Accuracy and precision of statistical descriptors obtained from multidimensional diffusion signal inversion algorithms

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