Accuracies and Contrasts of Models of the Diffusion-weighted-dependent Attenuation of the Mri Signal at Intermediate <i>B</i>-values

Renaud, Nicolas; Sibon, Igor; Hiba, Bassem

doi:10.4137/mri.s25301

Cited by 8 publications

(3 citation statements)

References 44 publications

(179 reference statements)

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“…Three-compartment models to describe the IVIM signal have previously been applied to brain [3,28,29], and prostate cancer [30]. The main difference between the previous models and the one used in the present study is the addition of relaxation compensation.…”

Section: Discussionmentioning

confidence: 99%

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Rydhög

Pasternak

Ståhlberg

et al. 2019

European Journal of Radiology Open

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Compartmental diffusion MRI models that account for intravoxel incoherent motion (IVIM) of blood perfusion allow for estimation of the fractional volume of the microvascular compartment. Conventional IVIM models are known to be biased by not accounting for partial volume effects caused by free water and cerebrospinal fluid (CSF), or for tissue-dependent relaxation effects. In this work, a three-compartment model (tissue, free water and blood) that includes relaxation terms is introduced. To estimate the model parameters, in vivo human data were collected with multiple echo times (TE), inversion times (TI) and b-values, which allowed a direct relaxation estimate alongside estimation of perfusion, diffusion and fractional volume parameters. Compared to conventional two-compartment models (with and without relaxation compensation), the three-compartment model showed less effects of CSF contamination. The proposed model yielded significantly different volume fractions of blood and tissue compared to the non-relaxation-compensated model, as well as to the conventional two-compartment model, suggesting that previously reported parameter ranges, using models that do not account for relaxation, should be reconsidered.

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Section: Discussionmentioning

confidence: 99%

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Rydhög

Pasternak

Ståhlberg

et al. 2019

European Journal of Radiology Open

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“…This value was selected based on previous experimental diffusion MRI studies (19, 20), although faster diffusivities have also been reported (18, 34). Other three compartmental models to disentangle diffusivities have been proposed before (34, 35), but these were different in that the tissue compartment was assumed to be isotropic (34, 35) or that tissue was modeled as two isotropic compartments (fast and slow) without an explicit free-water compartment (34). Here our main goal is to evaluate how neglecting the perfusion compartment affects the estimation of free-water and other diffusivities in white and gray matter, which required an anisotropic model for tissue, and an explicit free-water compartment.…”

Section: Methodsmentioning

confidence: 99%

Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain

et al. 2017

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The assessment of the free water fraction in the brain provides important information about extracellular processes such as atrophy and neuroinflammation in various clinical conditions as well as in normal development and aging. Free water estimates from diffusion MRI are assumed to account for freely diffusing water molecules in the extracellular space, but may be biased by other pools of molecules in rapid random motion, such as the intravoxel incoherent motion (IVIM) of blood, where water molecules perfuse in the randomly oriented capillary network. The goal of this work was to separate the signal contribution of the perfusing blood from that of free-water and of other brain diffusivities. The influence of the vascular compartment on the estimation of the free water fraction and other diffusivities was investigated by simulating perfusion in diffusion MRI data. The perfusion effect in the simulations was significant, especially for the estimation of the free water fraction, and was maintained as long as low b-value data were included in the analysis. Two approaches to reduce the perfusion effect were explored in this study: (i) increasing the minimal b-value used in the fitting, and (ii) using a three-compartment model that explicitly accounts for water molecules in the capillary blood. Estimation of the model parameters while excluding low b-values reduced the perfusion effect but was highly sensitive to noise. The three-compartment model fit was more stable and additionally, provided an estimation of the volume fraction of the capillary blood compartment. The three-compartment model thus disentangles the effects of free water diffusion and perfusion, which is of major clinical importance since changes in these components in the brain may indicate different pathologies, i.e., those originating from the extracellular space, such as neuroinflammation and atrophy, and those related to the vascular space, such as vasodilation, vasoconstriction and capillary density. Diffusion MRI data acquired from a healthy volunteer, using multiple b-shells, demonstrated an expected non-zero contribution from the blood fraction, and indicated that not accounting for the perfusion effect may explain the overestimation of the free water fraction evinced in previous studies. Finally, the applicability of the method was demonstrated with a dataset acquired using a clinically feasible protocol with shorter acquisition time and fewer b-shells.

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“…41 While non-parametric techniques have been developed to retrieve the entire DTD from dMRI data, such as the marginal distributions constrained optimization (MADCO), [42][43][44] multidimensional correlation spectroscopic imaging, 45,46 and Monte-Carlo signal inversions, [47][48][49][50] an alternative approach consists in approximating P(D) with a plausible parametric functional form whose parameters can be fitted against the acquired signal. This parametric approach encompasses diffusion tensor imaging (DTI), 3 signal representations based on normal, 18,[51][52][53][54] log-normal 55,56 and Gamma [56][57][58] distributions of diffusivities, models 25,31,34,[59][60][61][62] and higherthan-second-order truncated cumulant expansions. 63,64 In particular, two-term cumulant expansions of the low b-value diffusion signal are equivalent to considering a normal distribution of diffusivities, as detailed in previous work.…”

Section: Introductionmentioning

confidence: 99%

Matrix moments of the diffusion tensor distribution

Reymbaut

2020

Preprint

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Purpose: To facilitate the implementation/validation of signal representations and models using parametric matrix-variate distributions to approximate the diffusion tensor distribution (DTD) P(D). Theory: We establish practical mathematical tools, the matrix moments of the DTD, enabling to compute the mean diffusion tensor and covariance tensor associated with any parametric matrixvariate DTD whose moment-generating function is known. As a proof of concept, we apply these tools to the non-central matrix-variate Gamma (nc-mv-Gamma) distribution, whose covariance tensor was so far unknown, and design a new signal representation capturing intra-voxel heterogeneity via a single nc-mv-Gamma distribution: the matrix-variate Gamma approximation. Methods: Furthering this proof of concept, we evaluate the matrix-variate Gamma approximation in silico and in vivo, in a human-brain 'tensor-valued' diffusion MRI dataset. Results: The matrix-variate Gamma approximation fails to capture the heterogeneity arising from orientation dispersion and from simultaneous variances in the trace (size) and anisotropy (shape) of the underlying diffusion tensors, which is explained by the structure of the covariance tensor associated with the nc-mv-Gamma distribution. Conclusion:The matrix moments promote a more widespread use of matrix-variate distributions as plausible approximations of the DTD by alleviating their intractability, thereby facilitating the design/validation of matrix-variate microstructural techniques.

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Accuracies and Contrasts of Models of the Diffusion-weighted-dependent Attenuation of the Mri Signal at Intermediate B-values

Cited by 8 publications

References 44 publications

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Estimation of diffusion, perfusion and fractional volumes using a multi-compartment relaxation-compensated intravoxel incoherent motion (IVIM) signal model

Separating blood and water: Perfusion and free water elimination from diffusion MRI in the human brain

Matrix moments of the diffusion tensor distribution

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