Assessing the validity of the approximation of diffusion‐weighted‐MRI signals from crossing fascicles by sums of signals from single fascicles

Rensonnet, Gaëtan; Scherrer, Benoît; Warfield, Simon K.; Macq, Benoı̂t; Taquet, Maxime

doi:10.1002/mrm.26832

Cited by 21 publications

(21 citation statements)

References 36 publications

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“…In addition, we found that simulations substrates with less than 10,000 sampled cylinders induced an important bias on the directional symmetry of the diffusion signal in directions transversal to the the main fiber direction. Such parameters are almost one order of magnitude bigger than previously used on the literature, which inherently affects the reproducibility of such results [39,40,2,21]. Second, our evaluation over the effect of introducing angular perturbations in the intra-axonal space representation-by means of the estimated axon diameter based on the cylindrical model-showed a considerable deviation from the expected results.…”

Section: Discussionmentioning

confidence: 68%

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

Rafael-Patiño

Romascano

Ramírez-Manzanares

et al. 2020

Front. Neuroinform.

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Monte-Carlo Diffusion Simulations (MCDS) have been used extensively as a ground truth tool for the validation of microstructure models for Diffusion-Weighted MRI. However, methodological pitfalls in the design of the biomimicking geometrical configurations and the simulation parameters can lead to approximation biases. Such pitfalls affect the reliability of the estimated signal, as well as its validity and reproducibility as ground truth data. In this work, we first present a set of experiments in order to study three critical pitfalls encountered in the design of MCDS in the literature, namely, the number of simulated particles and time steps, simplifications in the intra-axonal substrate representation, and the impact of the substrate's size on the signal stemming from the extra-axonal space. The results obtained show important changes in the simulated signals and the recovered microstructure features when changes in those parameters are introduced. Thereupon, driven by our findings from the first studies, we outline a general framework able to generate complex substrates. We show the framework's capability to overcome the aforementioned simplifications by generating a complex crossing substrate, which preserves the volume in the crossing area and achieves a high packing density. The results presented in this work, along with the simulator developed, pave the way towards more realistic and reproducible Monte-Carlo simulations for Diffusion-Weighted MRI.

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Section: Discussionmentioning

confidence: 68%

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

Rafael-Patiño

Romascano

Ramírez-Manzanares

et al. 2020

Front. Neuroinform.

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“…[6][7][8][9] In the context of dMRI, the measured signal  probes diffusion processes over a specific observational time-scale that depends on the choice of experimental time parameters. For a given observational time-scale, a common description of the subvoxel composition of heterogeneous tissues is obtained by considering the combined non-Gaussian diffusion effects of restriction and exchange and approximating the signal decay by a continuous weighted sum of exponential decays, 6,[10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] yielding the following multidimensional Laplace transform:…”

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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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“…Numerical simulations can help deepen the understanding of the relationship between the cellular structure and the diffusion magnetic resonance imaging signal and lead to the formulation of appropriate models. They can be also used to investigate the effect of different pulse sequences and tissue features on the measured signal which can be used for the development, testing, and optimization of novel diffusion magnetic resonance imaging pulse sequences [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

SpinDoctor: A MATLAB toolbox for diffusion MRI simulation

Nguyen

Tran

et al. 2019

NeuroImage

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The complex transverse water proton magnetization subject to diffusion-encoding magnetic field gradient pulses in a heterogeneous medium can be modeled by the multiple compartment Bloch-Torrey partial differential equation. Under the assumption of negligible water exchange between compartments, the time-dependent apparent diffusion coefficient can be directly computed from the solution of a diffusion equation subject to a time-dependent Neumann boundary condition.

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Assessing the validity of the approximation of diffusion‐weighted‐MRI signals from crossing fascicles by sums of signals from single fascicles

Cited by 21 publications

References 36 publications

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

Robust Monte-Carlo Simulations in Diffusion-MRI: Effect of the Substrate Complexity and Parameter Choice on the Reproducibility of Results

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

SpinDoctor: A MATLAB toolbox for diffusion MRI simulation

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