Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module

Fang, Chengran; Nguyen, Van Dang; Wassermann, Demián; Li, Jing-Rebecca

doi:10.1016/j.neuroimage.2020.117198

Cited by 15 publications

(22 citation statements)

References 68 publications

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“…However, the neurite behavior is typically obscured by somas (and extracellular water) in the observable signal as proposed by ( Palombo et al., 2020 ). These findings were independently reported by ( Fang et al., 2020 ) also using simulations of microscopy reconstructed neurons simultaneously with the numerical part of this investigation ( Olesen and Jespersen, 2020 ). The results are highly relevant because it validates modeling neurites as sticks in GM even if the stick power-law could not be observed in practice.…”

Section: Discussionsupporting

confidence: 83%

“…Such a component would tend to obscure the neurites’ behavior and explain the power-law's absence in GM. This hypothesis is supported experimentally by the agreement between GM data and the soma and neurite density imaging (SANDI) model, which extends the SM with a soma compartment ( Palombo et al., 2020 ), and numerically by simulations in microscopy reconstructed neurons ( Fang et al., 2020 ; Olesen and Jespersen, 2020 ).…”

Section: Introductionmentioning

confidence: 69%

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Diffusion time dependence, power-law scaling, and exchange in gray matter

et al. 2022

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Section: Discussionsupporting

confidence: 83%

Section: Introductionmentioning

confidence: 69%

Diffusion time dependence, power-law scaling, and exchange in gray matter

et al. 2022

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“… 87 Generally, more complicated modeling makes its validation more difficult. In that sense, simulation of diffusion phenomenon and DWI acquisition 88 will get more important.…”

Section: Perspectives and Discussionmentioning

confidence: 99%

Recent Advances in Parameter Inference for Diffusion MRI Signal Models

Masutani

2022

magn reson med sci

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In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.

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“…SpinDoctor provides a user‐friendly interface to easily define cell configurations relevant to the brain white matter. In Reference, 30 we presented a module of SpinDoctor called the Neuron Module that enables diffusion MRI simulations for a group of pyramidal neurons and a group of spindle neurons whose morphological descriptions were found in the neuron repository NeuroMorpho.Org 31 . The key to making accurate simulation possible is the use of high quality finite element meshes for the neurons.…”

Section: Introductionmentioning

confidence: 99%

“…The key to making accurate simulation possible is the use of high quality finite element meshes for the neurons. We refer the reader to Reference 29,30 for implementation details and timing comparisons with Monte Carlo (random walk) type simulations.…”

Section: Introductionmentioning

confidence: 99%

Practical computation of the diffusion MRI signal based on Laplace eigenfunctions: permeable interfaces

Agdestein

Tran²,

2021

NMR in Biomedicine

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The complex transverse water proton magnetization subject to diffusion‐encoding magnetic field gradient pulses in a heterogeneous medium such as brain tissue can be modeled by the Bloch‐Torrey partial differential equation. The spatial integral of the solution of this equation in realistic geometry provides a gold‐standard reference model for the diffusion MRI signal arising from different tissue micro‐structures of interest. A closed form representation of this reference diffusion MRI signal, called matrix formalism, which makes explicit the link between the Laplace eigenvalues and eigenfunctions of the tissue geometry and its diffusion MRI signal, was derived 20 years ago. In addition, once the Laplace eigendecomposition has been computed and saved, the diffusion MRI signal can be calculated for arbitrary diffusion‐encoding sequences and b‐values at negligible additional cost. In a previous publication, we presented a simulation framework that we implemented inside the MATLAB‐based diffusion MRI simulator SpinDoctor that efficiently computes the matrix formalism representation for biological cells subject to impermeable membrane boundary conditions. In this work, we extend our simulation framework to include geometries that contain permeable cell membranes. We describe the new computational techniques that allowed this generalization and we analyze the effects of the magnitude of the permeability coefficient on the eigendecomposition of the diffusion and Bloch‐Torrey operators. This work is another step in bringing advanced mathematical tools and numerical method development to the simulation and modeling of diffusion MRI.

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Diffusion MRI simulation of realistic neurons with SpinDoctor and the Neuron Module

Cited by 15 publications

References 68 publications

Diffusion time dependence, power-law scaling, and exchange in gray matter

Diffusion time dependence, power-law scaling, and exchange in gray matter

Recent Advances in Parameter Inference for Diffusion MRI Signal Models

Practical computation of the diffusion MRI signal based on Laplace eigenfunctions: permeable interfaces

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