ConFiG: Contextual Fibre Growth to generate realistic axonal packing for diffusion MRI simulation

This paper presents Contextual Fibre Growth (ConFiG), an approach to generate white matter numerical phantoms by mimicking natural fibre genesis. ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. Signals simulated from ConFiG phantoms match real diffusion MRI data well, showing that ConFiG phantoms can be used to generate realistic diffusion MRI data. This demonstrates the feasibility of ConFiG to generate realistic synthetic diffusion MRI data for developing and validating microstructure modelling approaches

Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution

Axons in white matter have been shown to have varying geometries within a bundle using ex vivo imaging techniques, but what does this mean for diffusion MRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the fibre orientation distribution function (fODF) by assuming a single dMRI fibre response function (FRF) for all white matter populations and deconvolving this FRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre geometry within a bundle however suggests the FRF might not be constant even within a single voxel. We test what impact realistic fibre geometry has on SD by simulating the dMRI signal in a range of realistic white matter numerical phantoms, including synthetic phantoms and real axons segmented from electron microscopy. We demonstrate that variable fibre geometry leads to a variable FRF across axons and that in general no single FRF is effective to recover the underlying fibre orientation distribution function (fODF). This finding suggests that assuming a single FRF can lead to misestimation of the fODF, causing further downstream errors in techniques such as tractography

Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence

Water diffusion MRI is a very powerful tool for probing tissue microstructure, but disentangling the contribution of compartment-specific structural disorder from cellular restriction and inter-compartment exchange remains an open challenge. Here, we use diffusion MR spectroscopy (dMRS) of water and metabolites as a function of diffusion time in vivo in mouse Gray Matter (GM) to shed light on: which of these concomitant mechanisms dominates the MR measurements and with which specific signature. We report the diffusion time-dependence of water with excellent SNR conditions up to 500 ms. Water kurtosis decreases with increasing diffusion time, showing the concomitant influence of both structural disorder and exchange. Despite the excellent SNR, we were not able to identify clearly the nature of the structural disorder (i.e. 1D versus 2D/3D short-range disorder). Measurements of intracellular metabolites diffusion time-dependence (up to 500 ms) show opposite behavior to water, with metabolites kurtosis increasing as a function of diffusion time. We show that this is a signature of diffusion restricted in the intracellular space from which cellular microstructural features can be estimated. Finally, by comparing water and metabolites diffusion time-dependencies, we attempt to disentangle the effect of intra/extracellular exchange and structural disorder of the extracellular space (both impacting water diffusion only). Our results suggest a relatively short intra/extracellular exchange time (1-50 ms) and short-range disorder (still unclear if 1D or 2D/3D) most likely coming from the extracellular compartment. This work provides novel insights to interpret water diffusion time-dependent measurements in terms of the underlying GM microstructure and suggests that diffusion time-dependent measurements of intracellular metabolites may offer a new way to quantify microstructural restrictions in GM

Impact of within-voxel heterogeneity in fibre geometry on spherical deconvolution

Axons in white matter have been shown to have varying geometries within a bundle using ex vivo imaging techniques, but what does this mean for diffusion MRI (dMRI) based spherical deconvolution (SD)? SD attempts to estimate the fibre orientation distribution function (fODF) by assuming a single dMRI fibre response function (FRF) for all white matter populations and deconvolving this FRF from the dMRI signal at each voxel to estimate the fODF. Variable fibre geometry within a bundle however suggests the FRF might not be constant even within a single voxel. We test what impact realistic fibre geometry has on SD by simulating the dMRI signal in a range of realistic white matter numerical phantoms, including synthetic phantoms and real axons segmented from electron microscopy. We demonstrate that variable fibre geometry leads to a variable FRF across axons and that in general no single FRF is effective to recover the underlying fibre orientation distribution function (fODF). This finding suggests that assuming a single FRF can lead to misestimation of the fODF, causing further downstream errors in techniques such as tractography

Investigating exchange, structural disorder and restriction in Gray Matter via water and metabolites diffusivity and kurtosis time-dependence

Water diffusion-weighted MRI is a very powerful tool for probing tissue microstructure, but disentangling the contribution of compartment-specific structural disorder from cellular restriction and inter-compartment exchange remains an open challenge. In this work we use diffusion-weighted MR spectroscopy (dMRS) of water and metabolite as a function of diffusion time in vivo in mouse gray matter to shed light on: i) which of these concomitant mechanisms (structural disorder, restriction and exchange) dominates the MR measurements and ii) with which specific signature. We report the diffusion time-dependence of water with excellent SNR conditions as provided by dMRS, up to a very long diffusion time (500 ms). Water kurtosis decreases with increasing diffusion time, showing the concomitant influence of both structural disorder and exchange. However, despite the excellent experimental conditions, we were not able to clearly identify the nature of the structural disorder (i.e. 1D versus 2D/3D short-range disorder). Measurements of purely intracellular metabolites diffusion time-dependence (up to 500 ms) show opposite behavior to water, with metabolites kurtosis increasing as a function of diffusion time. We show that this is a signature of diffusion restricted in the intracellular space, from which cellular microstructural features such as soma’s and cell projections’ size can be estimated. Finally, by comparing water and metabolite diffusion time dependencies, we attempt to disentangle the effect of intra/extracellular exchange and structural disorder of the extracellular space (both impacting water diffusion only). Our results suggest a relatively short intra/extracellular exchange time (~1-50 ms) and short-range disorder (still unclear if 1D or 2D/3D) most likely coming from the extracellular compartment. This work provides novel insights to help interpret water diffusion-time dependent measurements in terms of the underlying microstructure of gray matter and suggests that diffusion-time dependent measurements of intracellular metabolites may offer a new way to quantify microstructural restrictions in gray matter

Mapping complex cell morphology in the grey matter with double diffusion encoding MR: a simulation study

This paper investigates the impact of cell body (soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to study the ability of dMRI/dMRS to characterize the complex morphology of brain grey matter, focusing on these two distinctive features. To this end, we employ a recently developed framework to create realistic meshes for Monte Carlo simulations, covering a wide range of soma sizes and branching orders of cellular projections, for diffusivities reflecting both water and metabolites. For SDE sequences, we assess the impact of soma size and branching order on the signal b-value dependence as well as the time dependence of the apparent diffusion coefficient (ADC). For DDE sequences, we assess their impact on the mixing time dependence of the signal angular modulation and of the estimated microscopic anisotropy, a promising contrast derived from DDE measurements. The SDE results show that soma size has a measurable impact on both the b-value and diffusion time dependence, for both water and metabolites. On the other hand, branching order has little impact on either, especially for water. In contrast, the DDE results show that soma size has a measurable impact on the signal angular modulation at short mixing times and the branching order significantly impacts the mixing time dependence of the signal angular modulation as well as of the derived microscopic anisotropy, for both water and metabolites. Our results confirm that soma size can be estimated from SDE based techniques, and most importantly, show for the first time that DDE measurements show sensitivity to the branching of cellular projections, paving the way for non-invasive characterization of grey matter morphology

Contextual fibre growth to generate realistic axonal packing for diffusion MRI simulation

This paper presents ConFiG, a method for generating white matter (WM) numerical phantoms with more realistic orientation dispersion and packing density. Numerical phantoms are commonly used in the validation of diffusion MRI (dMRI) techniques so it is important that they are as realistic as possible. Current numerical phantoms either oversimplify the complex morphology of WM or are unable to produce realistic orientation dispersion at high packing density. The highest packing density and orientation dispersion achieved so far is only 20% at 10∘. ConFiG takes advantage of a shift of paradigm: rather than ‘packing fibres’, our algorithm ‘grows fibres’ contextually and efficiently, attempting to produce a substrate with desired morphological priors (orientation dispersion, packing density and diameter distribution), whilst avoiding intersection between fibres. The potential of ConFiG is demonstrated by reaching the highest packing density and orientation dispersion ever, to our knowledge (25% at 35∘). The algorithm is compared with a ‘brute force’ growth approach showing that it is much more efficient, being O(n) compared to the O(n2) brute-force method. The application of the method to dMRI is demonstrated with simulations of diffusion-weighted MR signal in three example substrates with differing orientation-dispersions, packing-densities and permeabilities

Double diffusion encoding and applications for biomedical imaging

Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application