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

Callaghan, Ross; Alexander, Daniel C.; Palombo, Marco; Zhang, Hui

doi:10.1016/j.neuroimage.2020.117107

Cited by 28 publications

(32 citation statements)

References 71 publications

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“…Last, the axonal diameter variations and dispersion behavior presented here could act as an axonal "fingerprint" to guide the construction of anatomically informed axonal phantoms for MC simulations. Existing frameworks to model morphological features such as fiber undulation (44, 54) (although we do not observe periodic undulations in our data) and diameter variations (55,56) exist. Others allow for the generation of a more complex WM environment with beaded axons and cells (57).…”

Section: Discussionmentioning

confidence: 41%

Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship

Andersson

Kjer

Rafael-Patiño

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

112

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Axonal conduction velocity, which ensures efficient function of the brain network, is related to axon diameter. Noninvasive, in vivo axon diameter estimates can be made with diffusion magnetic resonance imaging, but the technique requires three-dimensional (3D) validation. Here, high-resolution, 3D synchrotron X-ray nano-holotomography images of white matter samples from the corpus callosum of a monkey brain reveal that blood vessels, cells, and vacuoles affect axonal diameter and trajectory. Within single axons, we find that the variation in diameter and conduction velocity correlates with the mean diameter, contesting the value of precise diameter determination in larger axons. These complex 3D axon morphologies drive previously reported 2D trends in axon diameter and g-ratio. Furthermore, we find that these morphologies bias the estimates of axon diameter with diffusion magnetic resonance imaging and, ultimately, impact the investigation and formulation of the axon structure–function relationship.

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Section: Discussionmentioning

confidence: 41%

Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship

Andersson

Kjer

Rafael-Patiño

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

112

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“…In current literature, the impact of undulations often focuses on the measurement of radial diffusion or axon diameter [32, 75, 33], though the effect of undulations on other diffusion characteristics, such as axial diffusion, deserve further investigation. Future work will benefit greatly from more realistic simulations [76, 34, 33] where, for example, the tissue structure is directly inspired by 3D reconstructed surfaces from microscopy images of real tissue [77, 34, 33].…”

Section: Discussionmentioning

confidence: 99%

Estimating axial diffusivity in the NODDI model

Howard

Lange

Mollink

et al. 2020

Preprint

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By analysing the diffusion MRI signal, we can infer information about the microscopic structure of the brain. Two parameters of interest - the intra-axonal axial diffusivity and fibre orientation dispersion - are potential biomarkers for very different aspects of the white matter microstructure, yet they are difficult to disentangle. The parameters covary such that, if one is not accurately accounted for, the other will be biased. In this work we use high b-value data to isolate the signal from the intra-axonal compartment and resolve any degeneracies with the extra-axonal compartment. In the high b-value regime, we then use a model of dispersed sticks to estimate the intra-axonal axial diffusivity and fibre orientation distribution on a voxelwise basis. Our results in in vivo, human data show an intra-axonal axial diffusivity of ~ 2.3 – 3 μm2/ms, where 3 μm2/ms is the diffusivity of free water at 37°C. The intra-axonal axial diffusivity is seen to vary considerably across the white matter. For example, in the corpus callosum we find high values in the genu and splenium, and lower values in the midbody. Furthermore, the axial diffusivity and orientation dispersion appear negatively correlated, behaviour which we show is consistent with the presence of fibre undulations but not consistent with a degeneracy between fanning fibres and axial diffusivity. Finally, we demonstrate that the parameter maps output from Neurite Orientation Dispersion and Density Imaging (NODDI) change substantially when the assumed axial diffusivity was increased from 1.7 to 2.5 or 3 μm2/ms.

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“…A number of techniques have been used to produce dMRI ground truths, 1,2 including numerical phantoms that simulate scan data, 3,4 histology of brain samples scanned ex vivo 5,6 or in vivo before extraction, 7‐9 and physical phantoms with separately characterized microstructure 10,11 . Numerical phantoms can use analytic models of diffusion in substrates composed of well‐defined compartments, 12 or Monte Carlo simulations of diffusion using arbitrarily complex and realistic mesh‐based substrates 13‐15 . Numerical phantoms allow precise experimental control and increasingly realistic substrates, but realistic Monte Carlo simulations demand significant computational resources, which limits the possible volume of simulated substrates.…”

Section: Introductionmentioning

confidence: 99%

Design and characterization of a 3D‐printed axon‐mimetic phantom for diffusion MRI

Mushtaha

Kuehn

El‐Deeb

et al. 2021

Magnetic Resonance in Med

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Purpose To introduce and characterize inexpensive and easily produced 3D‐printed axon‐mimetic diffusion MRI phantoms in terms of pore geometry and diffusion kurtosis imaging metrics. Methods Phantoms were 3D‐printed with a composite printing material that, after the dissolution of the polyvinyl alcohol, exhibits microscopic fibrous pores. Confocal microscopy and synchrotron phase‐contrast micro‐CT imaging were performed to visualize and assess the pore sizes. Diffusion MRI scans of four identical phantoms and phantoms with varying print parameters in water were performed at 9.4 T. Diffusion kurtosis imaging was fit to both data sets and used to assess the reproducibility between phantoms and effects of print parameters on diffusion kurtosis imaging metrics. Identical scans were performed 25 and 76 days later, to test their stability. Results Segmentation of pores in three microscopy images yielded a mean, median, and SD of equivalent pore diameters of 7.57 μm, 3.51 μm, and 12.13 μm, respectively. Phantoms had T1/T2 = 2 seconds/180 ms, and those with identical parameters showed a low coefficient of variation (~10%) in mean diffusivity (1.38 × 10−3 mm2/s) and kurtosis (0.52) metrics and radial diffusivity (1.01 × 10−3 mm2/s) and kurtosis (1.13) metrics. Printing temperature and speed had a small effect on diffusion kurtosis imaging metrics (< 16%), whereas infill density had a larger and more variable effect (> 16%). The stability analysis showed small changes over 2.5 months (< 7%). Conclusion Three‐dimension‐printed axon‐mimetic phantoms can mimic the fibrous structure of axon bundles on a microscopic scale, serving as complex, anisotropic diffusion MRI phantoms.

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

Cited by 28 publications

References 71 publications

Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship

Axon morphology is modulated by the local environment and impacts the noninvasive investigation of its structure–function relationship

Estimating axial diffusivity in the NODDI model

Design and characterization of a 3D‐printed axon‐mimetic phantom for diffusion MRI

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