1,723 research outputs found
Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND)
A key challenge in medical imaging is determining a precise correspondence between image properties and tissue microstructure. This comparison is hindered by disparate scales and resolutions between medical imaging and histology. We present a new technique, 3D Bridging of Optically-clear histology with Neuroimaging Data (3D-BOND), for registering medical images with 3D histology to overcome these limitations. Ex vivo 120 × 120 × 200 μm resolution diffusion-MRI (dMRI) data was acquired at 7 T from adult C57Bl/6 mouse hippocampus. Tissue was then optically cleared using CLARITY and stained with cellular markers and confocal microscopy used to produce high-resolution images of the 3D-tissue microstructure. For each sample, a dense array of hippocampal landmarks was used to drive registration between upsampled dMRI data and the corresponding confocal images. The cell population in each MRI voxel was determined within hippocampal subregions and compared to MRI-derived metrics. 3D-BOND provided robust voxel-wise, cellular correlates of dMRI data. CA1 pyramidal and dentate gyrus granular layers had significantly different mean diffusivity (p > 0.001), which was related to microstructural features. Overall, mean and radial diffusivity correlated with cell and axon density and fractional anisotropy with astrocyte density, while apparent fibre density correlated negatively with axon density. Astrocytes, axons and blood vessels correlated to tensor orientation
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
Towards in vivo g-ratio mapping using MRI: unifying myelin and diffusion imaging
The g-ratio, quantifying the comparative thickness of the myelin sheath
encasing an axon, is a geometrical invariant that has high functional relevance
because of its importance in determining neuronal conduction velocity. Advances
in MRI data acquisition and signal modelling have put in vivo mapping of the
g-ratio, across the entire white matter, within our reach. This capacity would
greatly increase our knowledge of the nervous system: how it functions, and how
it is impacted by disease. This is the second review on the topic of g-ratio
mapping using MRI. As such, it summarizes the most recent developments in the
field, while also providing methodological background pertinent to aggregate
g-ratio weighted mapping, and discussing pitfalls associated with these
approaches. Using simulations based on recently published data, this review
demonstrates the relevance of the calibration step for three myelin-markers
(macromolecular tissue volume, myelin water fraction, and bound pool fraction).
It highlights the need to estimate both the slope and offset of the
relationship between these MRI-based markers and the true myelin volume
fraction if we are really to achieve the goal of precise, high sensitivity
g-ratio mapping in vivo. Other challenges discussed in this review further
evidence the need for gold standard measurements of human brain tissue from ex
vivo histology. We conclude that the quest to find the most appropriate MRI
biomarkers to enable in vivo g-ratio mapping is ongoing, with the potential of
many novel techniques yet to be investigated.Comment: Will be published as a review article in Journal of Neuroscience
Methods as parf of the Special Issue with Hu Cheng and Vince Calhoun as Guest
Editor
An open resource combining multi-contrast MRI and microscopy in the macaque brain
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular structures to macroscale features of whole brain organisation. Here we introduce the BigMac dataset, a resource combining in vivo MRI, extensive postmortem MRI and multi-contrast microscopy for multimodal characterisation of a single whole macaque brain. The data spans modalities (MRI and microscopy), tissue states (in vivo and postmortem), and four orders of spatial magnitude, from microscopy images with micrometre or sub-micrometre resolution, to MRI signals on the order of millimetres. Crucially, the MRI and microscopy images are carefully co-registered together to facilitate quantitative multimodal analyses. Here we detail the acquisition, curation, and first release of the data, that together make BigMac a unique, openly-disseminated resource available to researchers worldwide. Further, we demonstrate example analyses and opportunities afforded by the data, including improvement of connectivity estimates from ultra-high angular resolution diffusion MRI, neuroanatomical insight provided by polarised light imaging and myelin-stained histology, and the joint analysis of MRI and microscopy data for reconstruction of the microscopy-inspired connectome. All data and code are made openly available
Axon diameters and myelin content modulate microscopic fractional anisotropy at short diffusion times in fixed rat spinal cord
Mapping tissue microstructure accurately and noninvasively is one of the
frontiers of biomedical imaging. Diffusion Magnetic Resonance Imaging (MRI) is
at the forefront of such efforts, as it is capable of reporting on microscopic
structures orders of magnitude smaller than the voxel size by probing
restricted diffusion. Double Diffusion Encoding (DDE) and Double Oscillating
Diffusion Encoding (DODE) in particular, are highly promising for their ability
to report on microscopic fractional anisotropy ({\mu}FA), a measure of the pore
anisotropy in its own eigenframe, irrespective of orientation distribution.
However, the underlying correlates of {\mu}FA have insofar not been studied.
Here, we extract {\mu}FA from DDE and DODE measurements at ultrahigh magnetic
field of 16.4T in the aim to probe fixed rat spinal cord microstructure. We
further endeavor to correlate {\mu}FA with Myelin Water Fraction (MWF) derived
from multiexponential T2 relaxometry, as well as with literature-based
spatially varying axonal diameters. In addition, a simple new method is
presented for extracting unbiased {\mu}FA from three measurements at different
b-values. Our findings reveal strong anticorrelations between {\mu}FA (derived
from DODE) and axon diameter in the distinct spinal cord tracts; a moderate
correlation was also observed between {\mu}FA derived from DODE and MWF. These
findings suggest that axonal membranes strongly modulate {\mu}FA, which - owing
to its robustness towards orientation dispersion effects - reflects axon
diameter much better than its typical FA counterpart. The {\mu}FA exhibited
modulations when measured via oscillating or blocked gradients, suggesting
selective probing of different parallel path lengths and providing insight into
how those modulate {\mu}FA metrics. Our findings thus shed light into the
underlying microstructural correlates of {\mu}FA and are (...
Generation of realistic white matter substrates with controllable morphology for diffusion MRI simulations
Numerical phantoms have played a key role in the development of diffusion MRI (dMRI)
techniques seeking to estimate features of the microscopic structure of tissue by providing
a ground truth for simulation experiments against which we can validate and compare
techniques. One common limitation of numerical phantoms which represent white matter
(WM) is that they oversimplify the true complex morphology of the tissue which has
been revealed through ex vivo studies. It is important to try to generate WM numerical
phantoms that capture this realistic complexity in order to understand how it impacts the
dMRI signal.
This thesis presents work towards improving the realism of WM numerical phantoms
by generating fibres mimicking natural fibre genesis. A novel phantom generator is
presented which was developed over two works, resulting in Contextual Fibre Growth
(ConFiG). 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. ConFiG is applied to investigate the intra-axonal diffusivity
and probe assumptions in a family of dMRI modelling techniques based on spherical
deconvolution (SD), demonstrating that the microscopic variations in fibres’ shapes
affects the diffusion within axons. This leads to variations in the per-fibre signal contrary
to the assumptions inherent in SD which may have a knock-on effect in popular techniques
such as tractography
Computational modelling of diffusion magnetic resonance imaging based on cardiac histology
The exact relationship between changes in myocardial microstructure as a result of heart disease and the signal measured using diffusion tensor cardiovascular magnetic resonance (DT-CMR) is currently not well understood. Computational modelling of diffusion in combination with realistic numerical phantoms offers the unique opportunity to study effects of pathologies or the efficacy of improvements to acquisition protocols in a controlled in-silico environment. In this work, Monte Carlo random walk (MCRW) methods are used to simulate diffusion in a histology-based 3D model of the myocardium. Sensitivity of typical DT-CMR sequences to changes in tissue properties is assessed.
First, myocardial tissue is analysed to identify important geometric features and diffusion parameters. A two-compartment model is considered where intra-cellular compartments with a reduced bulk diffusion coefficient are separated from extra-cellular space by permeable membranes. Secondary structures like groups of cardiomyocyte (sheetlets) must also be included, and different methods are developed to automatically generate realistic histology-based substrates. Next, in-silico simulation of DT-CMR is reviewed and a tool to generate idealised versions of common pulse sequences is discussed. An efficient GPU-based numerical scheme for obtaining a continuum solution to the Bloch--Torrey equations is presented and applied to domains directly extracted from histology images. In order to verify the numerical methods used throughout this work, an analytical solution to the diffusion equation in 1D is described. It relies on spectral analysis of the diffusion operator and requires that all roots of a complex transcendental equation are found. To facilitate a fast and reliable solution, a novel root finding algorithm based on Chebyshev polynomial interpolation is proposed. To simulate realistic 3D geometries MCRW methods are employed. A parallel simulator for both grid-based and surface mesh--based geometries is presented. The presence of permeable membranes requires special treatment. For this, a commonly used transit model is analysed. Finally, the methods above are applied to study the effect of various model and sequence parameters on DT-CMR results. Simulations with impermeable membranes reveal sequence-specific sensitivity to extra-cellular volume fraction and diffusion coefficients. By including membrane permeability, DT-CMR results further approach values expected in vivo.Open Acces
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
The value of in vivo preclinical diffusion MRI (dMRI) is substantial.
Small-animal dMRI has been used for methodological development and validation,
characterizing the biological basis of diffusion phenomena, and comparative
anatomy. Many of the influential works in this field were first performed in
small animals or ex vivo samples. The steps from animal setup and monitoring,
to acquisition, analysis, and interpretation are complex, with many decisions
that may ultimately affect what questions can be answered using the data. This
work aims to serve as a reference, presenting selected recommendations and
guidelines from the diffusion community, on best practices for preclinical dMRI
of in vivo animals. In each section, we also highlight areas for which no
guidelines exist (and why), and where future work should focus. We first
describe the value that small animal imaging adds to the field of dMRI,
followed by general considerations and foundational knowledge that must be
considered when designing experiments. We briefly describe differences in
animal species and disease models and discuss how they are appropriate for
different studies. We then give guidelines for in vivo acquisition protocols,
including decisions on hardware, animal preparation, imaging sequences and data
processing, including pre-processing, model-fitting, and tractography. Finally,
we provide an online resource which lists publicly available preclinical dMRI
datasets and software packages, to promote responsible and reproducible
research. An overarching goal herein is to enhance the rigor and
reproducibility of small animal dMRI acquisitions and analyses, and thereby
advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl
Recommended from our members
Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.
Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties
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