233 research outputs found
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution
We propose two strategies to improve the quality of tractography results
computed from diffusion weighted magnetic resonance imaging (DW-MRI) data. Both
methods are based on the same PDE framework, defined in the coupled space of
positions and orientations, associated with a stochastic process describing the
enhancement of elongated structures while preserving crossing structures. In
the first method we use the enhancement PDE for contextual regularization of a
fiber orientation distribution (FOD) that is obtained on individual voxels from
high angular resolution diffusion imaging (HARDI) data via constrained
spherical deconvolution (CSD). Thereby we improve the FOD as input for
subsequent tractography. Secondly, we introduce the fiber to bundle coherence
(FBC), a measure for quantification of fiber alignment. The FBC is computed
from a tractography result using the same PDE framework and provides a
criterion for removing the spurious fibers. We validate the proposed
combination of CSD and enhancement on phantom data and on human data, acquired
with different scanning protocols. On the phantom data we find that PDE
enhancements improve both local metrics and global metrics of tractography
results, compared to CSD without enhancements. On the human data we show that
the enhancements allow for a better reconstruction of crossing fiber bundles
and they reduce the variability of the tractography output with respect to the
acquisition parameters. Finally, we show that both the enhancement of the FODs
and the use of the FBC measure on the tractography improve the stability with
respect to different stochastic realizations of probabilistic tractography.
This is shown in a clinical application: the reconstruction of the optic
radiation for epilepsy surgery planning
Rotationally-invariant mapping of scalar and orientational metrics of neuronal microstructure with diffusion MRI
We develop a general analytical and numerical framework for estimating intra-
and extra-neurite water fractions and diffusion coefficients, as well as
neurite orientational dispersion, in each imaging voxel. By employing a set of
rotational invariants and their expansion in the powers of diffusion weighting,
we analytically uncover the nontrivial topology of the parameter estimation
landscape, showing that multiple branches of parameters describe the
measurement almost equally well, with only one of them corresponding to the
biophysical reality. A comprehensive acquisition shows that the branch choice
varies across the brain. Our framework reveals hidden degeneracies in MRI
parameter estimation for neuronal tissue, provides microstructural and
orientational maps in the whole brain without constraints or priors, and
connects modern biophysical modeling with clinical MRI.Comment: 25 pages, 12 figures, elsarticle two-colum
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
Longitudinal Reproducibility of Neurite Orientation Dispersion and Density Imaging (NODDI) Derived Metrics in the White Matter
Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CVWS), between-subject coefficient of variation (CVBS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing [“VBM-style”], ROI-based analysis). We observed high test–retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CVWS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field
Extraction of Structural Metrics from Crossing Fiber Models
Diffusion MRI (dMRI) measurements allow us to infer the microstructural properties of white matter and to reconstruct fiber pathways in-vivo. High angular diffusion imaging (HARDI) allows for the creation of more and more complex local models connecting the microstructure to the measured signal. One of the challenges is the derivation of meaningful metrics describing the underlying structure from the local models. The aim hereby is to increase the specificity of the widely used metric fractional anisotropy (FA) by using the additional information contained within the HARDI data.
A local model which is connected directly to the underlying microstructure through the model of a single fiber population is spherical deconvolution. It produces a fiber orientation density function (fODF), which can often be interpreted as superposition of multiple peaks, each associated to one relatively coherent fiber population (bundle). Parameterizing these peaks one is able to disentangle and characterize these bundles. In this work, the fODF peaks are approximated by Bingham distributions, capturing first and second order statistics of the fiber orientations, from which metrics for the parametric quantification of fiber bundles are derived. Meaningful relationships between these measures and the underlying microstructural properties are proposed. The focus lies on metrics derived directly from properties of the Bingham distribution, such as peak length, peak direction, peak spread, integral over the peak, as well as a metric derived from the comparison of the largest peaks, which probes the complexity of the underlying microstructure. These metrics are compared to the conventionally used fractional anisotropy (FA) and it is shown how they may help to increase the specificity of the characterization of microstructural properties.
Visualization of the micro-structural arrangement is another application of dMRI. This is done by using tractography to propagate the fiber layout, extracted from the local model, in each voxel. In practice most tractography algorithms use little of the additional information gained from HARDI based local models aside from the reconstructed fiber bundle directions. In this work an approach to tractography based on the Bingham parameterization of the fODF is introduced. For each of the fiber populations present in a voxel the diffusion signal and tensor are computed. Then tensor deflection tractography is performed. This allows incorporating the complete bundle information, performing local interpolation as well as using multiple directions per voxel for generating tracts.
Another aspect of this work is the investigation of the spherical harmonic representation which is used most commonly for the fODF by means of the parameters derived from the Bingham distribution fit. Here a strong connection between the approximation errors in the spherical representation of the Dirac delta function and the distribution of crossing angles recovered from the fODF was discovered.
The final aspect of this work is the application of the metrics derived from the Bingham fit to a number of fetal datasets for quantifying the brain’s development. This is done by introducing the Gini-coefficient as a metric describing the brain’s age
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
-Equivariant Networks for Spherical Deconvolution in Diffusion MRI
We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an
equivariant framework for sparse deconvolution of volumes
where each voxel contains a spherical signal. Such 6D data naturally arises in
diffusion MRI (dMRI), a medical imaging modality widely used to measure
microstructure and structural connectivity. As each dMRI voxel is typically a
mixture of various overlapping structures, there is a need for blind
deconvolution to recover crossing anatomical structures such as white matter
tracts. Existing dMRI work takes either an iterative or deep learning approach
to sparse spherical deconvolution, yet it typically does not account for
relationships between neighboring measurements. This work constructs
equivariant deep learning layers which respect to symmetries of spatial
rotations, reflections, and translations, alongside the symmetries of voxelwise
spherical rotations. As a result, RT-ESD improves on previous work across
several tasks including fiber recovery on the DiSCo dataset,
deconvolution-derived partial volume estimation on real-world \textit{in vivo}
human brain dMRI, and improved downstream reconstruction of fiber tractograms
on the Tractometer dataset. Our implementation is available at
https://github.com/AxelElaldi/e3so3_convComment: Accepted to Medical Imaging with Deep Learning (MIDL) 2023. Code
available at https://github.com/AxelElaldi/e3so3_conv . 19 pages with 6
figure
Quantitative Susceptibility Imaging of Tissue Microstructure Using Ultra-High Field MRI
This thesis has used ultra-high field (UHF) magnetic resonance imaging (MRI) to investigate the fundamental relationships between tissue microstructure and such susceptibility-based contrast parameters as the apparent transverse relaxation rate (R2*), the local Larmor frequency shift (LFS) and quantitative volume magnetic susceptibility (QS). The interaction of magnetic fields with biological tissues results in shifts in the LFS which can be used to distinguish underlying cellular architecture. The LFS is also linked to the relaxation properties of tissues in a gradient echo MRI sequence. Equally relevant, histological analysis has identified iron and myelin as two major sources of the LFS. As a result, computation of LFS and the associated volume magnetic susceptibility from MRI phase data may serve as a significant method for in vivo monitoring of changes in iron and myelin associated with normal, healthy aging, as well as neurological disease processes.
In this research, the cellular level underpinnings of the R2* and LFS signals were examined in a model rat brain system using 9.4 T MRI. The study was carried out using biophysical modeling and correlation with quantitative histology. For the first time, multiple biophysical modeling schemes were compared in both gray and white matter of excised rat brain tissue. Suprisingly, R2* dependence on tissue orientation has not been fully understood. Accordingly, scaling relations were derived for calculating the reversible, mesoscopic magnetic field component, R2\u27, of the apparent transverse relaxation rate from the orientation dependence in gray and white matter. Our results demonstrate that the orientation dependence of R2* and LFS in both white and cortical gray matter has a sinusoidal dependence on tissue orientation and a linear dependence on the volume fraction of myelin in the tissue.
A susceptibility processing pipeline was also developed and applied to the calculation of phase-combined LFS and QS maps. The processing pipeline was subsequently used to monitor myelin and iron changes in multiple sclerosis (MS) patients compared to healthy, age and gender-matched controls. With the use of QS and R2* mapping, evidence of statistically significant increases in iron deposition in sub-cortical gray matter, as well as myelin degeneration along the white matter skeleton, were identified in MS patients. The magnetic susceptibility-based MRI methods were then employed as potential clinical biomarkers for disease severity monitoring of MS. It was demonstrated that the combined use of R2* and QS, obtained from multi-echo gradient echo MRI, could serve as an improved metric for monitoring both gray and white matter changes in early MS
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