178 research outputs found
Reducing variability in along-tract analysis with diffusion profile realignment
Diffusion weighted MRI (dMRI) provides a non invasive virtual reconstruction
of the brain's white matter structures through tractography. Analyzing dMRI
measures along the trajectory of white matter bundles can provide a more
specific investigation than considering a region of interest or tract-averaged
measurements. However, performing group analyses with this along-tract strategy
requires correspondence between points of tract pathways across subjects. This
is usually achieved by creating a new common space where the representative
streamlines from every subject are resampled to the same number of points. If
the underlying anatomy of some subjects was altered due to, e.g. disease or
developmental changes, such information might be lost by resampling to a fixed
number of points. In this work, we propose to address the issue of possible
misalignment, which might be present even after resampling, by realigning the
representative streamline of each subject in this 1D space with a new method,
coined diffusion profile realignment (DPR). Experiments on synthetic datasets
show that DPR reduces the coefficient of variation for the mean diffusivity,
fractional anisotropy and apparent fiber density when compared to the unaligned
case. Using 100 in vivo datasets from the HCP, we simulated changes in mean
diffusivity, fractional anisotropy and apparent fiber density. Pairwise
Student's t-tests between these altered subjects and the original subjects
indicate that regional changes are identified after realignment with the DPR
algorithm, while preserving differences previously detected in the unaligned
case. This new correction strategy contributes to revealing effects of interest
which might be hidden by misalignment and has the potential to improve the
specificity in longitudinal population studies beyond the traditional region of
interest based analysis and along-tract analysis workflows.Comment: v4: peer-reviewed round 2 v3 : deleted some old text from before
peer-review which was mistakenly included v2 : peer-reviewed version v1:
preprint as submitted to journal NeuroImag
Harmonization of diffusion MRI datasets with adaptive dictionary learning
Diffusion magnetic resonance imaging is a noninvasive imaging technique that
can indirectly infer the microstructure of tissues and provide metrics which
are subject to normal variability across subjects. Potentially abnormal values
or features may yield essential information to support analysis of controls and
patients cohorts, but subtle confounds affecting diffusion MRI, such as those
due to difference in scanning protocols or hardware, can lead to systematic
errors which could be mistaken for purely biologically driven variations
amongst subjects. In this work, we propose a new harmonization algorithm based
on adaptive dictionary learning to mitigate the unwanted variability caused by
different scanner hardware while preserving the natural biological variability
present in the data. Overcomplete dictionaries, which are learned automatically
from the data and do not require paired samples, are then used to reconstruct
the data from a different scanner, removing variability present in the source
scanner in the process. We use the publicly available database from an
international challenge to evaluate the method, which was acquired on three
different scanners and with two different protocols, and propose a new mapping
towards a scanner-agnostic space. Results show that the effect size of the four
studied diffusion metrics is preserved while removing variability attributable
to the scanner. Experiments with alterations using a free water compartment,
which is not simulated in the training data, shows that the effect size induced
by the alterations is also preserved after harmonization. The algorithm is
freely available and could help multicenter studies in pooling their data,
while removing scanner specific confounds, and increase statistical power in
the process.Comment: v5 Peer review for Human Brain Mapping v4: Peer review round 2 v3:
Peer reviewed version v2: Fix minor text issue + add supp materials v1: To be
submitted to Neuroimag
Generalized Richardson-Lucy (GRL) for analyzing multi-shell diffusion MRI data
Spherical deconvolution is a widely used approach to quantify fiber
orientation distribution from diffusion MRI data. The damped Richardson-Lucy
(dRL) is developed to perform robust spherical deconvolution on single shell
diffusion MRI data. While the dRL algorithm could in theory be directly applied
to multi-shell data, it is not optimised to model the signal from multiple
tissue types. In this work, we introduce a new framework based on dRL - dubbed
Generalized Richardson Lucy (GRL) - that uses multi-shell data in combination
with user-chosen tissue models to disentangle partial volume effects and
increase the accuracy in FOD estimation. The optimal weighting of multi-shell
data in the fit and the robustness to noise and partial volume effects of GRL
was studied with synthetic data. Subsequently, we investigated the performances
of GRL in comparison to dRL on a high-resolution diffusion MRI dataset from the
Human Connectome Project and on an MRI dataset acquired at 3T on a clinical
scanner. The feasibility of including intra-voxel incoherent motion (IVIM)
effects in the modelling was studied on a third dataset. Results of simulations
show that GRL can robustly disentangle different tissue types at SNR above 20
and improves the angular accuracy of the FOD estimation. On real data, GRL
provides signal fraction maps that are physiologically plausible and consistent
between datasets. When considering IVIM effects, high blood pseudo-diffusion
fraction is observed in the medial temporal lobe and in the sagittal sinus. In
comparison to dRL, GRL provides sharper FODs and less spurious peaks in
presence of partial volume effects and results in a better tract termination at
the grey/white matter interface or at the outer cortical surface. In
conclusion, GRL offers a new modular and flexible framework to perform
spherical deconvolution of multi-shell data
Automated characterization of noise distributions in diffusion MRI data
Knowledge of the noise distribution in diffusion MRI is the centerpiece to
quantify uncertainties arising from the acquisition process. Accurate
estimation beyond textbook distributions often requires information about the
acquisition process, which is usually not available. We introduce two new
automated methods using the moments and maximum likelihood equations of the
Gamma distribution to estimate all unknown parameters using only the magnitude
data. A rejection step is used to make the framework automatic and robust to
artifacts. Simulations were created for two diffusion weightings with parallel
imaging. Furthermore, MRI data of a water phantom with different combinations
of parallel imaging were acquired. Finally, experiments on freely available
datasets are used to assess reproducibility when limited information about the
acquisition protocol is available. Additionally, we demonstrated the
applicability of the proposed methods for a bias correction and denoising task
on an in vivo dataset. A generalized version of the bias correction framework
for non integer degrees of freedom is also introduced. The proposed framework
is compared with three other algorithms with datasets from three vendors,
employing different reconstruction methods. Simulations showed that assuming a
Rician distribution can lead to misestimation of the noise distribution in
parallel imaging. Results showed that signal leakage in multiband can also lead
to a misestimation of the noise distribution. Repeated acquisitions of in vivo
datasets show that the estimated parameters are stable and have lower
variability than compared methods. Results show that the proposed methods
reduce the appearance of noise at high b-value. The proposed algorithms herein
can estimate both parameters of the noise distribution automatically, are
robust to signal leakage artifacts and perform best when used on acquired noise
maps.Comment: v3: Peer reviewed version v2: Manuscript as submitted to Medical
image analysis v1: Manuscript as submitted to Magnetic resonance in medicin
The adverse effect of gradient nonlinearities on diffusion MRI: From voxels to group studies
Nonlinearities of gradient magnetic fields in diffusion MRI (dMRI) can introduce systematic errors in estimates of diffusion measures. While there are correction methods that can compensate for these errors, as presented in the Human Connectome Project, such nonlinear effects are often assumed to be negligible for typical applications, and as a result, gradient nonlinearities are mostly left uncorrected. In this work, we perform a systematic analysis to investigate the effect of gradient nonlinearities on dMRI studies, from voxel-wise estimates to group study outcomes. We present a novel framework to quantify and visualize these effects by decomposing them into their magnitude and angle components. Mean magnitude deviation and fractional gradient anisotropy are introduced to quantify the distortions in the size and shape of gradient vector distributions. By means of Monte-Carlo simulations and real data from the Human Connectome Project, the errors on dMRI measures derived from the diffusion tensor imaging and diffusional kurtosis imaging are highlighted. We perform a group study to showcase the alteration in the significance and effect size due to ignoring gradient nonlinearity correction. Our results indicate that the effect of gradient field nonlinearities on dMRI studies can be significant and may complicate the interpretation of the results and conclusions
The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion MRI data
Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson‐Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b‐matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b‐values in contrast to the perhaps common assumption that only high b‐value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable
Data quality in diffusion tensor imaging studies of the preterm brain: a systematic review
Background: To study early neurodevelopment in preterm infants, evaluation of brain maturation and injury is increasingly performed using diffusion tensor imaging, for which the reliability of underlying data is paramount. Objective: To review the literature to eva
Fiber orientation distribution from diffusion MRI: Effects of inaccurate response function calibration
Background and Purpose
Diffusion MRI of the brain enables to quantify white matter fiber orientations noninvasively. Several approaches have been proposed to estimate such characteristics from diffusion MRI data with spherical deconvolution being one of the most widely used methods. Spherical deconvolution requires to define––or derive from the data––a response function, which is used to compute the fiber orientation distribution (FOD). Different characteristics of the response function are expected to affect the FOD computation and the subsequent fiber tracking.
Methods
In this work, we explored the effects of inaccuracies in the shape factors of the response function on the FOD characteristics.
Results
With simulations, we show that the apparent fiber density could be doubled in the presence of underestimated shape factors in the response functions, whereas the overestimation of the shape factor will cause more spurious peaks in the FOD, especially when the signal-to-noise ratio is below 15. Moreover, crossing fiber populations with a separation angle smaller than 60° were more sensitive to inaccuracies in the response function than fiber populations with more orthogonal separation angles. Results with in vivo data demonstrate angular deviations in the FODs and spurious peaks as a result of modified shape factors of the response function, while the reconstruction of the main parts of fiber bundles is well preserved.
Conclusions
This work sheds light on how specific aspects of the response function shape can affect the estimated FODs, and highlights the importance of a proper calibration/definition of the response function
Proton Driven Plasma Wakefield Acceleration
Plasma wakefield acceleration, either laser driven or electron-bunch driven,
has been demonstrated to hold great potential. However, it is not obvious how
to scale these approaches to bring particles up to the TeV regime. In this
paper, we discuss the possibility of proton-bunch driven plasma wakefield
acceleration, and show that high energy electron beams could potentially be
produced in a single accelerating stage.Comment: 13 pages, 4 figure
The effect of gradient nonlinearities on fiber orientation estimates from spherical deconvolution of diffusion magnetic resonance imaging data
Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson-Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b-matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b-values in contrast to the perhaps common assumption that only high b-value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable
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