3,866 research outputs found
Model-Free, Regularized, Fast, and Robust Analytical Orientation Distribution Function Estimation
International audienceHigh Angular Resolution Imaging (HARDI) can better explore the complex micro-structure of white matter compared to Diffusion Tensor Imaging (DTI). Orientation Distribution Function (ODF) in HARDI is used to describe the probability of the fiber direction. There are two type definitions of the ODF, which were respectively proposed in Q-Ball Imaging (QBI) and Diffusion Spectrum Imaging (DSI). Some analytical reconstructions methods have been proposed to estimate these two type of ODFs from single shell HARDI data. However they all have some assumptions and intrinsic modeling errors. In this article, we propose, almost without any assumption, a uniform analytical method to estimate these two ODFs from DWI signals in q space, which is based on Spherical Polar Fourier Expression (SPFE) of signals. The solution is analytical and is a linear transformation from the q-space signal to the ODF represented by Spherical Harmonics (SH). It can naturally combines the DWI signals in dierent Q-shells. Moreover It can avoid the intrinsic Funk-Radon Transform (FRT) blurring error in QBI and it does not need any assumption of the signals, such as the multiple tensor model and mono/multi-exponential decay. We validate our method using synthetic data, phantom data and real data. Our method works well in all experiments, especially for the data with low SNR, low anisotropy and non-exponential decay
Spherical deconvolution of multichannel diffusion MRI data with non-Gaussian noise models and spatial regularization
Spherical deconvolution (SD) methods are widely used to estimate the
intra-voxel white-matter fiber orientations from diffusion MRI data. However,
while some of these methods assume a zero-mean Gaussian distribution for the
underlying noise, its real distribution is known to be non-Gaussian and to
depend on the methodology used to combine multichannel signals. Indeed, the two
prevailing methods for multichannel signal combination lead to Rician and
noncentral Chi noise distributions. Here we develop a Robust and Unbiased
Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with
realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to
Rician and noncentral Chi likelihood models. To quantify the benefits of using
proper noise models, RUMBA-SD was compared with dRL-SD, a well-established
method based on the RL algorithm for Gaussian noise. Another aim of the study
was to quantify the impact of including a total variation (TV) spatial
regularization term in the estimation framework. To do this, we developed TV
spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The
evaluation was performed by comparing various quality metrics on 132
three-dimensional synthetic phantoms involving different inter-fiber angles and
volume fractions, which were contaminated with noise mimicking patterns
generated by data processing in multichannel scanners. The results demonstrate
that the inclusion of proper likelihood models leads to an increased ability to
resolve fiber crossings with smaller inter-fiber angles and to better detect
non-dominant fibers. The inclusion of TV regularization dramatically improved
the resolution power of both techniques. The above findings were also verified
in brain data
Bayesian uncertainty quantification in linear models for diffusion MRI
Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue
microstructure. By fitting a model to the dMRI signal it is possible to derive
various quantitative features. Several of the most popular dMRI signal models
are expansions in an appropriately chosen basis, where the coefficients are
determined using some variation of least-squares. However, such approaches lack
any notion of uncertainty, which could be valuable in e.g. group analyses. In
this work, we use a probabilistic interpretation of linear least-squares
methods to recast popular dMRI models as Bayesian ones. This makes it possible
to quantify the uncertainty of any derived quantity. In particular, for
quantities that are affine functions of the coefficients, the posterior
distribution can be expressed in closed-form. We simulated measurements from
single- and double-tensor models where the correct values of several quantities
are known, to validate that the theoretically derived quantiles agree with
those observed empirically. We included results from residual bootstrap for
comparison and found good agreement. The validation employed several different
models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI)
and Constrained Spherical Deconvolution (CSD). We also used in vivo data to
visualize maps of quantitative features and corresponding uncertainties, and to
show how our approach can be used in a group analysis to downweight subjects
with high uncertainty. In summary, we convert successful linear models for dMRI
signal estimation to probabilistic models, capable of accurate uncertainty
quantification.Comment: Added results from a group analysis and a comparison with residual
bootstra
NODDI-SH: a computational efficient NODDI extension for fODF estimation in diffusion MRI
Diffusion Magnetic Resonance Imaging (DMRI) is the only non-invasive imaging
technique which is able to detect the principal directions of water diffusion
as well as neurites density in the human brain. Exploiting the ability of
Spherical Harmonics (SH) to model spherical functions, we propose a new
reconstruction model for DMRI data which is able to estimate both the fiber
Orientation Distribution Function (fODF) and the relative volume fractions of
the neurites in each voxel, which is robust to multiple fiber crossings. We
consider a Neurite Orientation Dispersion and Density Imaging (NODDI) inspired
single fiber diffusion signal to be derived from three compartments:
intracellular, extracellular, and cerebrospinal fluid. The model, called
NODDI-SH, is derived by convolving the single fiber response with the fODF in
each voxel. NODDI-SH embeds the calculation of the fODF and the neurite density
in a unified mathematical model providing efficient, robust and accurate
results. Results were validated on simulated data and tested on
\textit{in-vivo} data of human brain, and compared to and Constrained Spherical
Deconvolution (CSD) for benchmarking. Results revealed competitive performance
in all respects and inherent adaptivity to local microstructure, while sensibly
reducing the computational cost. We also investigated NODDI-SH performance when
only a limited number of samples are available for the fitting, demonstrating
that 60 samples are enough to obtain reliable results. The fast computational
time and the low number of signal samples required, make NODDI-SH feasible for
clinical application
Increasing the Analytical Accessibility of Multishell and Diffusion Spectrum Imaging Data Using Generalized Q-Sampling Conversion
Many diffusion MRI researchers, including the Human Connectome Project (HCP),
acquire data using multishell (e.g., WU-Minn consortium) and diffusion spectrum
imaging (DSI) schemes (e.g., USC-Harvard consortium). However, these data sets
are not readily accessible to high angular resolution diffusion imaging (HARDI)
analysis methods that are popular in connectomics analysis. Here we introduce a
scheme conversion approach that transforms multishell and DSI data into their
corresponding HARDI representations, thereby empowering HARDI-based analytical
methods to make use of data acquired using non-HARDI approaches. This method
was evaluated on both phantom and in-vivo human data sets by acquiring
multishell, DSI, and HARDI data simultaneously, and comparing the converted
HARDI, from non-HARDI methods, with the original HARDI data. Analysis on the
phantom shows that the converted HARDI from DSI and multishell data strongly
predicts the original HARDI (correlation coefficient > 0.9). Our in-vivo study
shows that the converted HARDI can be reconstructed by constrained spherical
deconvolution, and the fiber orientation distributions are consistent with
those from the original HARDI. We further illustrate that our scheme conversion
method can be applied to HCP data, and the converted HARDI do not appear to
sacrifice angular resolution. Thus this novel approach can benefit all
HARDI-based analysis approaches, allowing greater analytical accessibility to
non-HARDI data, including data from the HCP
Fast Fiber Orientation Estimation in Diffusion MRI from kq-Space Sampling and Anatomical Priors
High spatio-angular resolution diffusion MRI (dMRI) has been shown to provide
accurate identification of complex fiber configurations, albeit at the cost of
long acquisition times. We propose a method to recover intra-voxel fiber
configurations at high spatio-angular resolution relying on a kq-space
under-sampling scheme to enable accelerated acquisitions. The inverse problem
for reconstruction of the fiber orientation distribution (FOD) is regularized
by a structured sparsity prior promoting simultaneously voxelwise sparsity and
spatial smoothness of fiber orientation. Prior knowledge of the spatial
distribution of white matter, gray matter and cerebrospinal fluid is also
assumed. A minimization problem is formulated and solved via a forward-backward
convex optimization algorithmic structure. Simulations and real data analysis
suggest that accurate FOD mapping can be achieved from severe kq-space
under-sampling regimes, potentially enabling high spatio-angular dMRI in the
clinical setting.Comment: 10 pages, 5 figures, Supplementary Material
Diffusion Maps Clustering for Magnetic Resonance Q-Ball Imaging Segmentation
International audienceWhite matter fiber clustering aims to get insight about anatomical structures in order to generate atlases, perform clear visualizations, and compute statistics across subjects, all important and current neuroimaging problems. In this work, we present a diffusion maps clustering method applied to diffusion MRI in order to segment complex white matter fiber bundles. It is well known that diffusion tensor imaging (DTI) is restricted in complex fiber regions with crossings and this is why recent high-angular resolution diffusion imaging (HARDI) such as Q-Ball imaging (QBI) has been introduced to overcome these limitations. QBI reconstructs the diffusion orientation distribution function (ODF), a spherical function that has its maxima agreeing with the underlying fiber populations. In this paper, we use a spherical harmonic ODF representation as input to the diffusion maps clustering method.We first show the advantage of using diffusion maps clustering over classical methods such as N-Cuts and Laplacian eigenmaps. In particular, our ODF diffusion maps requires a smaller number of hypothesis from the input data, reduces the number of artifacts in the segmentation, and automatically exhibits the number of clusters segmenting the Q-Ball image by using an adaptive scalespace parameter.We also show that our ODF diffusion maps clustering can reproduce published results using the diffusion tensor (DT) clustering with N-Cuts on simple synthetic images without crossings. On more complex data with crossings, we show that our ODF-based method succeeds to separate fiber bundles and crossing regions whereas the DT-based methods generate artifacts and exhibit wrong number of clusters. Finally, we show results on a real-brain dataset where we segment well-known fiber bundles
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