86 research outputs found
Design of Anisotropic Diffusion Hardware Fiber Phantoms
A gold standard for the validation of diffusion weighted magnetic resonance imaging (DW-MRI) in brain white matter (WM) is essential for clinical purposes but still not available. Synthetic anisotropic fiber bundles are proposed as phantoms for the validation of DW-MRI because of their well-known structure, their long preservability and the possibility
to create complex geometries such as curved and fiber crossings. A crucial question is how the different material properties and size of the fiber phantoms influence the outcome of the DW-MRI experiment. Several fiber materials are compared in this study. The effect of surface
relaxation and internal gradients on the SNR is evaluated. In addition, the dependency of the fiber density and fiber radius on the diffusion properties is investigated
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
Universal Sampling Denoising (USD) for noise mapping and noise removal of non-Cartesian MRI
Random matrix theory (RMT) combined with principal component analysis has
resulted in a widely used MPPCA noise mapping and denoising algorithm, that
utilizes the redundancy in multiple acquisitions and in local image patches.
RMT-based denoising relies on the uncorrelated identically distributed noise.
This assumption breaks down after regridding of non-Cartesian sampling. Here we
propose a Universal Sampling Denoising (USD) pipeline to homogenize the noise
level and decorrelate the noise in non-Cartesian sampled k-space data after
resampling to a Cartesian grid. In this way, the RMT approaches become
applicable to MRI of any non-Cartesian k-space sampling. We demonstrate the
denoising pipeline on MRI data acquired using radial trajectories, including
diffusion MRI of a numerical phantom and ex vivo mouse brains, as well as in
vivo MRI of a healthy subject. The proposed pipeline robustly estimates
noise level, performs noise removal, and corrects bias in parametric maps, such
as diffusivity and kurtosis metrics, and relaxation time. USD stabilizes
the variance, decorrelates the noise, and thereby enables the application of
RMT-based denoising approaches to MR images reconstructed from any
non-Cartesian data. In addition to MRI, USD may also apply to other medical
imaging techniques involving non-Cartesian acquisition, such as PET, CT, and
SPECT
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