Results 31 to 40 of about 343,323 (262)
NeuroImage, 2020 In diffusion MRI, spherical deconvolution approaches can estimate local white matter (WM) fiber orientation distributions (FOD) which can be used to produce fiber tractography reconstructions.
Alberto De Luca +3 more
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Sparse solution of fiber orientation distribution function by diffusion decomposition. [PDF]
PLoS ONE, 2013 Fiber orientation is the key information in diffusion tractography. Several deconvolution methods have been proposed to obtain fiber orientations by estimating a fiber orientation distribution function (ODF).
Fang-Cheng Yeh, Wen-Yih Isaac Tseng
doaj +1 more source
Modelling white matter with spherical deconvolution: How and why? [PDF]
NMR Biomed, 2019 Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain.
Dell'Acqua F, Tournier JD.
europepmc +2 more sources
Artificial intelligence in corticospinal tract segmentation using constrained spherical deconvolution. [PDF]
Surg Neurol IntBackground: Tractography of cerebral white matter tracts is a technique with applications in neurosurgical planning and the diagnosis of neurological diseases. In this context, the approach based on the constrained spherical deconvolution (CSD) algorithm allows for more efficient and plausible segmentations.
Freitas ELA +1 more
europepmc +3 more sources
A Fiber Tracking Algorithm Based on Non-Local Constrained Spherical Deconvolution
Chinese Journal of Magnetic Resonance, 2020 Fiber tracking with diffusion magnetic resonance imaging provides a powerful tool for non-invasive observation of white matter in the brain. Constrained spherical deconvolution (CSD) is a multi-fiber tracking model, which can model the orientation of ...
YUE Qing, WANG Yuan-jun
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A Reversible Jump MCMC in Bayesian Blind Deconvolution With a Spherical Prior
IEEE Signal Processing Letters, 2022 Blind deconvolution (BD) is the recovery of a scene of interest convolved with an unknown impulse response function. Within a Bayesian context, prior distributions are assigned to both unknowns to regularize the BD problem. A common approach is to use Gaussian priors. Nonetheless, the latter do not alleviate the so-called scale ambiguity that prevents
Benjamin Traullé +2 more
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$E(3) \times SO(3)$-Equivariant Networks for Spherical Deconvolution in Diffusion MRI [PDF]
, 2023 We present Roto-Translation Equivariant Spherical Deconvolution (RT-ESD), an $E(3)\times SO(3)$ equivariant framework for sparse deconvolution of volumes where each voxel contains a spherical signal. Such 6D data naturally arises in diffusion MRI (dMRI),
Elaldi, Axel, Dey, Neel, Gerig, Guido
core +1 more source
Optimal Spherical Deconvolution
Journal of Multivariate Analysis, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Kim, Peter T., Koo, Ja-Yong
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Fiber Continuity Based Spherical Deconvolution in Spherical Harmonic Domain [PDF]
, 2013 The reliable estimation of fiber orientation distributions from diffusion-sensitized magnetic resonance imaging is an important processing step for revealing structural connectivity in the human brain. Fiber orientation distributions are usually represented in spherical harmonic (SH) domain, but many modern spatial regularization techniques were not ...
Reisert, Marco, Skibbe, Henrik
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Localized spherical deconvolution
The Annals of Statistics, 2011 Published in at http://dx.doi.org/10.1214/10-AOS858 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
Kerkyacharian, Gérard +2 more
openaire +4 more sources