Constrained Spherical Deconvolution of Nonspherically Sampled Diffusion MRI Data [PDF]
Human Brain Mapping, 2021 AbstractConstrained spherical deconvolution (CSD) of diffusion‐weighted MRI (DW‐MRI) is a popular analysis method that extracts the full white matter (WM) fiber orientation density function (fODF) in the living human brain, noninvasively. It assumes that the DW‐MRI signal on the sphere can be represented as the spherical convolution of a single‐fiber ...
Jan Morez, Ben Jeurissen, Jan Sijbers
exaly +6 more sources
Randomized iterative spherical‐deconvolution informed tractogram filtering
NeuroImage, 2023 Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of ...
Antonia Hain +2 more
doaj +3 more sources
Spherical Deconvolution Improves Quality of Single Particle Reconstruction [PDF]
Journal of Structural Biology, 2014 One single-particle reconstruction technique is the reconstruction of macromolecules from projection images of randomly oriented particles (SPRR). In SPRR the reliability and consequent interpretation of the final reconstruction is affected by errors arising from incorrect assignment of projection angles to individual particles. In order to improve the
Kishchenko GP, Leith A.
exaly +5 more sources
Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
NeuroImage, 2021 Multi-tissue constrained spherical deconvolution (MT-CSD) leverages the characteristic b-value dependency of each tissue type to estimate both the apparent tissue densities and the white matter fiber orientation distribution function from diffusion MRI ...
Ben Jeurissen, Filip Szczepankiewicz
doaj +4 more sources
Near-Field Spherical Scanning Antenna Measurements: Probe Deconvolution and Sensitivity [PDF]
International Journal of Antennas and Propagation, 2014 We define and calculate sensitivity for several actual and simulated probes. Probe sensitivity can have a significant impact on the measurement uncertainty associated with probe deconvolution in near-field spherical-scanning, antenna measurements.
Ronald C. Wittmann, Michael H. Francis
doaj +2 more sources
The dictionary approach for spherical deconvolution
Journal of Multivariate Analysis, 2013 We consider the problem of estimating a density of probability from indirect data in the spherical convolution model. We aim at building an estimate of the unknown density as a linear combination of functions of an overcomplete dictionary. The procedure is devised through a well-calibrated @?"1-penalized criterion.
Pham Ngoc, Thanh Mai, Rivoirard, Vincent
core +6 more sources
Constrained spherical deconvolution on signal and ODF values
, 2013 International audienceFor the purpose of the ISBI HARDI reconstruction challenge 2013 and for the categories DTI and HARDI acquisitions, we reconstructed the diffusion datasets using two well established methods: a) Spherical Deconvolution Transform (SDT)
Garyfallidis, Eleftherios +4 more
core +4 more sources
Improving Fiber Alignment in HARDI by Combining Contextual PDE Flow with Constrained Spherical Deconvolution. [PDF]
PLoS One, 2015 We propose two strategies to improve the quality of tractography results computed from diffusion weighted magnetic resonance imaging (DW-MRI) data.
Portegies JM +5 more
europepmc +2 more sources
Probabilistic MRI tractography of the optic radiation using constrained spherical deconvolution: a feasibility study. [PDF]
PLoS One, 2015 Imaging the optic radiation (OR) is of considerable interest in studying diseases affecting the visual pathway and for pre-surgical planning of temporal lobe resections.
Lim JC +7 more
europepmc +2 more sources
Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization. [PDF]
PLoS One, 2015 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 ...
Canales-Rodríguez EJ +13 more
europepmc +2 more sources