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Journal of Multivariate Analysis, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peter T Kim, Dennis M Healy
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Informed Constrained Spherical Deconvolution (iCSD)
Medical Image Analysis, 2015Diffusion-weighted (DW) magnetic resonance imaging (MRI) is a noninvasive imaging method, which can be used to investigate neural tracts in the white matter (WM) of the brain. However, the voxel sizes used in DW-MRI are relatively large, making DW-MRI prone to significant partial volume effects (PVE).
Timo Roine, Ben Jeurissen, Jan Aelterman
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Optimal mollifiers for spherical deconvolution
Inverse Problems, 2015This paper deals with the inversion of the spherical Funk–Radon transform, and, more generally, with the inversion of spherical convolution operators from the point of view of statistical inverse problems. This means we consider discrete data perturbed by white noise and aim at estimators with optimal mean square error for functions out of a Sobolev ...
Ralf Hielscher, Michael Quellmalz
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A New Insight and Improvement on Deconvolution Beamforming in Spherical Harmonics Domain
Applied Acoustics, 2021Abstract The existing deconvolution methods with spherical microphone arrays (SMAs) have not been implemented under a unified mathematical framework. In other words, they utilize different output expressions of spherical harmonics beamforming (SHB), which makes their application and popularization inconvenient.
Zhigang Chu
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NeuroImage, 2010 Spherical deconvolution methods have been applied to diffusion MRI to improve diffusion tensor tractography results in brain regions with multiple fibre crossing.
Paola Scifo +2 more
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Estimation of uncertainty in constrained spherical deconvolution fiber orientations
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008Constrained spherical deconvolution (CSD) is a new reconstruction technique that extracts white matter fiber orientations from diffusion weighted MRI data of the brain. However, since these orientations are estimated from noisy data, they are subject to errors, which propagate during fiber tractography.
Ben Jeurissen +3 more
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Optimized Response Function Estimation for Spherical Deconvolution
2020Constrained spherical deconvolution (CSD) is the most widely used algorithm to estimate fiber orientations for tractography in diffusion-weighted magnetic resonance imaging. CSD models the diffusion-weighted signal as the convolution of a fiber orientation distribution function and a “single fiber response function”, representing the signal profile of ...
Tom Dela Haije, Aasa Feragen
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Auto-calibrating Spherical Deconvolution Based on ODF Sparsity
2013Spherical deconvolution models the diffusion MRI signal as the convolution of a fiber orientation density function (fODF) with a single fiber response. We propose a novel calibration procedure that automatically determines this fiber response. This has three advantages: First, the user no longer needs to provide an estimate of the response.
Thomas Schultz 0001, Samuel Groeschel
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Simultaneous Spherical Divergence Correction and Optima Deconvolution
IEEE Transactions on Geoscience and Remote Sensing, 1980The effects of spherical divergence on reflection seismograms destroys the stationarity assumption implicit in Weiner smoothing by distorting the amplitudes of the reflection coefficient sequence. The traditional correction for spherical divergence consists of an immediate scaling of the observed signal by an expected attenuation factor.
John J. Kormylo, Jerry M. Mendel
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Red nucleus connectivity as revealed by constrained spherical deconvolution tractography
Neuroscience Letters, 2016Previous Diffusion Tensor Imaging studies have demonstrated that the human red nucleus is widely interconnected with sensory-motor and prefrontal cortices. In this study, we assessed red nucleus connectivity by using a multi-tensor model called non- negative Constrained Spherical Deconvolution (CSD), which is able to resolve more than one fiber ...
MILARDI, Demetrio +10 more
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