Multi-tissue spherical deconvolution of tensor-valued diffusion MRI

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 data. In this work, we generalize MT-CSD to ten...

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Autores principales:	Ben Jeurissen, Filip Szczepankiewicz
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Magnetic resonance imaging Tensor-valued diffusion encoding Multidimensional diffusion encoding B-tensors Multi-tissue constrained spherical deconvolution Tractography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/9971a1abe0154413acf262aad9e38870
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spelling	oai:doaj.org-article:9971a1abe0154413acf262aad9e388702021-11-18T04:44:58ZMulti-tissue spherical deconvolution of tensor-valued diffusion MRI1095-957210.1016/j.neuroimage.2021.118717https://doaj.org/article/9971a1abe0154413acf262aad9e388702021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009897https://doaj.org/toc/1095-9572Multi-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 data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.Ben JeurissenFilip SzczepankiewiczElsevierarticleMagnetic resonance imagingTensor-valued diffusion encodingMultidimensional diffusion encodingB-tensorsMulti-tissue constrained spherical deconvolutionTractographyNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118717- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Magnetic resonance imaging Tensor-valued diffusion encoding Multidimensional diffusion encoding B-tensors Multi-tissue constrained spherical deconvolution Tractography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Magnetic resonance imaging Tensor-valued diffusion encoding Multidimensional diffusion encoding B-tensors Multi-tissue constrained spherical deconvolution Tractography Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Ben Jeurissen Filip Szczepankiewicz Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
description	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 data. In this work, we generalize MT-CSD to tensor-valued diffusion encoding with arbitrary b-tensor shapes. This enables the use of data encoded with mixed b-tensors, rather than being limited to the subset of linear (conventional) b-tensors. Using the complete set of data, including all b-tensor shapes, provides a categorical improvement in the estimation of apparent tissue densities, fiber ODF, and resulting tractography. Furthermore, we demonstrate that including multiple b-tensor shapes in the analysis provides improved contrast between tissue types, in particular between gray matter and white matter. We also show that our approach provides high-quality apparent tissue density maps and high-quality fiber tracking from data, even with sparse sampling across b-tensors that yield whole-brain coverage at 2 mm isotropic resolution in approximately 5:15 min.
format	article
author	Ben Jeurissen Filip Szczepankiewicz
author_facet	Ben Jeurissen Filip Szczepankiewicz
author_sort	Ben Jeurissen
title	Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
title_short	Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
title_full	Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
title_fullStr	Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
title_full_unstemmed	Multi-tissue spherical deconvolution of tensor-valued diffusion MRI
title_sort	multi-tissue spherical deconvolution of tensor-valued diffusion mri
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/9971a1abe0154413acf262aad9e38870
work_keys_str_mv	AT benjeurissen multitissuesphericaldeconvolutionoftensorvalueddiffusionmri AT filipszczepankiewicz multitissuesphericaldeconvolutionoftensorvalueddiffusionmri
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Multi-tissue spherical deconvolution of tensor-valued diffusion MRI

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