A new framework for MR diffusion tensor distribution

Abstract The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine i...

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Autores principales:	Kulam Najmudeen Magdoom, Sinisa Pajevic, Gasbarra Dario, Peter J. Basser
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3f907c6d7805406f94c5c2172debdb4a
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spelling	oai:doaj.org-article:3f907c6d7805406f94c5c2172debdb4a2021-12-02T14:06:57ZA new framework for MR diffusion tensor distribution10.1038/s41598-021-81264-x2045-2322https://doaj.org/article/3f907c6d7805406f94c5c2172debdb4a2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81264-xhttps://doaj.org/toc/2045-2322Abstract The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.Kulam Najmudeen MagdoomSinisa PajevicGasbarra DarioPeter J. BasserNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kulam Najmudeen Magdoom Sinisa Pajevic Gasbarra Dario Peter J. Basser A new framework for MR diffusion tensor distribution
description	Abstract The ability to characterize heterogeneous and anisotropic water diffusion processes within macroscopic MRI voxels non-invasively and in vivo is a desideratum in biology, neuroscience, and medicine. While an MRI voxel may contain approximately a microliter of tissue, our goal is to examine intravoxel diffusion processes on the order of picoliters. Here we propose a new theoretical framework and efficient experimental design to describe and measure such intravoxel structural heterogeneity and anisotropy. We assume that a constrained normal tensor-variate distribution (CNTVD) describes the variability of positive definite diffusion tensors within a voxel which extends its applicability to a wide range of b-values while preserving the richness of diffusion tensor distribution (DTD) paradigm unlike existing models. We introduce a new Monte Carlo (MC) scheme to synthesize realistic 6D DTD numerical phantoms and invert the MR signal. We show that the signal inversion is well-posed and estimate the CNTVD parameters parsimoniously by exploiting the different symmetries of the mean and covariance tensors of CNTVD. The robustness of the estimation pipeline is assessed by adding noise to calculated MR signals and compared with the ground truth. A family of invariant parameters and glyphs which characterize microscopic shape, size and orientation heterogeneity within a voxel are also presented.
format	article
author	Kulam Najmudeen Magdoom Sinisa Pajevic Gasbarra Dario Peter J. Basser
author_facet	Kulam Najmudeen Magdoom Sinisa Pajevic Gasbarra Dario Peter J. Basser
author_sort	Kulam Najmudeen Magdoom
title	A new framework for MR diffusion tensor distribution
title_short	A new framework for MR diffusion tensor distribution
title_full	A new framework for MR diffusion tensor distribution
title_fullStr	A new framework for MR diffusion tensor distribution
title_full_unstemmed	A new framework for MR diffusion tensor distribution
title_sort	new framework for mr diffusion tensor distribution
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3f907c6d7805406f94c5c2172debdb4a
work_keys_str_mv	AT kulamnajmudeenmagdoom anewframeworkformrdiffusiontensordistribution AT sinisapajevic anewframeworkformrdiffusiontensordistribution AT gasbarradario anewframeworkformrdiffusiontensordistribution AT peterjbasser anewframeworkformrdiffusiontensordistribution AT kulamnajmudeenmagdoom newframeworkformrdiffusiontensordistribution AT sinisapajevic newframeworkformrdiffusiontensordistribution AT gasbarradario newframeworkformrdiffusiontensordistribution AT peterjbasser newframeworkformrdiffusiontensordistribution
_version_	1718391957407924224

A new framework for MR diffusion tensor distribution

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