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Diffusion tensor distribution imaging

Topgaard, Daniel LU (2019) In NMR in Biomedicine 32(5).
Abstract

Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with “size,” “shape,” and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a... (More)

Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with “size,” “shape,” and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
b-tensor, microscopic diffusion anisotropy, modulated gradient, multidimensional diffusion MRI, orientation dispersion, q-trajectory
in
NMR in Biomedicine
volume
32
issue
5
publisher
John Wiley & Sons
external identifiers
  • scopus:85061293236
ISSN
0952-3480
DOI
10.1002/nbm.4066
language
English
LU publication?
yes
id
4536d30d-2b49-4e5c-bfc9-7dd969175ac4
date added to LUP
2019-02-20 08:56:36
date last changed
2019-10-15 06:58:57
@article{4536d30d-2b49-4e5c-bfc9-7dd969175ac4,
  abstract     = {<p>Conventional diffusion MRI yields voxel-averaged parameters that suffer from ambiguities for heterogeneous anisotropic materials such as brain tissue. Using principles from solid-state NMR spectroscopy, we have previously introduced the shape of the diffusion encoding tensor as a separate acquisition dimension that disentangles isotropic and anisotropic contributions to the observed diffusivities, thereby allowing for unconstrained data inversion into diffusion tensor distributions with “size,” “shape,” and orientation dimensions. Here we combine our recent non-parametric data inversion algorithm and data acquisition protocol with an imaging pulse sequence to demonstrate spatial mapping of diffusion tensor distributions using a previously developed composite phantom with multiple isotropic and anisotropic components. We propose a compact format for visualizing two-dimensional arrays of the distributions, new scalar parameters quantifying intra-voxel heterogeneity, and a binning procedure giving maps of all relevant parameters for each of the components resolved in the multidimensional distribution space.</p>},
  articleno    = {e4066},
  author       = {Topgaard, Daniel},
  issn         = {0952-3480},
  keyword      = {b-tensor,microscopic diffusion anisotropy,modulated gradient,multidimensional diffusion MRI,orientation dispersion,q-trajectory},
  language     = {eng},
  month        = {02},
  number       = {5},
  publisher    = {John Wiley & Sons},
  series       = {NMR in Biomedicine},
  title        = {Diffusion tensor distribution imaging},
  url          = {http://dx.doi.org/10.1002/nbm.4066},
  volume       = {32},
  year         = {2019},
}