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Nonparametric distributions of tensor-valued Lorentzian diffusion spectra for model-free data inversion in multidimensional diffusion MRI

Narvaez, Omar ; Yon, Maxime LU ; Jiang, Hong LU ; Bernin, Diana LU ; Forssell-Aronsson, Eva ; Sierra, Alejandra and Topgaard, Daniel LU (2024) In Journal of Chemical Physics 161(8).
Abstract

Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or “D(ω) distributions,” as a general representation with sufficient flexibility... (More)

Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or “D(ω) distributions,” as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Chemical Physics
volume
161
issue
8
article number
084201
publisher
American Institute of Physics (AIP)
external identifiers
  • scopus:85201999839
  • pmid:39171708
ISSN
0021-9606
DOI
10.1063/5.0213252
language
English
LU publication?
yes
id
4d90ca88-4cbf-4812-9e76-c211e7c6701b
date added to LUP
2024-10-28 13:59:03
date last changed
2025-06-10 21:38:34
@article{4d90ca88-4cbf-4812-9e76-c211e7c6701b,
  abstract     = {{<p>Magnetic resonance imaging (MRI) is the method of choice for noninvasive studies of micrometer-scale structures in biological tissues via their effects on the time- and frequency-dependent (restricted) and anisotropic self-diffusion of water. While new designs of time-dependent magnetic field gradient waveforms have enabled disambiguation between different aspects of translational motion that are convolved in traditional MRI methods relying on single pairs of field gradient pulses, data analysis for complex heterogeneous materials remains a challenge. Here, we propose and demonstrate nonparametric distributions of tensor-valued Lorentzian diffusion spectra, or “D(ω) distributions,” as a general representation with sufficient flexibility to describe the MRI signal response from a wide range of model systems and biological tissues investigated with modulated gradient waveforms separating and correlating the effects of restricted and anisotropic diffusion.</p>}},
  author       = {{Narvaez, Omar and Yon, Maxime and Jiang, Hong and Bernin, Diana and Forssell-Aronsson, Eva and Sierra, Alejandra and Topgaard, Daniel}},
  issn         = {{0021-9606}},
  language     = {{eng}},
  number       = {{8}},
  publisher    = {{American Institute of Physics (AIP)}},
  series       = {{Journal of Chemical Physics}},
  title        = {{Nonparametric distributions of tensor-valued Lorentzian diffusion spectra for model-free data inversion in multidimensional diffusion MRI}},
  url          = {{http://dx.doi.org/10.1063/5.0213252}},
  doi          = {{10.1063/5.0213252}},
  volume       = {{161}},
  year         = {{2024}},
}