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Gradient waveform design for tensor-valued encoding in diffusion MRI

Szczepankiewicz, Filip LU orcid ; Westin, Carl Fredrik and Nilsson, Markus LU (2021) In Journal of Neuroscience Methods 348.
Abstract

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in... (More)

Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Diffusion magnetic resonance imaging, Gradient waveform design, Tensor-valued diffusion encoding
in
Journal of Neuroscience Methods
volume
348
article number
109007
publisher
Elsevier
external identifiers
  • scopus:85096829316
  • pmid:33242529
ISSN
0165-0270
DOI
10.1016/j.jneumeth.2020.109007
language
English
LU publication?
yes
id
200a8100-81a5-480c-94ea-bf9cf2342af1
date added to LUP
2020-12-14 10:03:12
date last changed
2024-06-28 06:09:33
@article{200a8100-81a5-480c-94ea-bf9cf2342af1,
  abstract     = {{<p>Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.</p>}},
  author       = {{Szczepankiewicz, Filip and Westin, Carl Fredrik and Nilsson, Markus}},
  issn         = {{0165-0270}},
  keywords     = {{Diffusion magnetic resonance imaging; Gradient waveform design; Tensor-valued diffusion encoding}},
  language     = {{eng}},
  month        = {{01}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Neuroscience Methods}},
  title        = {{Gradient waveform design for tensor-valued encoding in diffusion MRI}},
  url          = {{http://dx.doi.org/10.1016/j.jneumeth.2020.109007}},
  doi          = {{10.1016/j.jneumeth.2020.109007}},
  volume       = {{348}},
  year         = {{2021}},
}