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Magic DIAMOND : Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding

Reymbaut, Alexis ; Caron, Alex Valcourt ; Gilbert, Guillaume ; Szczepankiewicz, Filip LU orcid ; Nilsson, Markus LU ; Warfield, Simon K. ; Descoteaux, Maxime and Scherrer, Benoit (2021) In Medical Image Analysis 70.
Abstract

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition... (More)

Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this “Magic DIAMOND” model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.

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author
; ; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Crossing fibers, Diffusion MRI, Microstructure, Tensor distribution modeling, Tensor-valued diffusion encoding, Tractography
in
Medical Image Analysis
volume
70
article number
101988
publisher
Elsevier
external identifiers
  • pmid:33611054
  • scopus:85100909167
ISSN
1361-8415
DOI
10.1016/j.media.2021.101988
language
English
LU publication?
yes
id
d1ed6a6f-660c-456b-9de0-f5f65f6fb2f2
date added to LUP
2021-03-01 08:54:48
date last changed
2024-06-13 07:38:06
@article{d1ed6a6f-660c-456b-9de0-f5f65f6fb2f2,
  abstract     = {{<p>Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this “Magic DIAMOND” model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.</p>}},
  author       = {{Reymbaut, Alexis and Caron, Alex Valcourt and Gilbert, Guillaume and Szczepankiewicz, Filip and Nilsson, Markus and Warfield, Simon K. and Descoteaux, Maxime and Scherrer, Benoit}},
  issn         = {{1361-8415}},
  keywords     = {{Crossing fibers; Diffusion MRI; Microstructure; Tensor distribution modeling; Tensor-valued diffusion encoding; Tractography}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Medical Image Analysis}},
  title        = {{Magic DIAMOND : Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding}},
  url          = {{http://dx.doi.org/10.1016/j.media.2021.101988}},
  doi          = {{10.1016/j.media.2021.101988}},
  volume       = {{70}},
  year         = {{2021}},
}