Advanced

q-space trajectory imaging for multidimensional diffusion MRI of the human brain.

Westin, Carl-Fredrik; Knutsson, Hans; Pasternak, Ofer; Szczepankiewicz, Filip LU ; Özarslan, Evren; van Westen, Danielle LU ; Mattisson, Cecilia LU ; Bogren, Mats LU ; O'Donnell, Lauren and Kubicki, Marek, et al. (2016) In NeuroImage 135. p.345-362
Abstract
This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient... (More)
This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture. (Less)
Please use this url to cite or link to this publication:
author
, et al. (More)
(Less)
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
NeuroImage
volume
135
pages
18 pages
publisher
Elsevier
external identifiers
  • pmid:26923372
  • scopus:84973115643
  • wos:000378047600031
ISSN
1095-9572
DOI
10.1016/j.neuroimage.2016.02.039
language
English
LU publication?
yes
id
5ca6b951-4225-41f6-b770-c2e5b08ea9b1 (old id 8857173)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/26923372?dopt=Abstract
date added to LUP
2016-03-15 09:14:55
date last changed
2017-11-05 03:17:48
@article{5ca6b951-4225-41f6-b770-c2e5b08ea9b1,
  abstract     = {This work describes a new diffusion MR framework for imaging and modeling of microstructure that we call q-space trajectory imaging (QTI). The QTI framework consists of two parts: encoding and modeling. First we propose q-space trajectory encoding, which uses time-varying gradients to probe a trajectory in q-space, in contrast to traditional pulsed field gradient sequences that attempt to probe a point in q-space. Then we propose a microstructure model, the diffusion tensor distribution (DTD) model, which takes advantage of additional information provided by QTI to estimate a distributional model over diffusion tensors. We show that the QTI framework enables microstructure modeling that is not possible with the traditional pulsed gradient encoding as introduced by Stejskal and Tanner. In our analysis of QTI, we find that the well-known scalar b-value naturally extends to a tensor-valued entity, i.e., a diffusion measurement tensor, which we call the b-tensor. We show that b-tensors of rank 2 or 3 enable estimation of the mean and covariance of the DTD model in terms of a second order tensor (the diffusion tensor) and a fourth order tensor. The QTI framework has been designed to improve discrimination of the sizes, shapes, and orientations of diffusion microenvironments within tissue. We derive rotationally invariant scalar quantities describing intuitive microstructural features including size, shape, and orientation coherence measures. To demonstrate the feasibility of QTI on a clinical scanner, we performed a small pilot study comparing a group of five healthy controls with five patients with schizophrenia. The parameter maps derived from QTI were compared between the groups, and 9 out of the 14 parameters investigated showed differences between groups. The ability to measure and model the distribution of diffusion tensors, rather than a quantity that has already been averaged within a voxel, has the potential to provide a powerful paradigm for the study of complex tissue architecture.},
  author       = {Westin, Carl-Fredrik and Knutsson, Hans and Pasternak, Ofer and Szczepankiewicz, Filip and Özarslan, Evren and van Westen, Danielle and Mattisson, Cecilia and Bogren, Mats and O'Donnell, Lauren and Kubicki, Marek and Topgaard, Daniel and Nilsson, Markus},
  issn         = {1095-9572},
  language     = {eng},
  month        = {02},
  pages        = {345--362},
  publisher    = {Elsevier},
  series       = {NeuroImage},
  title        = {q-space trajectory imaging for multidimensional diffusion MRI of the human brain.},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2016.02.039},
  volume       = {135},
  year         = {2016},
}