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Neuroimage. 2016 Jul 15;135:345-62. doi: 10.1016/j.neuroimage.2016.02.039. Epub 2016 Feb 23.

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

Author information

1
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Engineering, Linköping University, Linköping, Sweden. Electronic address: westin@bwh.harvard.edu.
2
Department of Biomedical Engineering, Linköping University, Linköping, Sweden.
3
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4
Department of Medical Radiation Physics, Lund University, Lund, Sweden.
5
Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Physics, Bogazici University, Istanbul, Turkey.
6
Department of Diagnostic Radiology, Lund University, Lund, Sweden.
7
Clinical Sciences, Psychiatry, Lund University, Lund, Sweden.
8
Division of Physical Chemistry, Department of Chemistry, Lund University, Lund, Sweden.
9
Lund University Bioimaging Center, Lund University, Lund, Sweden.

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.

KEYWORDS:

DDE; DTI; Diffusion MRI; Diffusion tensor distribution; Microscopic anisotropy; Microscopic fractional anisotropy μFA; QTI; SDE; TDE; q-space; q-space trajectory

PMID:
26923372
PMCID:
PMC4916005
DOI:
10.1016/j.neuroimage.2016.02.039
[Indexed for MEDLINE]
Free PMC Article

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