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NMR Biomed. 2017 Jul;30(7). doi: 10.1002/nbm.3722. Epub 2017 Mar 22.

Tensor estimation for double-pulsed diffusional kurtosis imaging.

Author information

1
Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina, USA.
2
Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina, USA.
3
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China.
4
Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina, USA.
5
Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, USA.

Abstract

Double-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.

KEYWORDS:

DKI; MRI; brain; double diffusion encoding; kurtosis; least squares; microscopic diffusion anisotropy; tensor

PMID:
28328072
DOI:
10.1002/nbm.3722
[Indexed for MEDLINE]

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