Probabilistic multi-tensor estimation using the tensor distribution function
Leow, A., Zhu, S., McMahon, K., de Zubicaray, G. I., Meredith, M., Wright, M., & Thompson, P. (2008) Probabilistic multi-tensor estimation using the tensor distribution function. In IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008, IEEE, Anchorage, AK, pp. 1-6.
Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. Recently, a number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the Tensor Distribution Function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF. Once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.
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|Item Type:||Conference Paper|
|Divisions:||Current > QUT Faculties and Divisions > Faculty of Health
Current > Institutes > Institute of Health and Biomedical Innovation
|Copyright Owner:||Copyright 2008 IEEE.|
|Deposited On:||12 Nov 2015 01:44|
|Last Modified:||02 Dec 2015 02:51|
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