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Characterizing Non‐gaussian Diffusion by Using Generalized Diffusion Tensors by Chunlei Liu; Roland Bammer; Burak Acar; Michael E. Moseley is a Medicine article available to read on EtoBox.

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## Abstract Diffusion tensor imaging (DTI) is known to have a limited capability of resolving multiple fiber orientations within one voxel. This is mainly because the probability density function (PDF) for random spin displacement is non‐Gaussian in the confining environment of biological tissues and, thus, the modeling of self‐diffusion by a second‐order tensor breaks down. The statistical property of a non‐Gaussian diffusion process is characterized via the higher‐order tensor (HOT) coefficients by reconstructing the PDF of the random spin displacement. Those HOT coefficients can be determined by combining a series of complex diffusion‐weighted measurements. The signal equation for an MR diffusion experiment was investigated theoretically by generalizing Fick's law to a higher‐order partial differential equation (PDE) obtained via Kramers‐Moyal expansion. A relationship has been derived between the HOT coefficients of the PDE and the higher‐order cumulants of the random spin displacement. Monte‐Carlo simulations of diffusion in a restricted environment with different geometrical shapes were performed, and the strengths and weaknesses of both HOT and established diffusion analysis

Who reads Characterizing Non‐gaussian Diffusion by Using Generalized Diffusion Tensors?

Author

Chunlei Liu; Roland Bammer; Burak Acar; Michael E. Moseley

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 0740-3194)

Published

2004

Language

EN

Field

Medicine (Health Sciences)