The application of diffusion tensor MRI to neurological segmentation

Andrew Simmons; Derek K. Jones; M A Horsfield; Steven Williams

The application of diffusion tensor MRI to neurological segmentation

Andrew Simmons, Derek K. Jones, M A Horsfield, Steven Williams

Neuroimaging

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

We consider the application of segmentation based on cluster classification techniques to a series of images derived from the diffusion tensor. The extension of a sophisticated cluster simulation tool used for optimizing data acquisition for such segmentation methods to diffusion-based images is described. The characteristics of a variety of diffusion-based images including fractional anisotropy images, diffusion tensor trace images, and isotropically diffusion-weighted images are considered and their application to neurological image segmentation is investigated. The critical effect of the signal-to-noise ratio on fractional anisotropy is analyzed and limitations of current echo planar–based strategies are discussed. Segmentation is shown to be possible using only images derived from the diffusion tensor, and such images are shown to offer exciting new avenues for neurological segmentation. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 273–286, 1999

Original language	English
Pages (from-to)	273-286
Journal	INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
Volume	10
Issue number	3
Publication status	Published - 1999

Cite this

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title = "The application of diffusion tensor MRI to neurological segmentation",

abstract = "We consider the application of segmentation based on cluster classification techniques to a series of images derived from the diffusion tensor. The extension of a sophisticated cluster simulation tool used for optimizing data acquisition for such segmentation methods to diffusion-based images is described. The characteristics of a variety of diffusion-based images including fractional anisotropy images, diffusion tensor trace images, and isotropically diffusion-weighted images are considered and their application to neurological image segmentation is investigated. The critical effect of the signal-to-noise ratio on fractional anisotropy is analyzed and limitations of current echo planar–based strategies are discussed. Segmentation is shown to be possible using only images derived from the diffusion tensor, and such images are shown to offer exciting new avenues for neurological segmentation. {\textcopyright} 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 273–286, 1999",

author = "Andrew Simmons and Jones, {Derek K.} and Horsfield, {M A} and Steven Williams",

year = "1999",

language = "English",

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T1 - The application of diffusion tensor MRI to neurological segmentation

AU - Simmons, Andrew

AU - Jones, Derek K.

AU - Horsfield, M A

AU - Williams, Steven

PY - 1999

Y1 - 1999

N2 - We consider the application of segmentation based on cluster classification techniques to a series of images derived from the diffusion tensor. The extension of a sophisticated cluster simulation tool used for optimizing data acquisition for such segmentation methods to diffusion-based images is described. The characteristics of a variety of diffusion-based images including fractional anisotropy images, diffusion tensor trace images, and isotropically diffusion-weighted images are considered and their application to neurological image segmentation is investigated. The critical effect of the signal-to-noise ratio on fractional anisotropy is analyzed and limitations of current echo planar–based strategies are discussed. Segmentation is shown to be possible using only images derived from the diffusion tensor, and such images are shown to offer exciting new avenues for neurological segmentation. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 273–286, 1999

AB - We consider the application of segmentation based on cluster classification techniques to a series of images derived from the diffusion tensor. The extension of a sophisticated cluster simulation tool used for optimizing data acquisition for such segmentation methods to diffusion-based images is described. The characteristics of a variety of diffusion-based images including fractional anisotropy images, diffusion tensor trace images, and isotropically diffusion-weighted images are considered and their application to neurological image segmentation is investigated. The critical effect of the signal-to-noise ratio on fractional anisotropy is analyzed and limitations of current echo planar–based strategies are discussed. Segmentation is shown to be possible using only images derived from the diffusion tensor, and such images are shown to offer exciting new avenues for neurological segmentation. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 273–286, 1999

M3 - Article

SN - 0899-9457

VL - 10

SP - 273

EP - 286

JO - INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY

JF - INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY

IS - 3

ER -

The application of diffusion tensor MRI to neurological segmentation

Abstract

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