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Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Model-based variational smoothing and segmentation for diffusion tensor imaging in the brain

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References (71)

Publisher
Springer Journals
Copyright
Copyright © Humana Press Inc. 2006
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1385/ni:4:3:217
Publisher site
See Article on Publisher Site

Abstract

This article applies a unified approach to variational smoothing and segmentation to brain diffusion tensor image data along user-selected attributes derived from the tensor, with the aim of extracting detailed brain structure information. The application of this framework simultaneously segments and denoises to produce edges and smoothed regions within the white matter of the brain that are relatively homogeneous with respect to the diffusion tensor attributes of choice. This approach enables the visualization of a smoothed, scale invariant representation of the tensor data field in a variety of diverse forms. In addition to known attributes such as fractional anisotropy, these representations include selected directional tensor components and additionally associated continuous valued edge fields that might be used for further segmentation. A comparison is presented of the results of three different data model selections with respect to their ability to resolve white matter structure. The resulting images are integrated to provide better perspective of the model properties (edges, smoothed image, and so forth) and their relationship to the underlying brain anatomy. The improvement in brain image quality is illustrated both qualitatively and quantitatively, and the robust performance of the algorithm in the presence of added noise is shown. Smoothing occurs without loss of edge features because of the simultaneous segmentation aspect of the variational approach, and the output enables better delineation of tensors representative of local and long-range association, projection, and commissural fiber systems.

Journal

Neuroinformatics – Springer Journals

Published: Sep 1, 2006

Keywords: Cerebral white matter; diffusion tensor imaging (DTI); magnetic resonance imaging (MRI); Variational segmentation functional

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