Regularization of diffusion tensor images

J. Cisternas; T. Asahi; M. Gálvez; G. Rojas

doi:10.1109/ISBI.2008.4541151

Regularization of diffusion tensor images

J. Cisternas, T. Asahi, M. Gálvez, G. Rojas

Research output: Contribution to conference › Paper

2 Scopus citations

Abstract

We present a regularization scheme for diffusion tensor images, that respects the geometrical structure of diffusion ellipsoids and does not introduce artifacts such as anisotropy drops. The method can be stated as a variational problem and solved by means of a gradient flow. The main ingredient is the notion of a distance between two ellipsoids that considers differences in shape as well as differences in orientation. The method is specialized to the case of cylindrically-symmetric ellipsoids and implemented in terms of ordinary vector manipulations such as cross and dot products. The regularization algorithm is tested using a synthetic tensor field and a dataset acquired from a diffusion phantom. In both cases the algorithm was able to reduce the noise from the tensor field.

Original language	American English
Pages	935-938
Number of pages	4
DOIs	https://doi.org/10.1109/ISBI.2008.4541151
State	Published - 1 Jan 2008
Event	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI - Duration: 1 Jan 2008 → …

Conference

Conference	2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
Period	1/01/08 → …

Keywords

Biomedical image processing
Biomedical magnetic resonance imaging
Eigenvalues and eigenfunctions
Smoothing methods
Variational methods

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10.1109/ISBI.2008.4541151

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AU - Cisternas, J.

AU - Asahi, T.

AU - Gálvez, M.

AU - Rojas, G.

PY - 2008/1/1

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N2 - We present a regularization scheme for diffusion tensor images, that respects the geometrical structure of diffusion ellipsoids and does not introduce artifacts such as anisotropy drops. The method can be stated as a variational problem and solved by means of a gradient flow. The main ingredient is the notion of a distance between two ellipsoids that considers differences in shape as well as differences in orientation. The method is specialized to the case of cylindrically-symmetric ellipsoids and implemented in terms of ordinary vector manipulations such as cross and dot products. The regularization algorithm is tested using a synthetic tensor field and a dataset acquired from a diffusion phantom. In both cases the algorithm was able to reduce the noise from the tensor field.

AB - We present a regularization scheme for diffusion tensor images, that respects the geometrical structure of diffusion ellipsoids and does not introduce artifacts such as anisotropy drops. The method can be stated as a variational problem and solved by means of a gradient flow. The main ingredient is the notion of a distance between two ellipsoids that considers differences in shape as well as differences in orientation. The method is specialized to the case of cylindrically-symmetric ellipsoids and implemented in terms of ordinary vector manipulations such as cross and dot products. The regularization algorithm is tested using a synthetic tensor field and a dataset acquired from a diffusion phantom. In both cases the algorithm was able to reduce the noise from the tensor field.

KW - Biomedical image processing

KW - Biomedical magnetic resonance imaging

KW - Eigenvalues and eigenfunctions

KW - Smoothing methods

KW - Variational methods

KW - Biomedical image processing

KW - Biomedical magnetic resonance imaging

KW - Eigenvalues and eigenfunctions

KW - Smoothing methods

KW - Variational methods

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Regularization of diffusion tensor images

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