Regularization of diffusion tensor images

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

Research output: Contribution to conferencePaper

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 languageAmerican English
Pages935-938
Number of pages4
DOIs
StatePublished - 1 Jan 2008
Event2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI -
Duration: 1 Jan 2008 → …

Conference

Conference2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI
Period1/01/08 → …

Keywords

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

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