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Interpolating multi-fiber models by Gaussian mixture simplification | IEEE Conference Publication | IEEE Xplore

Interpolating multi-fiber models by Gaussian mixture simplification


Abstract:

Multi-fiber models have been introduced to leverage the accuracy of the diffusion representation in crossing fiber areas. The improved accuracy may, however, be impaired ...Show More

Abstract:

Multi-fiber models have been introduced to leverage the accuracy of the diffusion representation in crossing fiber areas. The improved accuracy may, however, be impaired by poor processing of the multi-fiber models. In particular, interpolating multi-fiber models proves challenging, while it is a pervasive and recurrent task in many processes. The error accumulated from iterating a poor interpolation may yield significantly corrupted global results. In this paper, we propose an interpolation scheme based on gaussian mixture simplification and demonstrate its benefits over a heuristic approach in terms of spatial normalization and tractography results.
Date of Conference: 02-05 May 2012
Date Added to IEEE Xplore: 12 July 2012
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Conference Location: Barcelona, Spain
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
ICTEAM Institute, Université catholique de Louvain, Belgium
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
ICTEAM Institute, Université catholique de Louvain, Belgium
Computational Radiology Laboratory, Harvard Medical School, Boston, USA

Computational Radiology Laboratory, Harvard Medical School, Boston, USA
ICTEAM Institute, Université catholique de Louvain, Belgium
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
Computational Radiology Laboratory, Harvard Medical School, Boston, USA
ICTEAM Institute, Université catholique de Louvain, Belgium
Computational Radiology Laboratory, Harvard Medical School, Boston, USA

References

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