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| Home > Publications database > Robust tensor estimation in diffusion tensor imaging. |
| Journal Article | PreJuSER-19553 |
; ;
2011
Elsevier
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.jmr.2011.09.035
Abstract: The signal response measured in diffusion tensor imaging is subject to detrimental influences caused by noise. Noise fields arise due to various contributions such as thermal and physiological noise and sources related to the hardware imperfection. As a result, diffusion tensors estimated by different linear and non-linear least squares methods in absence of a proper noise correction tend to be substantially corrupted. In this work, we propose an advanced tensor estimation approach based on the least median squares method of the robust statistics. Both constrained and non-constrained versions of the method are considered. The performance of the developed algorithm is compared to that of the conventional least squares method and of the alternative robust methods proposed in the literature. Two examples of simulated diffusion attenuations and experimental in vivo diffusion data sets were used as a basis for comparison. The robust algorithms were shown to be advantageous compared to the least squares method in the cases where elimination of the outliers is desirable. Additionally, the constraints were applied in order to prevent generation of the non-positive definite tensors and reduce related artefacts in the maps of fractional anisotropy. The developed method can potentially be exploited also by other MR techniques where a robust regression or outlier localisation is required.
Keyword(s): Algorithms (MeSH) ; Computer Simulation (MeSH) ; Diffusion Tensor Imaging: methods (MeSH) ; Diffusion Tensor Imaging: statistics & numerical data (MeSH) ; Image Enhancement: methods (MeSH) ; Image Processing, Computer-Assisted: methods (MeSH) ; Image Processing, Computer-Assisted: statistics & numerical data (MeSH) ; Least-Squares Analysis (MeSH) ; Nonlinear Dynamics (MeSH) ; Signal-To-Noise Ratio (MeSH) ; J ; Diffusion tensor imaging (auto) ; Noise correction (auto) ; Least median squares (auto) ; Robust tensor estimation (auto) ; Non-linear constrained robust regression (auto)
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