Abstract
The diffusion-weighted magnetic resonance imaging (DW-MRI) has been used to diagnose anomalies in human brain by describing the magnitude and directionality of water diffusion per voxel. Such information can be represented alternatively in diffusion tensor imaging (DT-MRI), yielding images of normal and abnormal white matter fiber structures, and maps of brain connectivity through fiber tracking. A DW-MRI study is usually characterized by a low signal to noise ratio, which may reflect in the poor estimation of DT-MRI. Filters based on local similarity have been receiving increasing attention, but they have been barely studied for DT-MRI. In this proposal we introduce adaptive and optimized filtering techniques based on local similarity for MRI to remove the biasing in both DW-MRI filtering and DT-MRI estimation, evidencing a better performance respect to classical filters and robust DT estimation algorithms. We estimate the DT-MRI extracting metrics computed from the DT to evaluate the filtering performance.
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Notes
- 1.
The robust estimation of tensors by outlier rejection (RESTORE), uses iteratively reweighted least-squares regression to identify potential outliers and exclude them. Available on http://nipy.org/dipy/examples_built/restore_dti.html.
- 2.
Dataset used by Leigh Morrow et al. from a 3-Tesla Siemens Trio. Available at http://www.cabiatl.com/CABI/resources/dti-analysis/.
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Acknowledgment
This work was funded by COLCIENCIAS under the project 1110-569-34461. Authors were also supported by the 617 agreement, “Jóvenes Investigadores e Innovadores”, funded by COLCIENCIAS. Finally, the authors are thankful to the research group in Automática ascribed to the engineering program at the Universidad Tecnológica de Pereira, and M.Sc. H.F. García for technical support.
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López-Lopera, A.F., Álvarez, M.A., Orozco, Á.Á. (2015). Improving Diffusion Tensor Estimation Using Adaptive and Optimized Filtering Based on Local Similarity. In: Paredes, R., Cardoso, J., Pardo, X. (eds) Pattern Recognition and Image Analysis. IbPRIA 2015. Lecture Notes in Computer Science(), vol 9117. Springer, Cham. https://doi.org/10.1007/978-3-319-19390-8_69
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