Abstract
Advanced Diffusion Weighted Imaging (DWI) techniques and leading tractography algorithms produce dense fiber sets of hundreds of thousands of fibers, or more. In order to make fiber based analysis more practical, the fiber set needs to be preprocessed to eliminate redundancies and to keep only essential representative fibers. In this paper we evaluate seven commonly used distance metrics for fiber clustering and present a novel approach for comparing the metrics as well as estimating the anatomical information loss as a function of the reduction rate. The framework includes pre-clustering into sub-groups using K-means, followed by further decomposition using Hierarchical Clustering, each time with a different distance metric. Finally, volume histograms comparison is used to compare the reduction quality with the different metrics. The proposed comparison was applied to a dataset containing tractographies of four healthy individuals. Each set contains around 600k fibers.
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Acknowledgements
We would like to thank our colleagues for providing the data and tractography for this work: Marilu Gorno-Tempini, MD, Ph.D., Language and Neurobiology Laboratory at the UCSF Memory and Aging Center, Roland Henry Ph.D., department of Neurology, UCSF, as well as Maria Luisa Mandelli and Bagrat Amirbekian, department of Neurology, UCSF.
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Moreno, G.Z., Alexandroni, G., Greenspan, H. (2016). White Matter Fiber Set Simplification by Redundancy Reduction with Minimum Anatomical Information Loss. In: Fuster, A., Ghosh, A., Kaden, E., Rathi, Y., Reisert, M. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-28588-7_15
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DOI: https://doi.org/10.1007/978-3-319-28588-7_15
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