Abstract
Based on a diffusion tensor image (DTI) and a tentative tractogram of a fiber bundle we propose a filtering method for operationally defining and removing outliers using tractometry. To this end we assign to each track a set of K invariants, i.e. scalars invariant under rigid transformations. The cluster of K-tuples of all tracks in a bundle may be pruned using outlier detection methods in \(\mathbb {R}^K\), after which backprojection of the remaining K-tuples produces a filtered tractogram with enhanced coherence. This intrinsic pruning method is blind to the relative spatial organization of tracks in a bundle. We consider two types of invariants, one capturing local diffusion properties and one representing differential properties averaged along tracks. Our experiments indicate that our tractometric filtering is complementary to extrinsic methods based on the relative spatial configuration of tracks.
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Notes
- 1.
Super-/subscripts denote contra-/covariant indices, to which Einstein summation convention applies, i.e. each pair of identical sub- and superscript implies a summation over the corresponding ‘dummy’ index.
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Acknowledgements
This work is part of the research programme “Diffusion MRI Tractography with Uncertainty Propagation for the Neurosurgical Workflow” with project number 16338, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO). This work was supported by a research grant (00028384) from VILLUM FONDEN. We would like to thank neurosurgeon Geert-Jan Rutten for sharing the clinical dataset used in our experiments at the Elisabeth TweeSteden Hospital (ETZ) in Tilburg, The Netherlands, and for fruitful discussions.
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Sengers, R., Dela Haije, T., Fuster, A., Florack, L. (2022). Tractometric Coherence of Fiber Bundles in DTI. In: Cetin-Karayumak, S., et al. Computational Diffusion MRI. CDMRI 2022. Lecture Notes in Computer Science, vol 13722. Springer, Cham. https://doi.org/10.1007/978-3-031-21206-2_12
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