Abstract
Probabilistic tractography provides estimates of the probability of a structural connection between points or regions in a brain volume, based on information from diffusion MRI. The ability to estimate the uncertainty associated with reconstructed pathways is valuable, but noise in the image data leads to premature termination or erroneous trajectories in sampled streamlines. In this work we describe automated methods, based on a probabilistic model of tract shape variability between individuals, which can be applied to select seed points in order to maximise consistency in tract segmentation; and to discard streamlines which are unlikely to belong to the tract of interest. Our method is shown to ameliorate false positives and remove the widely observed falloff in connection probability with distance from the seed region due to noise, two important problems in the tractography literature. Moreover, the need to apply an arbitrary threshold to connection probability maps is entirely obviated by our approach, thus removing a significant user-specified parameter from the tractography pipeline.
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Keywords
- Fractional Anisotropy
- Seed Region
- Orientation Distribution Function
- Seed Point
- Markov Chain Monte Carlo Method
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© 2009 Springer-Verlag Berlin Heidelberg
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Clayden, J.D., King, M.D., Clark, C.A. (2009). Shape Modelling for Tract Selection. In: Yang, GZ., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009. MICCAI 2009. Lecture Notes in Computer Science, vol 5762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04271-3_19
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DOI: https://doi.org/10.1007/978-3-642-04271-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04270-6
Online ISBN: 978-3-642-04271-3
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