Probabilistic Tractography for Topographically Organized Connectomes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)

Abstract

While tractography is widely used in brain imaging research, its quantitative validation is highly difficult. Many fiber systems, however, have well-known topographic organization which can even be quantitatively mapped such as the retinotopy of visual pathway. Motivated by this previously untapped anatomical knowledge, we develop a novel tractography method that preserves both topographic and geometric regularity of fiber systems. For topographic preservation, we propose a novel likelihood function that tests the match between parallel curves and fiber orientation distributions. For geometric regularity, we use Gaussian distributions of Frenet-Serret frames. Taken together, we develop a Bayesian framework for generating highly organized tracks that accurately follow neuroanatomy. Using multi-shell diffusion images of 56 subjects from Human Connectome Project, we compare our method with algorithms from MRtrix. By applying regression analysis between retinotopic eccentricity and tracks, we quantitatively demonstrate that our method achieves superior performance in preserving the retinotopic organization of optic radiation.

Keywords

Probabilistic tractography Bayesian inference Visual pathway 

Notes

Acknowledgements

This work was in part supported by the National Institute of Health (NIH) under Grant K01EB013633, P41EB015922, P50AG005142, U01EY025864, U01AG051218.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesUSA

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