Abstract
The architecture of the white matter is endowed with kissing and crossing bundles configurations. When these white matter tracts are reconstructed using diffusion MRI tractography, this systematically induces the reconstruction of many fiber tracts that are not coherent with the structure of the brain. The question on how to discriminate between true positive connections and false positive connections is the one addressed in this work. State-of-the-art techniques provide a partial solution to this problem by considering anatomical priors in the false positives detection process. We propose a novel model that tackles the same issue but takes into account both structural and functional information by combining them in a convex optimization problem. We validate it on two toy phantoms that reproduce the kissing and the crossing bundles configurations, showing that through this approach we are able to correctly distinguish true positives and false positives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bach, F., Jenatton, R., Mairal, J., Obozinski, G., et al.: Structured sparsity through convex optimization. Stat. Sci. 27(4), 450–468 (2012)
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)
Caruyer, E., Daducci, A., Descoteaux, M., Houde, J.C., Thiran, J.P., Verma, R.: Phantomas: a flexible software library to simulate diffusion mr phantoms. In: ISMRM (2014)
Caruyer, E., Lenglet, C., Sapiro, G., Deriche, R.: Design of multishell sampling schemes with uniform coverage in diffusion MRI. Magn. Reson. Med. 69(6), 1534–1540 (2013)
Daducci, A., Barakovic, M., Girard, G., Descoteaux, M., Thiran, J.P.: Reducing false positives in tractography with microstructural and anatomical priors. In: ISMRM 2018-International Society for Magnetic Resonance in Medicine (2018)
Daducci, A., Dal Palú, A., Descoteaux, M., Thiran, J.P.: Microstructure informed tractography: pitfalls and open challenges. Front. Neurosci. 10, 247 (2016)
Daducci, A., Dal Palù, A., Lemkaddem, A., Thiran, J.P.: Commit: convex optimization modeling for microstructure informed tractography. IEEE Trans. Med. Imaging 34(1), 246–257 (2015)
Frigo, M., Barakovic, M., Thiran, J.P., Daducci, A.: Hierarchical tractography optimisation. In: CoBCoM 2017-Computational Brain Connectivity Mapping Winter School Workshop, p. 1 (2017)
Frigo, M., Gallardo, G., Costantini, I., Daducci, A., Wassermann, D., Deriche, R., Deslauriers-Gauthier, S.: Reducing false positive connection in tractograms using joint structure-function filtering. In: OHBM 2018-Organization for Human Brain Mapping (2018)
Girard, G., Fick, R., Descoteaux, M., Deriche, R., Wassermann, D.: Axtract: microstructure-driven tractography based on the ensemble average propagator. In: International Conference on Information Processing in Medical Imaging, pp. 675–686. Springer (2015)
Jenatton, R., Mairal, J., Obozinski, G., Bach, F.R.: Proximal methods for sparse hierarchical dictionary learning. In: ICML, pp. 487–494 (2010) (No. 2010, Citeseer)
Leonardi, N., Van De Ville, D.: On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104, 430–436 (2015)
Maier-Hein, K.H., Neher, P.F., Houde, J.C., Côté, M.A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.C., Lin, Y.C., Ji, Q., et al.: The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8(1), 1349 (2017)
Panagiotaki, E., Schneider, T., Siow, B., Hall, M.G., Lythgoe, M.F., Alexander, D.C.: Compartment models of the diffusion MR signal in brain white matter: a taxonomy and comparison. Neuroimage 59(3), 2241–2254 (2012)
Pestilli, F., Yeatman, J.D., Rokem, A., Kay, K.N., Wandell, B.A.: Evaluation and statistical inference for human connectomes. Nat. Methods 11(10), 1058 (2014)
Sanz Leon, P., Knock, S.A., Woodman, M.M., Domide, L., Mersmann, J., McIntosh, A.R., Jirsa, V.: The virtual brain: a simulator of primate brain network dynamics. Front. Neuroinformatics 7, 10 (2013)
Smith, R.E., Tournier, J.D., Calamante, F., Connelly, A.: Sift2: enabling dense quantitative assessment of brain white matter connectivity using streamlines tractography. Neuroimage 119, 338–351 (2015)
The virtual brain: the resting state network scripting tutorial (2018)
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L.L., van den Heuvel, M.P., Breakspear, M.: Connectome sensitivity or specificity: which is more important? Neuroimage 142, 407–420 (2016)
Acknowledgements
The authors would like to thank Rebecca Bonham-Carter for the help in simulating resting state networks. This work received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM–Computational Brain Connectivity Mapping).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Frigo, M., Costantini, I., Deriche, R., Deslauriers-Gauthier, S. (2019). Resolving the Crossing/Kissing Fiber Ambiguity Using Functionally Informed COMMIT. In: Bonet-Carne, E., Grussu, F., Ning, L., Sepehrband, F., Tax, C. (eds) Computational Diffusion MRI. MICCAI 2019. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-05831-9_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-05831-9_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05830-2
Online ISBN: 978-3-030-05831-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)