Abstract
In this paper we propose a novel algorithm which leverages models of white matter fibre dispersion to improve tractography. Tractography methods exploit directional information from diffusion weighted magnetic resonance (DW-MR) imaging to infer connectivity between different brain regions. Most tractography methods use a single direction (e.g. the principal eigenvector of the diffusion tensor) or a small set of discrete directions (e.g. from the peaks of an orientation distribution function) to guide streamline propagation. This strategy ignores the effects of within-bundle orientation dispersion, which arises from fanning or bending at the sub-voxel scale, and can lead to missing connections. Various recent DW-MR imaging techniques estimate the fibre dispersion in each bundle directly and model it as a continuous distribution. Here we introduce an algorithm to exploit this information to improve tractography. The algorithm further uses a particle filter to probe local neighbourhood structure during streamline propagation. Using information gathered from neighbourhood structure enables the algorithm to resolve ambiguities between converging and diverging fanning structures, which cannot be distinguished from isolated orientation distribution functions. We demonstrate the advantages of the new approach in synthetic experiments and in vivo data. Synthetic experiments demonstrate the effectiveness of the particle filter in gathering and exploiting neighbourhood information in recovering various canonical fibre configurations and experiments with in vivo brain data demonstrate the advantages of utilising dispersion in tractography, providing benefits in practical situations.
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References
Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.M.: Three dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999)
Conturo, T.E., Lori, N.F., Cull, T.S., Akbudak, E., Snyder, A.Z., Shimony, J.S., McKinstry, R.C., Burton, H., Raichle, M.E.: Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. U.S.A. 96, 10422–10427 (1999)
Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vitro fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000)
Parker, G.J., Alexander, D.C.: Probabilistic Monte Carlo based mapping of cerebral connections utilising whole-brain crossing fibre information. In: Taylor, C.J., Noble, J.A. (eds.) IPMI 2003. LNCS, vol. 2732, pp. 684–695. Springer, Heidelberg (2003)
Behrens, T.E.J., Johansen-Berg, H., Woolrich, M.W., Smith, S.M., Wheeler-Kingshott, C.A.M., Boulby, P.A., Barker, G.J., Sillery, E.L., Sheehan, K., Cicarelli, O., Thompson, A.J., Brady, J.M., Matthews, P.M.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003)
Parker, G.J., Alexander, D.C.: Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Philosophical Transactions of the Royal Society B: Biological Sciences 360, 893–902 (2005)
Behrens, T.E.J., Johansen-Berg, H., Jbabdi, S., Rushworth, M.F.S., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: What can we gain? NeuroImage 34, 144–155 (2007)
Tournier, J., Calamante, F., Connelly, A.: Robust determination of the fibre orientation distribution in diffusion mri: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35, 1459–1472 (2007)
Jeurissen, B., Leemans, A., Tournier, J.D., Sijbers, J.: Estimation of uncertainty in constrained spherical deconvolution fiber orientations. In: IEEE International Symposium on Biomedical Imaging: Macro to Nano, pp. 907–910 (2008)
Jeurissen, B., Leemans, A., Jones, D.K., Tournier, J.D., Sijbers, J.: Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution. Human Brain Mapping 32, 461–479 (2011)
Tournier, J.D., Calamante, F., Gadian, D.G., Connelly, A.: Probabilistic fibre tracking through regions containing crossing fibres. In: Proc. Intl. Soc. Mag. Reson. Med., vol. 13, p. 1343 (2005)
Sherbondy, A.J., Dougherty, R.F., Ananthanarayanan, R., Modha, D.S., Wandell, B.A.: Think Global, Act Local; Projectome Estimation with Bluematter. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 861–868. Springer, Heidelberg (2009)
Fillard, P., Poupon, C., Mangin, J.-F.: A Novel Global Tractography Algorithm Based on an Adaptive Spin Glass Model. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 927–934. Springer, Heidelberg (2009)
Kreher, B.W., Mader, I., Kiselev, V.G.: Gibbs Tracking: A Novel Approach for the Reconstruction of Neuronal Pathways. Magnetic Resonance in Medicine 60, 953–963 (2008)
Sherbondy, A.J., Rowe, M.C., Alexander, D.C.: MicroTrack: an algorithm for concurrent projectome and microstructure estimation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 183–190. Springer, Heidelberg (2010)
Li, L., Rilling, J.K., Preuss, T.M., Glasser, M.F., Damen, F.W., Hu, X.: Quantitative assessment of a framework for creating anatomical brain networks via global tractography. NeuroImage 61, 1017–1030 (2012)
Kaden, E., Knosche, T.R., Anwander, A.: Parametric spherical deconvolution: Inferring anatomical connectivity using diffusion MR imaging. NeuroImage 37, 474–488 (2007)
Zhang, H., Hubbard, P.L., Parker, G.J.M., Alexander, D.C.: Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. NeuroImage 56, 1301–1315 (2011)
Sotiropoulos, S.N., Behrens, T.E.J., Jbabdi, S.: Ball and rackets: Inferring fiber fanning from diffusion-weighted MRI. NeuroImage 60, 1412–1425 (2012)
Zhang, H., Shneider, T., Wheeler-Kingshott, C., Alexander, D.C.: NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. NeuroImage 61, 1000–1016 (2012)
Jespersen, S.N., Leigland, L.A., Cornea, A., Kroenke, C.D.: Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Trans. Med. Imaging 31, 16–32 (2012)
Rowe, M., Zhang, H., Alexander, D.C.: Utilising measures of fiber dispersion in white matter tractography. In: MICCAI CDMRI Workshop (2012)
Savadjiev, P., Campbell, J.S.W., Descoteaux, M., Deriche, R., Pike, G.B., Siddiqi, K.: Labeling of ambiguous subvoxel fibre bundle configurations in high angular resolution diffusion MRI. NeuroImage 41, 58–68 (2008)
Zhang, F., Hancock, E.R., Goodlett, C., Gerig, G.: White matter tractography using sequential importance sampling. In: Proc. ISMRM Annual Meeting, vol. 10 (2002)
Zhang, F., Hancock, E.R., Goodlett, C., Gerig, G.: Probabilistic white matter fiber tracking using particle filtering and von Mises-Fisher sampling. Med. Image Anal. 13, 5–18 (2009)
Pontabry, J., Rousseau, F.: Probabilistic tractography using Q-ball modeling and particle filtering. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 209–216. Springer, Heidelberg (2011)
Doucet, A., Godsill, S., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Stat. and Comput. 10, 197–208 (2000)
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Rowe, M., Zhang, H.G., Oxtoby, N., Alexander, D.C. (2013). Beyond Crossing Fibers: Tractography Exploiting Sub-voxel Fibre Dispersion and Neighbourhood Structure. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds) Information Processing in Medical Imaging. IPMI 2013. Lecture Notes in Computer Science, vol 7917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38868-2_34
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DOI: https://doi.org/10.1007/978-3-642-38868-2_34
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