@inproceedings{cb99a53815674819ae1ce7a4de1655e9,
title = "Brain Connectivity Measures via Direct Sub-Finslerian Front Propagation on the 5D Sphere Bundle of Positions and Directions",
abstract = "We propose a novel connectivity measure between brain regions using diffusion-weighted MRI. This connectivity measure is based on optimal sub-Finslerian geodesic front propagation on the 5D base manifold of (3D) positions and (2D) directions, the so-called sphere bundle. The advantage over spatial front propagations is that it prevents leakage at omnipresent crossings. Our optimal fronts on the sphere bundle are geodesically equidistant w.r.t. an asymmetric Finsler metric, and can be computed with existing anisotropic fast-marching methods. Comparisons to ground truth connectivities provided by the ISBI-HARDI challenge reveal promising results, both quantitatively and qualitatively. We also apply the connectivity measures to real data from the Human Connectome Project.",
keywords = "anterior nucleus, brain connectivity, deconvolution, diffusion mri, electrical-stimulation, fast-marching, finsler geometry, geodesic fronts, geodesics, sphere bundle, system, thalamus, tractography, SYSTEM, Finsler geometry, Fast-marching, GEODESICS, ANTERIOR NUCLEUS, DECONVOLUTION, Brain connectivity, TRACTOGRAPHY, Sphere bundle, Geodesic fronts, ELECTRICAL-STIMULATION, THALAMUS, Diffusion MRI",
author = "J. Portegies and S. Meesters and P. Ossenblok and A. Fuster and L. Florack and R. Duits",
note = "Funding Information: Acknowledgements Data provided in part by the HCP, WU-Minn Consortium (PI{\textquoteright}s: D. Van Essen and K. Ugurbil; 1U54MH091657). We thank S. Mari{\"e}n for co-developing the rching Tool, available at https://goo.gl/D5Q7dE (Downloads section). We thank J.M. Mirebeau for his Hamiltonian fast-marching C++-code, available at https://www.math.u-psud.fr/~mirebeau. The research leading to these results has received funding from the European Research Council under the European Community{\textquoteright}s 7-th Framework Programme (FP7/2007-2013) / ERC grant Lie Analysis, agr. nr. 335555. Funding Information: ii. This penalization is based on the FOD. Curves poorly supported by the FOD yield higher distances. Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.",
year = "2019",
doi = "10.1007/978-3-030-05831-9_24",
language = "English",
isbn = "978-3-030-05830-2",
series = "Mathematics and Visualization",
publisher = "Springer, Cham",
pages = "309--321",
editor = "Elisenda Bonet-Carne and Francesco Grussu and Lipeng Ning and Farshid Sepehrband and Tax, {Chantal M.W.}",
booktitle = "Computational Diffusion MRI",
address = "Switzerland",
}