Abstract
Diffeomorphic registration of diffusion MRI data is a non-convex optimization problem which raises issues both with respect to the choice of a model describing the dMRI data and the associated optimization method used to warp non-scalar images obtained from this model. In this paper, we take into account the full information available from the diffusion-weighted signal by using the local SHORE Mean Apparent Propagator (MAP) model. A discrete representation of the SHORE MAPs on a multiple-shell sampling of the displacement space is introduced, simplifying the comparison between MAPs which are no longer in different local tensor frames and enabling to perform a complete reorientation of the MAPs at each step of the registration. The diffeomorphic transformation is first optimized at the voxel scale using dynamic programming on a minimum spanning tree before taking advantage of the continuous diffeomorphic demons registration algorithm to match dMRI data at finer scales. The efficacy of this registration approach was assessed using diffusion MR datasets acquired on two healthy subjects to evaluate its capability to reach an optimal alignment of several well-known long white matter bundles.
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This work was partially funded by the European FET Flagship Human Brain Project (SP2) FP7-ICT-2013-FET-F/604102.
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Ginsburger, K., Poupon, F., Teillac, A., Mangin, JF., Poupon, C. (2018). Diffeomorphic Registration of Diffusion Mean Apparent Propagator Fields Using Dynamic Programming on a Minimum Spanning Tree. In: Kaden, E., Grussu, F., Ning, L., Tax, C., Veraart, J. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-73839-0_6
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