After registration of the imaging data of two brains, homologous anatomical structures are expected to overlap better than before registration. Diffusion magnetic resonance imaging (dMRI) techniques and tractography techniques provide a representation of the anatomical connections in the white matter, as hundreds of thousands of streamlines, forming the tractogram. The literature on methods for aligning tractograms is in active development and provides methods that operate either from voxel information, e.g. fractional anisotropy, orientation distribution function, T1-weighted MRI, or directly from streamline information. In this work, we align streamlines using the linear assignment problem (LAP) and propose a method to reduce the high computational cost of aligning whole brain tractograms. As further contribution, we present a comparison among some of the freely-available linear and nonlinear tractogram alignment methods, where we show that our LAP-based method outperforms all others. In discussing the results, we show that a main limitation of all streamline-based nonlinear registration methods is the computational cost and that addressing such problem may lead to further improvement in the quality of registration.

Nonlinear alignment of whole tractograms with the linear assignment problem / Olivetti, E.; Gori, P.; Astolfi, P.; Bertò, G.; Avesani, P.. - 12120:(2020), pp. 3-11. ((Intervento presentato al convegno 9th International Workshop on Biomedical Image Registration, WBIR 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-50120-4_1].

Nonlinear alignment of whole tractograms with the linear assignment problem

Olivetti E.;Astolfi P.;Bertò G.;
2020

Abstract

After registration of the imaging data of two brains, homologous anatomical structures are expected to overlap better than before registration. Diffusion magnetic resonance imaging (dMRI) techniques and tractography techniques provide a representation of the anatomical connections in the white matter, as hundreds of thousands of streamlines, forming the tractogram. The literature on methods for aligning tractograms is in active development and provides methods that operate either from voxel information, e.g. fractional anisotropy, orientation distribution function, T1-weighted MRI, or directly from streamline information. In this work, we align streamlines using the linear assignment problem (LAP) and propose a method to reduce the high computational cost of aligning whole brain tractograms. As further contribution, we present a comparison among some of the freely-available linear and nonlinear tractogram alignment methods, where we show that our LAP-based method outperforms all others. In discussing the results, we show that a main limitation of all streamline-based nonlinear registration methods is the computational cost and that addressing such problem may lead to further improvement in the quality of registration.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Switzerland
Springer
978-3-030-50119-8
978-3-030-50120-4
Olivetti, E.; Gori, P.; Astolfi, P.; Bertò, G.; Avesani, P.
Nonlinear alignment of whole tractograms with the linear assignment problem / Olivetti, E.; Gori, P.; Astolfi, P.; Bertò, G.; Avesani, P.. - 12120:(2020), pp. 3-11. ((Intervento presentato al convegno 9th International Workshop on Biomedical Image Registration, WBIR 2020 tenutosi a svn nel 2020 [10.1007/978-3-030-50120-4_1].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/296075
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