Abstract
Three-dimensional Polarized Light Imaging (3D-PLI) is a promising technique to reconstruct the nerve fiber architecture of human post-mortem brains from birefringence measurements of histological brain sections with micrometer resolution. To better understand how the reconstructed fiber orientations are related to the underlying fiber structure, numerical simulations are employed. Here, we present two complementary simulation approaches that reproduce the entire 3D-PLI analysis: First, we give a short review on a simulation approach that uses the Jones matrix calculus to model the birefringent myelin sheaths. Afterwards, we introduce a more sophisticated simulation tool: a 3D Maxwell solver based on a Finite-Difference Time-Domain algorithm that simulates the propagation of the electromagnetic light wave through the brain tissue. We demonstrate that the Maxwell solver is a valuable tool to better understand the interaction of polarized light with brain tissue and to enhance the accuracy of the fiber orientations extracted by 3D-PLI.
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Notes
- 1.
The function \(\mathrm{atan2}(x,y)\) denotes the angle (in radians) between the positive x-axis and the point (x, y). The angle is positive for \(y>0\) and negative for \(y<0\).
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Acknowledgments
Our work has been supported by the Helmholtz Association portfolio theme ‘Supercomputing and Modeling for the Human Brain’, by the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 604102 (Human Brain Project), and partially by the National Institutes of Health under grant agreement No. R01MH 092311.
We gratefully acknowledge the computing time granted by the JARA-HPC Vergabegremium and provided on the JARA-HPC Partition part of the supercomputer JUQUEEN [18] at Forschungszentrum Jülich.
We would like to thank M. Cremer, Ch. Schramm, and P. Nysten for the preparation of the histological brain sections.
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Menzel, M., Axer, M., De Raedt, H., Michielsen, K. (2016). Finite-Difference Time-Domain Simulation for Three-Dimensional Polarized Light Imaging. In: Amunts, K., Grandinetti, L., Lippert, T., Petkov, N. (eds) Brain-Inspired Computing. BrainComp 2015. Lecture Notes in Computer Science(), vol 10087. Springer, Cham. https://doi.org/10.1007/978-3-319-50862-7_6
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