arXiv subjects

Felix Matuschke

Publications and source records attributed to Felix Matuschke.

2 recordsLinked to original sources

arXiv2026

3D scattered light imaging: extracting 3D fiber orientations from 1D line profiles in brain imaging

Understanding the 3D fiber architecture of the brain at the microscopic scale is essential for revealing its structural connectivity and function. Polarization-based optical imaging (3D-PLI) techniques enable high-fidelity reconstruction of single nerve fiber orientations but struggle to resolve fiber crossings, which are critical for recovering the full connectome. Scattering-based imaging provides access to the structure factor of three-dimensionally oriented fibers. By probing a fixed scattering angle under multiple azimuthal illumination angles, in-plane fiber orientations and crossings can be recovered using computational scattered light imaging (SLI). However, despite containing 3D information, a theoretical framework to extract full 3D orientations has been lacking. In this talk, a simple analogical approximation of Rayleigh-Gans scattering theory is introduced to extract the 3D orientation of nerve fibers from one-dimensional SLI measurements. The theory is validated using tilted microscopic glass phantoms consisting of 2 um-thick rod lattices fabricated by two-photon lithography. Finally, the method is applied to brain tissue samples and compared with 3D-PLI.

physics.optics↗

arXiv2019

Dense Fiber Modeling for 3D-Polarized Light Imaging Simulations

3D-Polarized Light Imaging (3D-PLI) is a neuroimaging technique used to study the structural connectivity of the human brain at the meso- and microscale. In 3D-PLI, the complex nerve fiber architecture of the brain is characterized by 3D orientation vector fields that are derived from birefringence measurements of unstained histological brain sections by means of an effective physical model. To optimize the physical model and to better understand the underlying microstructure, numerical simulations are essential tools to optimize the used physical model and to understand the underlying microstructure in detail. The simulations rely on predefined configurations of nerve fiber models (e.g. crossing, kissing, or complex intermingling), their physical properties, as well as the physical properties of the employed optical system to model the entire 3D-PLI measurement. By comparing the simulation and experimental results, possible misinterpretations in the fiber reconstruction process of 3D-PLI can be identified. Here, we focus on fiber modeling with a specific emphasize on the generation of dense fiber distributions as found in the human brain's white matter. A new algorithm will be introduced that allows to control possible intersections of computationally grown fiber structures.

physics.comp-ph↗