Derivation of Fiber Orientations From Oblique Views Through Human Brain Sections in 3D-Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) enables high-resolution three-dimensional mapping of the nerve fiber architecture in unstained histological brain sections based on the intrinsic birefringence of myelinated nerve fibers. The interpretation of the measured birefringent signals comes with conjointly measured information about the local fiber birefringence strength and the fiber orientation. In this study, we present a novel approach to disentangle both parameters from each other based on a weighted least squares routine (ROFL) applied to oblique polarimetric 3D-PLI measurements. This approach was compared to a previously described analytical method on simulated and experimental data obtained from a post mortem human brain. Analysis of the simulations revealed in case of ROFL a distinctly increased level of confidence to determine steep and flat fiber orientations with respect to the brain sectioning plane. Based on analysis of histological sections of a human brain dataset, it was demonstrated that ROFL provides a coherent characterization of cortical, subcortical, and white matter regions in terms of fiber orientation and birefringence strength, within and across sections. Oblique measurements combined with ROFL analysis opens up new ways to determine physical brain tissue properties by means of 3D-PLI microscopy

A least squares approach for the reconstruction of nerve fiber orientations from tiltable specimen experiments in 3D-PLI

3D-Polarized Light Imaging has become a unique technique to study the fiber architecture of unstained brain sections at the meso- and microscale. It exploits the intrinsic birefringence of nerve fibers which is measured with a customized Polarimeter in which the brain section is placed on a tiltable specimen stage. So far, a computationally fast analytical method based on the discrete Fourier transformation to analyze the data acquired with the tiltable specimen stage has been used. In this study, we propose a new algorithm based on a fitting approach which provides an improved stability against measurement noise resulting in a more realistic orientation interpretation, in particular for low signals. For the first time, it is demonstrated how fiber courses at the boundary of white and grey matter can robustly be reconstructed with 3D-PLI. This significantly improves the reliability of mapping the cortex based on 3D-PLI data