Diattenuation of Brain Tissue and its Impact on 3D Polarized Light Imaging

3D-Polarized Light Imaging (3D-PLI) reconstructs nerve fibers in histological brain sections by measuring their birefringence. This study investigates another effect caused by the optical anisotropy of brain tissue - diattenuation. Based on numerical and experimental studies and a complete analytical description of the optical system, the diattenuation was determined to be below 4 % in rat brain tissue. It was demonstrated that the diattenuation effect has negligible impact on the fiber orientations derived by 3D-PLI. The diattenuation signal, however, was found to highlight different anatomical structures that cannot be distinguished with current imaging techniques, which makes Diattenuation Imaging a promising extension to 3D-PLI.Comment: 32 pages, 15 figure

A Jones matrix formalism for simulating three-dimensional polarized light imaging of brain tissue

The neuroimaging technique three-dimensional polarized light imaging (3D-PLI) provides a high-resolution reconstruction of nerve fibres in human post-mortem brains. The orientations of the fibres are derived from birefringence measurements of histological brain sections assuming that the nerve fibres - consisting of an axon and a surrounding myelin sheath - are uniaxial birefringent and that the measured optic axis is oriented in direction of the nerve fibres (macroscopic model). Although experimental studies support this assumption, the molecular structure of the myelin sheath suggests that the birefringence of a nerve fibre can be described more precisely by multiple optic axes oriented radially around the fibre axis (microscopic model). In this paper, we compare the use of the macroscopic and the microscopic model for simulating 3D-PLI by means of the Jones matrix formalism. The simulations show that the macroscopic model ensures a reliable estimation of the fibre orientations as long as the polarimeter does not resolve structures smaller than the diameter of single fibres. In the case of fibre bundles, polarimeters with even higher resolutions can be used without losing reliability. When taking the myelin density into account, the derived fibre orientations are considerably improved.Comment: 20 pages, 8 figure

Relating Polarized Light Imaging Data Across Scales

Polarized light imaging (PLI) (Axer et al. (2011a,b)) enables scanning of individual histological human brain sections with two independent setups: a large-area polarimeter (LAP, “object space resolution”, which is referred to as “resolution” in the remainder of this abstract: 64 × 64 μm²/px) and a polarizing microscope (PM, resolution: 1.6 × 1.6 μm²/px). While PM images are of high resolution (HR) containing complex information, the LAP provides low resolution (LR) overview-like data. The information contained in an LR image is a mixture of the information of its HR counterpart (Koenderink (1984)). Each resolution yields valuable information, which multiplies if they are combined.Image registration algorithms, for example, handle multiple resolutions (1) in case of several modalities with special metrics, and (2) in multi-resolution approaches (e.g. Trottenberg et al. (2001)) to increase the stability of the optimization process of automatic image registration. In the latter case, the data is coarsened synthetically. Our goal is to directly relate measured HR to LR data of the same object, avoiding artificial intermediate steps.All images show the average light intensity, that is transmitted through a thin brain slice (Axer et al. (2011a,b)), and depict a region from the human occipital pole. The images were manually segmented and smoothed by a Gaussian kernel suitable for noise reduction and adapted to each resolution.We selected octave 2 at LR and octave 7 at HR for SURF extraction (Bay et al. (2006)), where one octave denotes a decrease in resolution by a factor of 2. Features with corresponding scales were matched with FLANN (Muja and Lowe (2009)). Homography estimation from the resulting feature point pairs used RANSAC (Fischler and Bolles (1981)). The homography and a linear interpolation scheme were applied to transfer information from LR to HR and vice versa.Localization of the HR ROI in the LR ROI is plausible (figure 1(B)), while localization in the LAP image fails, because the matched feature point positions in HR and LR do not correspond. Numerical and feature point matching inaccuracies become evident in figure 1(C).The experiments were performed with one HR ROI (figure 1(A)), one LAP ROI (figure 1(B)) and one LAP image. We plan to improve the algorithm and to obtain complete HR data sets for further exploration of the method’s performance.Figure 1. This figure shows input data and results of the experiment. The arrows indicate the flow of information and the color by which it is displayed at its destination. Subfigure (A) shows the down-scaled PM ROI (original size: 20604 px × 17157 px). (B) shows the up-scaled LAP ROI (original size: 916 px × 510 px) with estimated PM ROI location (green frame). Note, that only part of the HR ROI is contained in the LR ROI. Also, most of the fine white structures depicted in (A) vanished due to the low resolution of (B). (C) shows the down-scaled overlay image (original size: 20604 px × 17157 px) of LR data (enclosed in the green frame in (B)) transferred to HR versus PM ROI data of (A), where HR data is labeled green and transferred LR data is labeled red. HR data and transferred LR data were normalized. Numerical and feature point matching inaccuracies become evident. Also, displacement and distortion compared to HR data is visible

A multiscale approach for the reconstruction of the fiber architecture of the human brain based on 3D-PLI

Structural connectivity of the brain can be conceptionalized as a multiscale organization. The present study is built on 3D-Polarized Light Imaging (3D-PLI), a neuroimaging technique targeting the reconstruction of nerve fiber orientations and therefore contributing to the analysis of brain connectivity. Spatial orientations of the fibers are derived from birefringence measurements of unstained histological sections that are interpreted by means of a voxel-based analysis. This implies that a single fiber orientation vector is obtained for each voxel, which reflects the net effect of all comprised fibers. We have utilized two polarimetric setups providing an object space resolution of 1.3 μm/px (microscopic setup) and 64 μm/px (macroscopic setup) to carry out 3D-PLI and retrieve fiber orientations of the same tissue samples, but at complementary voxel sizes (i.e., scales). The present study identifies the main sources which cause a discrepancy of the measured fiber orientations observed when measuring the same sample with the two polarimetric systems. As such sources the differing optical resolutions and diverging retardances of the implemented waveplates were identified. A methodology was implemented that enables the compensation of measured different systems' responses to the same birefringent sample. This opens up new ways to conduct multiscale analysis in brains by means of 3D-PLI and to provide a reliable basis for the transition between different scales of the nerve fiber architecture

Estimating Fiber Orientation Distribution Functions in 3D-Polarized Light Imaging

Research of the human brain connectome requires multiscale approaches derived from independent imaging methods ideally applied to the same object. Hence, comprehensible strategies for data integration across modalities and across scales are essential. We have successfully established a concept to bridge the spatial scales from microscopic fiber orientation measurements based on 3D-Polarized Light Imaging (3D-PLI) to meso- or macroscopic dimensions. By creating orientation distribution functions (pliODFs) from high-resolution vector data via series expansion with spherical harmonics utilizing high performance computing and supercomputing technologies, data fusion with Diffusion Magnetic Resonance Imaging has become feasible, even for a large-scale dataset such as the human brain. Validation of our approach was done effectively by means of two types of datasets that were transferred from fiber orientation maps into pliODFs: simulated 3D-PLI data showing artificial, but clearly defined fiber patterns and real 3D-PLI data derived from sections through the human brain and the brain of a hooded seal