Abstract-Photonic crystal fibers (PCFs) offer new possibilities of realizing highly birefringent fibers due to a higher intrinsic index contrast compared to conventional fibers. In this letter, we analyze theoretically the levels of birefringence that can be expected using relatively simple PCF designs. While extremely high degrees of birefringence may be obtained for the fibers, we demonstrate that careful design with respect to multimode behavior must be performed. We further discuss the cutoff properties of birefringent PCFs and present experimental results in agreement with theoretical predictions on both single-and multimode behavior and on levels of birefringence.

A discrete tomography algorithm is presented for the reconstruction of grain maps based on X-ray diffraction data. This is the first algorithm for this task, inherently exploiting the discrete structure of grain maps. Gibbs potentials serve to characterize the statistics of the local morphology of the grain boundaries. A Monte Carlo based algorithm is applied as a restoration method for improving the quality of grain maps produced by a classical (non-discrete) tomography algorithm (ART). The quality of the restored maps is demonstrated and quantified by simulation studies. The robustness of the algorithm with respect to the choice of Gibbs potentials is investigated.

FabIO is a Python module written for easy and transparent reading of raw twodimensional data from various X-ray detectors. The module provides a function for reading any image and returning a fabioimage object which contains both metadata (header information) and the raw data. All fabioimage objects offer additional methods to extract information about the image and to open other detector images from the same data series.

A novel algorithm is introduced for fast and nondestructive reconstruction of grain maps from X‐ray diffraction data. The discrete algebraic reconstruction technique (DART) takes advantage of the intrinsic discrete nature of grain maps, while being based on iterative algebraic methods known from classical tomography. To test the properties of the algorithm, three‐dimensional X‐ray diffraction microscopy data are simulated and reconstructed with DART as well as by a conventional iterative technique, namely SIRT (simultaneous iterative reconstruction technique). For 100 × 100 pixel reconstructions and moderate noise levels, DART is shown to generate essentially perfect two‐dimensional grain maps for as few as three projections per grain with running times on a PC in the range of less than a second. This is seen as opening up the possibility for fast reconstructions in connection with in situ studies.

This article presents the Monte Carlo simulation package McXtrace, intended for optimizing X‐ray beam instrumentation and performing virtual X‐ray experiments for data analysis. The system shares a structure and code base with the popular neutron simulation code McStas and is a good complement to the standard X‐ray simulation software SHADOW. McXtrace is open source, licensed under the General Public License, and does not require the user to have access to any proprietary software for its operation. The structure of the software is described in detail, and various examples are given to showcase the versatility of the McXtrace procedure and outline a possible route to using Monte Carlo simulations in data analysis to gain new scientific insights. The studies performed span a range of X‐ray experimental techniques: absorption tomography, powder diffraction, single‐crystal diffraction and pump‐and‐probe experiments. Simulation studies are compared with experimental data and theoretical calculations. Furthermore, the simulation capabilities for computing coherent X‐ray beam properties and a comparison with basic diffraction theory are presented.