Phase retrieval in propagation-based X-ray imaging beyond the limits of transport of intensity and contrast transfer function approaches.

We derive a phase retrieval formula for propagation-based phase contrast X-ray imaging that does not require weakly attenuating objects or short propagation distances. It is directly applicable to both single- and multiple-distance scenarios. We show the validity conditions and study the error of the underlying mutual intensity approximation, which uses the common assumptions of weak phase shift variations and phase-attenuation duality. The approximation generalizes those behind the transport of intensity (TIE) and contrast transfer function (CTF) models, and it approaches them when their respective additional assumptions are satisfied. When they are not, it clearly outperforms them, which we show both theoretically and practically on synthetic and measured data.

Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning.

Shot noise is a critical issue in radiographic and tomographic imaging, especially when additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper presents a method for improving the quality of noisy multi-channel imaging datasets, such as data from time or energy-resolved imaging, by exploiting structural similarities between channels. To achieve that, we broaden the application domain of the Noise2Noise self-supervised denoising approach. The method draws pairs of samples from a data distribution with identical signals but uncorrelated noise. It is applicable to multi-channel datasets if adjacent channels provide images with similar enough information but independent noise. We demonstrate the applicability and performance of the method via three case studies, namely spectroscopic X-ray tomography, energy-dispersive neutron tomography, and in vivo X-ray cine-radiography.

Evolution of flexible biting in hyperdiverse parasitoid wasps.

One key event in insect evolution was the development of mandibles with two joints, which allowed powerful biting but restricted their movement to a single degree of freedom. These mandibles define the Dicondylia, which constitute over 99% of all extant insect species. It was common doctrine that the dicondylic articulation of chewing mandibles remained unaltered for more than 400 million years. We report highly modified mandibles overcoming the restrictions of a single degree of freedom and hypothesize their major role in insect diversification. These mandibles are defining features of parasitoid chalcid wasps, one of the most species-rich lineages of insects. The shift from powerful chewing to precise cutting likely facilitated adaptations to parasitize hosts hidden in hard substrates, which pose challenges to the emerging wasps. We reveal a crucial step in insect evolution and highlight the importance of comprehensive studies even of putatively well-known systems.