High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Lensless Computational Imaging Technology Using Deep Convolutional Network

Within the framework of Internet of Things or when constrained in limited space, lensless imaging technology provides effective imaging solutions with low cost and reduced size prototypes. In this paper, we proposed a method combining deep learning with lensless coded mask imaging technology. After replacing lenses with the coded mask and using the inverse matrix optimization method to reconstruct the original scene images, we applied FCN-8s, U-Net, and our modified version of U-Net, which is called Dense-U-Net, for post-processing of reconstructed images. The proposed approach showed supreme performance compared to the classical method, where a deep convolutional network leads to critical improvements of the quality of reconstruction.

Progressive steepening of the SNR RX J1713.7–3946 X-ray spectrum from XMM–Newton to INTEGRAL

ABSTRACT In this work, we present the first detailed analysis of the supernova remnant RX J1713.7–3946 in the hard X-ray energy range with the Imager on Board the INTEGRAL Satellite (IBIS) coded-mask telescope onboard the INTEGRAL observatory. The shell-type morphology of the entire remnant is mapped in hard X-rays for the first time and significantly detected up to 50 keV. The IBIS sky image of RX J1713.7–3946, accumulated over 14 yr of operations, demonstrates two extended hard X-ray sources. These sources are spatially consistent with north-west and south-west rims of RX J1713.7–3946 and are also clearly visible at energies below 10 keV with XMM–Newton. This points to a single emission mechanism operating in soft and hard X-rays. The INTEGRAL 17–120 keV spectrum of RX J1713.7–3946 is characterized by a power-law continuum with the photon index of Γ ≈ 3 that is significantly softer than Γ ≈ 2 determined by XMM–Newton in the 1–10 keV energy range, suggesting a progressive steepening of the spectrum with the energy.