Phase retrieval based on deep learning in grating interferometer.

Grating interferometry is a promising technique to obtain differential phase contrast images with illumination source of low intrinsic transverse coherence. However, retrieving the phase contrast image from the differential phase contrast image is difficult due to the accumulated noise and artifacts from the differential phase contrast image (DPCI) reconstruction. In this paper, we implemented a deep learning-based phase retrieval method to suppress these artifacts. Conventional deep learning based denoising requires noise/clean image pair, but it is not feasible to obtain sufficient number of clean images for grating interferometry. In this paper, we apply a recently developed neural network called Noise2Noise (N2N) that uses noise/noise image pairs for training. We obtained many DPCIs through combination of phase stepping images, and these were used as input/target pairs for N2N training. The application of the N2N network to simulated and measured DPCI showed that the phase contrast images were retrieved with strongly suppressed phase retrieval artifacts. These results can be used in grating interferometer applications which uses phase stepping method.

Study on dark-field imaging with a laboratory x-ray source: Random stress variation analysis based on x-ray grating interferometry.

The dark-field image (DFI) in a grating interferometer involves the small-angle scattering properties of a material. The microstructure of the material can be characterized by an analysis of the auto-correlation length and the DFI. The feasibility of a DFI in a laboratory x-ray source with grating interferometry has been reported, but a follow-up study is needed. In this study, the random stress distribution was measured in the laboratory environment as an applied study. SiO2 mono-spheres as a cohesive powder with a 0.5 µm particle size were used as the sample. The microstructural changes according to the stresses on the particles were observed by acquiring a DFI along the auto-correlation length. In x-rays, a random two-phase media model was first used to analyze the characteristics of cohesive powder. This study showed that the microstructure of materials and x-ray images could be analyzed in a laboratory environment.

Deep learning for high-resolution and high-sensitivity interferometric phase contrast imaging.

In Talbot-Lau interferometry, the sample position yielding the highest phase sensitivity suffers from strong geometric blur. This trade-off between phase-sensitivity and spatial resolution is a fundamental challenge in such interferometric imaging applications with either neutron or conventional x-ray sources due to their relatively large beam-defining apertures or focal spots. In this study, a deep learning method is introduced to estimate a high phase-sensitive and high spatial resolution image from a trained neural network to attempt to avoid the trade-off for both high phase-sensitivity and high resolution. To realize this, the training data sets of the differential phase contrast images at a pair of sample positions, one of which is close to the phase grating and the other close to the detector, are numerically generated and are used as the inputs for the training data set of a generative adversarial network. The trained network has been applied to the real experimental data sets from a neutron grating interferometer and we have obtained improved images both in phase-sensitivity and spatial resolution.

K-edge-based interior tomography.

Interior tomography reconstructs a region of interest using truncated projection data, but it is subject to the ill-posedness of interior tomography. With the photon-counting detector, K-edge imaging uses data in the low and high energy bins around the K-edge of a contrast agent, and can faithfully recover true image contrast for improved diagnosis. The purpose of this paper is to reconstruct a region of interest inside a patient assuming the existence of a known K-edge material. In this case, there is a significant difference in x-ray attenuation around the K-edge, but these attenuation coefficients are inter-related to guide updating an intermediate reconstruction until a stopping criterion is satisfied. In our study, new interior tomography algorithms were developed without any major computational overhead, and several phantoms were used to validate the algorithms. The proposed methods are advantageous relative to the existing interior tomography algorithms, because of the available spectral information in the form of a known K-edge material.