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Propagation-based X-ray phase–contrast computed tomography (PB-PCCT) can serve as an effective tool for studying organ function and pathologies. However, it usually suffers from a high radiation dose due to the long scan time. To alleviate this problem, we propose a deep learning reconstruction framework for PB-PCCT with sparse-view projections. The framework consists of dual-path deep neural networks, where the edge detection, edge guidance, and artifact removal models are incorporated into two subnetworks. It is worth noting that the framework has the ability to achieve excellent performance by exploiting the data-based knowledge of the sample material characteristics and the model-based knowledge of PB-PCCT. To evaluate the effectiveness and capability of the proposed framework, simulations and real experiments were performed. The results demonstrated that the proposed framework could significantly suppress streaking artifacts and produce high-contrast and high-resolution computed tomography images.

In-line X-ray phase-contrast computed tomography typically contains two independent procedures: phase retrieval and computed tomography reconstruction, in which multi-material and streak artifacts are two important problems. To address these problems simultaneously, an accelerated 3D iterative image reconstruction algorithm is proposed. It merges the above-mentioned two procedures into one step, and establishes the data fidelity term in raw projection domain while introducing 3D total variation regularization term in image domain. Specifically, a transport-of-intensity equation (TIE)-based phase retrieval method is updated alternately for different areas of the multi-material sample. Simulation and experimental results validate the effectiveness and efficiency of the proposed algorithm.

A ternary Bi/AgBiS2/P25 has been successfully designed by a one-step solvothermal treatment for enhanced photocatalytic CO2 reduction. Bi/AgBiS2 decoration enhances carriers separation and transfer efficiency of P25 by constructing AgBiS2/P25 heterojunction and loading Bi as electron receiver. Besides, Bi/AgBiS2 decoration contributes to enhanced light-harvesting capability and high surface area of composite, both of which favor CO2 reduction. More importantly, increased OH and enhanced CO adsorption efficiency on Bi/AgBiS2/P25 surface could enhance CH4 production by providing more available protons and stabilizing more key intermediates for CH4 formation. The CH4 production was improved from 0.23 μmol/g/h of P25 to 4.31 μmol/g/h of Bi/AgBiS2/P25, and CO production from 3.52 μmol/g/h to 6.37 μmol/g/h, respectively, which have surpassed most TiO2 based works for CO2 photoreduction. Additionally, photo-thermal synergy can further enhance photocatalytic performance.