A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often. Conventional reconstruction algorithms are not easy to deal with incomplete data. They are usually involved with complicated parameter selection operations, also sensitive to noise and time-consuming. In this paper, we reported a new deep learning reconstruction framework for incomplete data DPC-CT. It is the tight coupling of the deep learning neural network and DPC-CT reconstruction algorithm in the phase-contrast projection sinogram domain. The estimated result is the complete phase-contrast projection sinogram not the artifacts caused by the incomplete data. After training, this framework is determined and can reconstruct the final DPC-CT images for a given incomplete phase-contrast projection sinogram. Taking the sparse-view DPC-CT as an example, this framework has been validated and demonstrated with synthetic and experimental data sets. Embedded with DPC-CT reconstruction, this framework naturally encapsulates the physical imaging model of DPC-CT systems and is easy to be extended to deal with other challengs. This work is helpful to push the application of the state-of-the-art deep learning theory in the field of DPC-CT

Stability Constants of Cobalt(II) and Copper(II) Complexes with 3-[(o-Carboxy-p-nitrobenzene)azo]chromotropic Acid and Selective Determination of Copper(II) by Competition Coordination

A method for selective determination of copper(II) based on the reactions of copper(II) or cobalt(II) with 3-[(o-carboxy-p-nitrobenzene)azo]chromotropic acid (CNBAC) at pH = 11.4 was developed. Results have shown that two complexes, Co(CNBAC)2 and Cu(CNBAC), were formed, whose cumulative stability constants were 5.22 × 109 and 7.61 × 105 dm3 mol–1, respectively, and their molar absorption coefficients were 1.19 × 104 and 2.12 × 104 dm3 mol–1 cm–1 at 610 nm. The competition coordination of CuII and CoII with CNBAC was applied for selective determination of CuII by the spectral correction technique. In the absence of any masking reagent, the recommended method was selective and was applied for quantitative determination of copper(II) in river and waste water samples