Phase contrast computed tomography (PCCT) provides an effective non-destructive testing tool for weak absorption objects. Limited by the phase stepping principle and radiation dose requirement, sparse-view sampling is usually performed in PCCT, introducing severe artifacts in reconstruction. In this paper, we report a dual-domain (i.e., the projection sinogram domain and image domain) enhancement framework based on deep learning (DL) for PCCT with sparse-view projections. It consists of two convolutional neural networks (CNN) in dual domains and the phase contrast Radon inversion layer (PCRIL) to connect them. PCRIL can achieve PCCT reconstruction, and it allows the gradients to backpropagate from the image domain to the projection sinogram domain while training. Therefore, parameters of CNNs in dual domains are updated simultaneously. It could overcome the limitations that the enhancement in the image domain causes blurred images and the enhancement in the projection sinogram domain introduces unpredictable artifacts. Considering the grating-based PCCT as an example, the proposed framework is validated and demonstrated with experiments of the simulated datasets and experimental datasets. This work can generate high-quality PCCT images with given incomplete projections and has the potential to push the applications of PCCT techniques in the field of composite imaging and biomedical imaging.

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Our objective is to report a new breast phantom that provides the objective assessment for three types of clinical mammography, i.e. digital mammography (DM), contrast-enhanced digital mammography (CEDM), and digital breast tomosynthesis (DBT). The tissue-equivalent materials are used to represent the corresponding tissue, and the layer-by-layer structure with separate regions is designed for image quality assessment of different mammography modes. For DM imaging, substitutes for microcalcifications and fibroglandular tissue of different sizes are used to simulate the conventional breast. For CEDM imaging, the tumor module that can be injected with imaging contrast agents is adopted to distinguish normal tissue and diseased tissue in the dense breast. For DBT imaging, the overlapping breast mass module with multiple layers is designed to perform the layer-by-layer imaging of overlapping tissue. In addition, the quantitative assessment module of image quality is designed based on contrast-to-noise ratio (CNR), modulation transfer function (MTF) and artifact spread function (ASF). This phantom allows image quality to be evaluated objectively for three different types of the clinical mammography, while it provides an effective tool for optimizing the dose-image quality relationship of patients.