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, modulation transfer function and artifact spread function. 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.

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.

In this letter, we propose a novel lightweight X-ray image contraband segmentation network, XSNet, which integrates State Space Models (SSM) with Convolutional Neural Networks (CNNs) to achieve a significant trade-off between segmentation accuracy and lightweight design for computer-aided X-ray security check. The model is built based on the encoder-decoder framework. Specifically, we design an Multi-scale Convolution Fusion (MCF) block for multi-scale information extraction and a Dual-branch State Space Model (DSSM) block to relieve the bias caused by the imbalance of single branch structure in feature extraction and maintain the capabilities of SSM in modeling long range pixel dependencies. In addition, we present two versions of the model in two different sizes called XSNet-s and XSNet-l respectively. The quantitative and qualitative evaluations on the public PIDray and PIXray datasets both show the superiority of two models in terms of mean Intersection over Union (mIoU) and FLOPs.

This paper introduces a simulation process for X-ray imaging systems, emphasizing the optimization of the objective function through critical parameter analysis. The study explores mathematical modeling and computer simulations to bridge the gap between simulated X-ray images and their real-world counterparts, discusses the impact of the properties of X-ray sources, detectors, and imaged objects, as well as their interplay, on the fidelity of X-ray simulation images, aiming to provide valuable insights for advancing X-ray technology applications in both the medical and industrial sectors.

Traditional computed tomography (CT) systems are cumbersome to transport and thus difficult to apply in field environments or space-constrained scenarios, which necessitates the development of rapid reconstruction techniques for portable cone-beam CT (CBCT) systems. However, the portability and temporary assembly nature of such systems pose significant challenges to high-precision spatial positioning, which has become a core bottleneck limiting reconstruction quality. Conventional calibration methods rely on complex, high-cost stereoscopic phantoms, with inherent drawbacks including prolonged calibration time (1–2 hours) and poor portability. These methods fail to meet the on-site rapid deployment requirements of portable systems and cannot satisfy the multi-dimensional geometric parameter calibration needs. To overcome this limitation, this study draws on machine vision calibration principles to propose, for the first time, an equivalent geometric model adapted to the 7-parameter assembled portable CT system. Furthermore, an industrial CT geometric parameter calibration method based on a circular array planar calibration board is developed, and the accuracy of the proposed model and algorithm is verified through reconstruction experiments. Experimental results show that the proposed method yields sub-pixel reprojection error and higher reconstruction resolution than conventional 3-parameter industrial CT, with total calibration data acquisition within 5 minutes, remarkably outperforming traditional stereoscopic phantom-based methods. Its accuracy and efficiency satisfy the on-site calibration requirements of portable industrial CT. Featuring low cost and fast deployment, it enables traditional portable DR equipment to be upgraded to CT capability, which alleviates the high-cost bottleneck of portable CT and provides key technical support for its market promotion and application extension.