Chest X-ray fluoroscopy is a commonly used medical imaging method, which has a wide range of applications in the diagnosis of lung diseases and other fields. However, due to low contrast and relatively close linear attenuation coefficients, some early small lesions are difficult to detect in time. Using the X-ray fluorescent effect of high atomic number metal elements and metal atom-containing agents that can be enriched in the lesion, the fluoroscopy signal and the fluorescent signal emitted by the metal atoms can be detected at the same time during the fluoroscopy, and the images of the two can be integrated, which can theoretically enhance the contrast between the lesion and the surrounding tissue. Based on GEANT4, this paper conducts Monte Carlo simulations to explore the feasibility and enhancement effects of three enhancement schemes: the pencil beam spot scanning method, cone-beam collimation method, and slit scanning method, and discusses the specific geometric structure and material selection.

Objective. Motion artifacts remain a significant challenge in cardiac CT imaging, often impairing the accurate detection and diagnosis of cardiac diseases. These artifacts result from involuntary cardiac motion, and traditional mitigation methods typically rely on retrospective rescans, which increase radiation exposure and are less effective for patients with irregular heart rhythms. In this study, we proposed a deep learning-based end-to-end reconstruction framework for dynamic cardiac imaging using single-beat rapid CT scanning. Approach. The method used common cardiac CT projections and applied a sliding-window approach to divide the projection data into overlapping short-scan intervals centered on specific cardiac phases. Each short-scan interval was first reconstructed and then processed through a denoising network, before being fed into the registration module to compute deformation vector fields (DVFs). These DVFs were then used to perform motion-compensated reconstruction. The denoising and registration networks were trained end-to-end by minimizing the difference between the reconstructed images and ground truth in a supervised manner. The model was trained using simulated projection data from 30 real patients and validated on simulated datasets from different institutions and XCAT-generated continuous phantoms. Main results. Experimental results showed that the proposed method effectively reduced motion artifacts and restored key anatomical structures such as coronary arteries. On high-resolution test cases, SSIM improved from 0.7234 to 0.7795, peak signal-to-noise ratio from 35.40 to 37.58, and root mean square error (HU) decreased from 63.98 to 49.28. Additional evaluations showed consistent improvements in segmentation accuracy, with Dice similarity coefficient scores for the left ventricle, coronary arteries, and calcified plaques increasing from 0.8025, 0.7347, and 0.5966 to 0.9614, 0.8811, and 0.7774, respectively. Significance. By relying solely on single-cycle scan data and placing no explicit restrictions on heart rate, the method demonstrated strong potential for generalizability and wider clinical applications.