lternating Source-Detector Array Stationary CT System and Its Reconstruction

In this paper, we propose a novel design for a stationary CT system, termed the Alternating Source-Detector Array stationary CT (ASDA-sCT). The ASDA-sCT system comprises an array of miniature carbon nanotube X-ray sources and a detector array strategically positioned in the gaps between sources. To minimize projection loss caused by ray path obstruction, the X-ray sources are distributed within a short-scan trajectory that takes advantage of the fan-beam symmetry. After interpolation-based restoration of the discontinuities, CT images can be directly reconstructed using the filtered backprojection (FBP) algorithm with Parkers weighting function. We further investigate the influence of the number of X-ray sources on the reconstruction quality of the ASDA-sCT system and determine the optimal source number for different X-ray exit window sizes. However, the limited number of sources and the interpolation errors introduced during sinogram restoration remain critical barriers to achieving high-quality image reconstruction. To tackle these issues, we propose a tailored triple-stage dual-domain cascade neural network (TSDDC-Net), which incorporates prior knowledge to correct interpolation errors in the sinogram and compensate for the missing projection views. In the projection domain, we introduce a novel multi-scale deformable convolution module (DFInception) that enhances feature extraction and improves the accuracy of sinogram refinement. In the image domain, a dual-encoder architecture is employed to independently extract features from the initial CT image reconstructed from raw interpolated projections and from the refined CT image reconstructed using the corrected sinogram. Ultimately, the well-designed deep learning model significantly enhances the quality of the reconstructed images. Experiments conducted on the Shepp-Logan phantom and abdominal CT datasets demonstrate the promising potential of the ASDA-sCT system for practical applications.