End-to-end deep learning for interior tomography with low-dose x-ray CT
To address the strong coupling between cupping artifacts and quantum noise caused by truncated projections in low-dose X-ray CT, this paper proposes a dual-domain end-to-end deep learning framework: denoising in the image domain and truncation-aware projection data extrapolation in the sinogram domain, synergistically enabling high-fidelity interior reconstruction. We introduce the novel “dual-domain decoupled modeling” paradigm, overcoming the fundamental limitation of single-domain CNNs in disentangling coupled artifacts. To our knowledge, this is the first work to demonstrate that sinogram-domain CNNs outperform state-of-the-art image-domain methods under combined truncation and low-dose conditions. The network architecture is theoretically grounded in deep convolutional principles and jointly optimizes two parallel branches. Experiments show significant improvements over image-domain SOTA methods in PSNR and SSIM; sinogram-domain reconstruction accuracy increases by over 15%; cupping artifacts and noise are effectively suppressed.