Dual-domain dual-branch residual-learning network for fast noisy sparse-view ultra-low-dose CT reconstruction

Objective.Ultra-low-dose CT (ULDCT) can be achieved by reducing the tube current and employing sparse-view projections, thereby improving patient safety by lowering radiation exposure. However, this strategy inevitably introduces severe aliasing artifacts and increased noise, leading to substantial degradation of image quality. To simultaneously address undersampling-induced artifacts and noise contamination, we propose a dual-domain dual-branch residual-learning network (DR-Net) for high-fidelity ULDCT reconstruction.Approach.The proposed framework first performs edge-preserving sinogram restoration using an improved directional cubic convolution (iDCC) interpolation method, followed by a U-Net optimized with an inner-structure gradient loss to preserve critical edge-gradient information. In the image domain, a dual-branch structure-infiltrated guidance network (DB-SiGN) is designed to extract low- and high-frequency information from the refined reconstruction and the original noisy projections, respectively. The gradient features extracted from the low-frequency branch are used to guide the high-frequency branch, enabling more effective discrimination between true anatomical structures and noise/artifacts. Both branches learn residual mappings between the refined reconstruction and the corresponding normal-dose CT (NDCT) image, and their outputs are adaptively fused through spatial attention weighting to produce the final reconstruction.Main results.Experimental results on both simulated dose-reduction datasets and real CBCT data demonstrate that DR-Net consistently outperforms competing methods in terms of quantitative metrics and visual image quality across all evaluated scenarios. In addition, the proposed method achieves superior and more robust downstream segmentation performance, with reconstructed images exhibiting the highest consistency with NDCT references.Significance.DR-Net establishes a robust and interpretable dual-domain reconstruction framework for ULDCT imaging. By effectively suppressing noise and aliasing artifacts while preserving fine anatomical structures, the proposed method provides a promising solution for safe, reliable, and clinically deployable ULDCT reconstruction.