BackgroundInterior tomography is a crucial technique in computed tomography (CT) that aims to minimize radiation exposure by limiting X-ray imaging to the region of interest (ROI) while maintaining diagnostic accuracy. However, traditional reconstruction algorithms often suffer from severe cupping artifacts caused by data truncation, which significantly degrades image quality.ObjectiveThis study aims to develop a parallel network that effectively integrates information between the projection and image domains to improve interior tomography reconstruction.MethodsIn this paper, we propose an end-to-end deep learning framework, the Two-Module Parallel Dual-Domain Network (TPDDN), which consists of two key modules. The Initial Restoration Module generates high-quality prior sinograms and images, providing a robust foundation for subsequent processing and effectively mitigating the impact of data truncation. The Interactive Fusion Module, the core of the network, employs two parallel and interactive branches that operate simultaneously on the projection and image domains. These branches enable bidirectional feature interaction and information fusion, significantly enhancing the accuracy and quality of the reconstructed images.ResultsExtensive experiments were conducted under both normal-dose and high-dose noise conditions to evaluate the performance of TPDDN. The results demonstrate that TPDDN achieves superior qualitative and quantitative performance compared to existing representative methods.ConclusionsThe proposed TPDDN offers a robust and effective approach for interior tomography reconstruction by synergistically integrating information from both the projection and image domains. It effectively suppresses cupping artifacts and enhances reconstructed image quality under both normal-dose and high-noise conditions, demonstrating promising potential for safer and more accurate diagnostic imaging.
Zhao et al. (Thu,) studied this question.