HD-DCDM: Hybrid-domain network for limited-angle computed tomography with deconvolution and conditional diffusion model

Abstract

Limited-angle computed tomography (LACT) has gained significant attention in recent years due to its wide range of applications. Despite the numerous algorithms proposed to improve imaging quality, reconstructing fine details remains a challenging problem. In this paper, we propose a novel hybrid domain framework that combines classical methods and learning-based methods to address this challenge. Our framework decomposes the solution of the least-squares problem into back-projection and deconvolution steps, leading to a significant improvement in reconstruction quality. Furthermore, we employ a conditional diffusion model to further fine-tune the reconstruction results, simultaneously preserving data consistency and enhancing the realness of the reconstructed images. The effectiveness of the proposed framework is evaluated using the Helsinki Tomography Challenge 2022 (HTC 2022) dataset. Comparative evaluations demonstrate that our framework outperforms previous methods in both visual quality and quantitative measures. These findings highlight the potential of the proposed framework in improving LACT reconstruction and offer valuable insights for advancing imaging techniques in various fields.