Dual-domain Attention-based Deep Network for Sparse-view CT Artifact Reduction

TL;DR

This paper introduces DDANet, a dual-domain attention-based deep network that effectively reduces streaking artifacts and preserves structures in sparse-view CT images, enhancing image quality while lowering radiation dose.

Contribution

The novel dual-domain attention mechanism in DDANet enables independent feature extraction and effective fusion, improving artifact removal and structural preservation in sparse-view CT reconstruction.

Findings

Robust removal of streaking artifacts demonstrated in simulations and experiments.

Maintains fine structural details in reconstructed CT images.

Outperforms existing methods in artifact reduction and image quality.

Abstract

Due to the wide applications of X-ray computed tomography (CT) in medical imaging activities, radiation exposure has become a major concern for public health. Sparse-view CT is a promising approach to reduce the radiation dose by down-sampling the total number of acquired projections. However, the CT images reconstructed by this sparse-view imaging approach suffer from severe streaking artifacts and structural information loss. In this work, an end-to-end dual-domain attention-based deep network (DDANet) is proposed to solve such an ill-posed CT image reconstruction problem. The image-domain CT image and the projection-domain sinogram are put into the two parallel sub-networks of the DDANet to independently extract the distinct high-level feature maps. In addition, a specified attention module is introduced to fuse the aforementioned dual-domain feature maps to allow complementary optimizations of removing the streaking artifacts and mitigating the loss of structure. Numerical simulations, anthropomorphic thorax phantom and in vivo pre-clinical experiments are conducted to verify the sparse-view CT imaging performance of the DDANet. Results demonstrate that this newly developed approach is able to robustly remove the streaking artifacts while maintaining the fine structures. As a result, the DDANet provides a promising solution in achieving high quality sparse-view CT imaging.