Decreasing projection views to lower X-ray radiation dose usually leads to severe streak artifacts. To improve image quality from sparse-view data, a Multi-domain Integrative Swin Transformer network (MIST-net) was developed in this article. First, MIST-net incorporated lavish domain features from data, residual-data, image, and residual-image using flexible network architectures, where residual-data and residual-image sub-network was considered as data consistency module to eliminate interpolation and reconstruction errors. Second, a trainable edge enhancement filter was incorporated to detect and protect image edges. Third, a high-quality reconstruction Swin transformer (i.e., Recformer) was designed to capture image global features. The experiment results on numerical and real cardiac clinical datasets with 48-views demonstrated that our proposed MIST-net provided better image quality with more small features and sharp edges than other competitors.
@article{arxiv.2111.14831,
title = {Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction},
author = {Jiayi Pan and Heye Zhang and Weifei Wu and Zhifan Gao and Weiwen Wu},
journal= {arXiv preprint arXiv:2111.14831},
year = {2022}
}