MIST-net: Multi-domain Integrative Swin Transformer network for Sparse-View CT Reconstruction

The deep learning-based tomographic image reconstruction have been attracting much attention among these years. The sparse-view data reconstruction is one of typical underdetermined inverse problems, how to reconstruct high-quality CT images from dozens of projections is still a challenge in practice. To address this challenge, in this article we proposed a Multi-domain Integrative Swin Transformer network (MIST-net). First, the proposed MIST-net incorporated lavish domain features from data, residual-data, image, and residual-image using flexible network architectures. Here, the residual-data and residual-image domains network components can be considered as the data consistency module to eliminate interpolation errors in both residual data and image domains, and then further retain image details. Second, to detect the image features and further protect image edge, the trainable Sobel Filter was incorporated into the network to improve the encode-decode ability. Third, with the classical Swin transformer, we further designed the high-quality reconstruction transformer (i.e., Recformer) to improve the reconstruction performance. The Recformer inherited the power of Swin transformer to capture the global and local features of the reconstructed image. The experiments on the numerical datasets with 48 views demonstrated our proposed MIST-net provided higher reconstructed image quality with small feature recovery and edge protection than other competitors including the advanced unrolled networks. The quantitative results show that our MIST-net also obtained the best performance. The trained network was transferred to the real cardiac CT dataset with 48 views, the reconstruction results further validated the advantages of our MIST-net and further demonstrated the good robustness of our MIST in clinical applications.