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- W4200633977 abstract "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." @default.
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- W4200633977 date "2021-01-01" @default.
- W4200633977 modified "2023-09-25" @default.
- W4200633977 title "Multi-Domain Integrative Swin Transformer Network for Sparse-View Tomographic Reconstruction" @default.
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- W4200633977 doi "https://doi.org/10.2139/ssrn.3991087" @default.
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