Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction
This work addresses image quality issues in medical imaging for reduced radiation dose scenarios, but it is incremental as it builds on existing transformer and domain integration methods.
The paper tackled the problem of severe streak artifacts in sparse-view tomographic reconstruction by developing a Multi-domain Integrative Swin Transformer network (MIST-net), which improved image quality with more small features and sharp edges on numerical and real cardiac clinical datasets with 48-views.
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.