Multi-domain Integrative Swin Transformer network for Sparse-View Tomographic Reconstruction

TL;DR

This paper introduces MIST-net, a novel multi-domain Swin Transformer framework that enhances sparse-view tomographic images by integrating domain features, residual data, and edge enhancement, significantly reducing artifacts.

Contribution

The paper proposes a multi-domain integrative Swin Transformer network (MIST-net) with residual modules and edge enhancement for improved sparse-view tomographic reconstruction.

Findings

MIST-net outperforms existing methods in image quality.

It captures small features and sharp edges effectively.

Demonstrated on clinical datasets with 48 views.

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.