MAFA-Uformer: Multi-attention and dual-branch feature aggregation U-shaped transformer for sparse-view CT reconstruction.

Journal: Journal of X-ray science and technology

PMID: 39973771

Abstract

BACKGROUND: Although computed tomography (CT) is widely employed in disease detection, X-ray radiation may pose a risk to the health of patients. Reducing the projection views is a common method, however, the reconstructed images often suffer from streak artifacts.

Authors

Xuan Zhang
Chenyun Fang

School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China.
Zhiwei Qiao

School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China. Electronic address: zqiao@sxu.edu.cn.

Keywords

Algorithms Artifacts Humans Image Processing, Computer-Assisted Neural Networks, Computer Phantoms, Imaging Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39973771)

MAFA-Uformer: Multi-attention and dual-branch feature aggregation U-shaped transformer for sparse-view CT reconstruction.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals