TD-Net: A Tri-domain network for sparse-view CT reconstruction
Xinyuan Wang Changqing Su Bo Xiong
Sparse-view CT reconstruction, aimed at reducing X-ray radiation risks,
frequently suffers from image quality degradation, manifested as noise and
artifacts. Existing post-processing and dual-domain techniques, although
effective in radiation reduction, often lead to over-smoothed results,
compromising diagnostic clarity. Addressing this, we introduce TD-Net, a
pioneering tri-domain approach that unifies sinogram, image, and frequency
domain optimizations. By incorporating Frequency Supervision Module(FSM),
TD-Net adeptly preserves intricate details, overcoming the prevalent
over-smoothing issue. Extensive evaluations demonstrate TD-Net's superior
performance in reconstructing high-quality CT images from sparse views,
efficiently balancing radiation safety and image fidelity. The enhanced
capabilities of TD-Net in varied noise scenarios highlight its potential as a
breakthrough in medical imaging.