TD-Net: A Tri-domain network for sparse-view CT reconstruction
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Abstract
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.
