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Low-dose CT imaging using a regularization-enhanced efficient diffusion probabilistic model
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DOI：10.1002/mp.70626.png)
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Abstract
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Low-dose CT (LDCT) imaging reduces patient radiation exposure but introduces elevated noise levels that degrade image quality and undermine downstream clinical tasks such as diagnosis and quantitative analysis. Existing denoising approaches often require extensive diffusion steps that impede real‑time clinical applicability.
Keywords:
denoising
diffusion probabilistic model
learned perceptual image patch similarity
low-dose CT
perceptual regularization
residual-shifting
Swin U-Net
total variation loss
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