Low-Dose CT Imaging Using a Regularization-Enhanced Efficient Diffusion Probabilistic Model.

ABSTRACT:

Background

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.

Purpose

To address this challenge, we propose a regularization-enhanced efficient diffusion probabilistic model (RE-EDPM), a rapid and high-fidelity denoising framework that incorporates residual guidance between low dose and full dose CT scans and employs hybrid perceptual and total variation regularization to preserve anatomical fidelity and diagnostic quality.

Methods

RE-EDPM incorporates a residual-shifting mechanism into the forwar

SUBMITTER: Li Q 

PROVIDER: S-EPMC12636755 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature