AI-Enhanced Hybrid Optimization and Deep Network Architecture for Noise Reduction in Low-Dose Medical Imaging

Abstract

Low-dose computed tomography (LDCT) is a significant non-invasive imaging modality for disease diagnosis in early stages and clinical oncology. However, the reduction of radiation dose unavoidably introduces severe quantum noise, photon starvation and Poisson-Gaussian noise, which degrade contrast-to-noise ratio (CNR) and obscure subtle anatomical details. Recent advances in Artificial Intelligence (AI) have shown great promise in medical image restoration. However, pure deep learning methods often suffer from over-smoothing of fine structures and poor interpretability, while traditional non-convex variational models can preserve global edges, but are sensitive to the choice of parameters and produce staircasing artifacts. We propose an AI-empowered hybrid restoration framework that combines non-convex Total Variation (TV) optimization and a deep convolutional residual network within the Plug-and-Play (PnP) Alternating Direction Method of Multipliers (ADMM) framework to enjoy the complementary merits of the two paradigms. The AI based deep residual network can learn complex noise features and image priors efficiently. The optimization part keeps the structural fidelity and ensures the stable reconstruction. The proposed framework is tested on clinical lung CT slices from LIDC-IDRI benchmark dataset, and the experimental results show that the proposed framework achieves 33.97dB of Peak Signal-to-Noise Ratio (PSNR) and 0.918 of Structural Similarity Index Measure (SSIM) at noise level of σ=25. The experimental results show that the proposed AI-enabled hybrid model can better preserve structure edges, recover fine anatomical details and suppress noise compared with the traditional optimization methods and deep learning alone, which shows the effectiveness for low-dose medical image denoising.