A dual-interactive fusion network for low-dose CT image denoising

Low-dose computed tomography (LDCT) has been widely adopted in clinical imaging to reduce radiation exposure. However, the inherent quantum noise and streaking artifacts in LDCT markedly degrade image quality, thereby compromising diagnostic accuracy.

While conventional model-based iterative denoising (MBIR) approaches effectively mitigate noise via rigorous physical modeling, their adoption is hindered by substantial computational overhead. Deep learning-based approaches demonstrate strong denoising capabilities but face challenges in generalizing across imaging scanners and protocols.

In this study, we propose the Dual-Interactive Fusion Network framework (DIFNet) for LDCT images, integrating the Dual-Phase Denoising Architecture (DPDA), Context-Aware Training Strategy (CATS), and a combined dual-phase loss function.

On two in‑house LDCT datasets acquired with different Philips scanners, our approach reduces noise and artifacts while preserving essential anatomical detail and outperforms established denoising methods such as RED CNN, EDCNN, DDPM, and CTformer in both qualitative evaluation and quantitative metrics. Evaluation on the public Mayo-2016 benchmark, collected using a Siemens scanner, confirms DIFNet's robust and competitive performance. Ablation experiments further validate the effectiveness of our overall network design and the contribution of its core components.

Our findings highlight the potential of DIFNet for real-world clinical applications, improving diagnostic reliability and patient care. The proposed framework advances LDCT denoising by balancing performance, robustness, and computational efficiency.