Adaptive noise-aware denoising network: Effective denoising for CT images with varying noise intensity

Abstract

X-ray computed tomography (CT) plays a crucial role in modern medical imaging for its non-invasive acquisition of anatomical information about the human body. However, its inherent carcinogenic risk remains an unavoidable topic. Low-dose CT (LDCT) reduces ionizing radiation exposure to minimize harm to the human body, but it introduces noise and artifacts of varying intensity, which can affect diagnosis and analysis. In recent years, deep learning has demonstrated competitive performance in medical image denoising. However, most current deep learning-based methods primarily focus on specific noise intensities, leading to degradation when faced with noise of different intensities. In this paper, we propose an Adaptive Noise-Aware Denoising Generative Adversarial Networks (ANAD-GAN) to address the aforementioned issues. The proposed noise-aware memory module can remember and decode high-level hidden features of noise of different intensities adaptively through self-updating feature vectors. To explore a more accurate perception of noise distribution, an intricately designed block named BLOGS is incorporated in our framework. We also integrate a discriminator-based detail-preserving loss in our framework to further enhance the visual quality of the denoised images. In summary, our innovative network dynamically adjusts to varying noise intensities, yielding remarkable denoising results. It attains a PSNR of 48.36 and SSIM of 0.9848 across 5936 images in the AAPM dataset, achieves a PSNR of 48.98 and SSIM of 0.9882 on the Philips dataset with 5000 images, and demonstrates outstanding performance with a PSNR of 50.74 and SSIM of 0.9906 on the private Siemens dataset comprising 4000 images. Through extensive experiments on three datasets, our method surpasses various widely adopted techniques in effectively addressing noise of different intensities.