Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising

TL;DR

This paper introduces a 3D multi-scale GAN-based method for low-dose CT denoising that leverages volumetric information to better preserve structures and textures, significantly reducing noise and artifacts.

Contribution

It presents a novel structure-sensitive multi-scale GAN that incorporates 3D information and explores different loss functions for improved LDCT denoising.

Findings

Effective preservation of structural and texture details.

Significant noise and artifact suppression.

Outperforms existing methods in qualitative assessments.

Abstract

Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that down-grade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structure-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and texture information from normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more detailed information, and outperforms competing methods.