  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.
