The continuous development and extensive use of CT in medical practice have raised a
public concern over the associated radiation dose to patients. Reducing the radiation dose …

Computed tomography (CT) is a widely used screening and diagnostic tool that allows
clinicians to obtain a high-resolution, volumetric image of internal structures in a non …

Objective. Self-supervised learning methods have been successfully applied for low-dose
computed tomography (LDCT) denoising, with the advantage of not requiring labeled data …

Low‐dose computed tomography has been highly desirable because of the health concern
about excessive radiation dose, but also challenging due to insufficient or noisy projection …

Convolutional neural network (CNN) based low-dose CT denoising is effective to deal with
complex CT noise. However, CNN denoiser with pixel-level loss functions (eg, mean …

Low-dose computed tomography (LDCT) is a viable solution for clinical diagnosis despite
noise and artifacts degrading diagnostic quality. Additionally, patients are protected from …

Radiation dose of X-ray Computed Tomography (CT) imaging has raised a worldwide health
concern. Therefore, low-dose CT imaging has been of a huge interest in the last decade …

Low dose computed tomography (LDCT) can reduce the radiation hazard to patients
effectively. However, mottle noise and streak artifacts often lower and degenerate the quality …

Deep learning (DL) approaches to image denoising have advanced real-world performance
in one of the most thoroughly studied areas of digital signal processing. Keys to the success …

Although deep learning has witnessed great successes for low-dose CT (LDCT) denoising,
there still exist two significant problems: i) most LDCT denoising approaches require paired …