The resurgence of deep neural networks has created an alternative pathway for low-dose
computed tomography denoising by learning a nonlinear transformation function between …

Deep neural networks have shown great improvements in low-dose computed tomography
(CT) denoising. Early deep learning-based low-dose CT denoising algorithms were …

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 …

Radiation dose reduction is one of the most important topics in the field of computed
tomography (CT). Over past years, deep learning based denoising methods have been …

Training a convolutional neural network (CNN) to reduce noise in low-dose CT (LDCT)
images typically relies on supervised learning, which requires input–target pairs of noisy …

In recent years, low-dose computed tomography (LDCT) has played an increasingly
important role in the diagnosis CT to reduce the potential adverse effects of x-ray radiation …

Background Although low‐dose computed tomography (CT) imaging has been more widely
adopted in clinical practice to reduce radiation exposure to patients, the reconstructed CT …

While various deep learning methods have been proposed for low-dose computed
tomography (CT) denoising, most of them leverage the normal-dose CT images as the …

This paper proposes a deep learning-based denoising method for noisy low-dose
computerized tomography (CT) images in the absence of paired training data. The proposed …

Removing noise and artifacts from low-dose computed tomography (LDCT) is a challenging
task, and most existing image-based algorithms tend to blur the results. To improve the …