Image denoising techniques are essential to reducing noise levels and enhancing diagnosis
reliability in low-dose computed tomography (CT). Machine learning based denoising …

In clinical applications, multi-dose scan protocols will cause the noise levels of computed
tomography (CT) images to fluctuate widely. The popular low-dose CT (LDCT) denoising …

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 …

To minimize radiation risk, dose reduction is important in the diagnostic and therapeutic
applications of computed tomography (CT). However, image noise degrades image quality …

Low Dose CT Denoising research aims to reduce the risks of radiation exposure to patients.
Recently researchers have used deep learning to denoise low dose CT images with …

Low-dose computed tomography (CT) will increase noise and artefacts while reducing the
radiation dose, which will adversely affect the diagnosis of radiologists. Low-dose CT image …

Low-dose computed tomography (LDCT) can reduce the X-ray radiation dose that the
patients receive, up to 86%, which decreases the potential hazards and expands its …

Considering that high-dose X-ray radiation during CT scans may bring potential risks to
patients, in the medical imaging industry there has been increasing emphasis on low-dose …

Deep learning has recently been extensively investigated to remove artifacts in low-dose
computed tomography (LDCT). However, the power of transfer learning for medical image …

Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-
restricted applications, but the quantum noise as resulted by the insufficient number of …