Deep convolutional neural network (CNN) based methods have become popular choices for
reducing image noise in CT. Some of these methods showed promising results, especially in …

Low-dose computer tomography (LDCT) has been widely used in medical diagnosis.
Various denoising methods have been presented to remove noise in LDCT scans. However …

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

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical
applications. At the same time, the X-ray radiation dose associated with CT scannings raises …

Background Low-dose computed tomography (LDCT) scans can effectively reduce the
radiation damage to patients, but this is highly detrimental to CT image quality. Deep …

Low dose CT (LDCT) by reducing the X-ray tube current is of huge significance during
clinical scanning. However, low-dose CT images often have strong noise and artifacts …

In the last decade, the technological progress of multi-slice CT imaging has turned CCTA
into a valuable tool for coronary assessment in many low to medium risk patients …

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 learning-based image denoising and reconstruction methods have shown promising
results for low-dose CT. When high-quality reference images are not available for training …

Computed tomography (CT) stands as a pivotal medical imaging technique, delivering
timely and reliable clinical evaluations. Yet, its dependence on ionizing radiation raises …