Deep learning (DL)-based methods show great potential in computed tomography (CT)
imaging field. The DL-based reconstruction methods are usually evaluated on the training …

Purpose Deep learning‐based image denoising and reconstruction methods demonstrated
promising performance on low‐dose CT imaging in recent years. However, most existing …

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning
while maintaining image quality, which involves a consistent pursuit of lower incident rays …

Innovations in CT have been impressive among imaging and medical technologies in both
the hardware and software domain. The range and speed of CT scanning improved from the …

Low-dose CT (LDCT) imaging is preferred in many applications to reduce the object's
exposure to X-ray radiation. In recent years, one promising approach to image …

Purpose Deep neural network‐based image reconstruction has demonstrated promising
performance in medical imaging for undersampled and low‐dose scenarios. However, it …

Commercial iterative reconstruction techniques help to reduce the radiation dose of
computed tomography (CT), but altered image appearance and artefacts can limit their …

Existing CNN-based low-dose CT reconstruction methods focus on restoring the degraded
CT images by processing on the image domain or the raw data (sinogram) domain …

Obtaining accurate and reliable images from low-dose computed tomography (CT) is
challenging. Regression convolutional neural network (CNN) models that are learned from …

Background Computed tomography (CT) is widely used in medical diagnoses due to its
ability to non-invasively detect the internal structures of the human body. However, CT scans …