This article reviews the use of a subdiscipline of artificial intelligence (AI), deep learning, for
the reconstruction of images in positron emission tomography (PET). Deep learning can be …

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 …

Recent efforts on solving inverse problems in imaging via deep neural networks use
architectures inspired by a fixed number of iterations of an optimization method. The number …

Objectives To assess the impact on image quality and dose reduction of a new deep
learning image reconstruction (DLIR) algorithm compared with a hybrid iterative …

Medical imaging is an invaluable resource in medicine as it enables to peer inside the
human body and provides scientists and physicians with a wealth of information …

The use of deep learning in medical imaging has increased rapidly over the past few years,
finding applications throughout the entire radiology pipeline, from improved scanner …

Purpose To compare the image quality of brain computed tomography (CT) images
reconstructed with deep learning–based image reconstruction (DLIR) and adaptive …

Conventional reconstruction techniques, such as filtered back projection (FBP) and iterative
reconstruction (IR), which have been utilised widely in the image reconstruction process of …

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

In clinical practice, computed tomography (CT) is an important noninvasive inspection
technology to provide patients' anatomical information. However, its potential radiation risk is …