Deep-learning-based tomographic imaging is an important application of artificial
intelligence and a new frontier of machine learning. Deep learning has been widely used in …

Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative
metrics—the so-called radiomic features—within medical images. Radiomic features capture …

Inverse problems are essential to imaging applications. In this letter, we propose a model-
based deep learning network, named FISTA-Net, by combining the merits of interpretability …

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 …

Over past several years, machine learning, or more generally artificial intelligence, has
generated overwhelming research interest and attracted unprecedented public attention. As …

Purpose Image quality of positron emission tomography (PET) is limited by various physical
degradation factors. Our study aims to perform PET image denoising by utilizing prior …

The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …

PET imaging involves radiotracer injections, raising concerns about the risk of radiation
exposure. To minimize the potential risk, one way is to reduce the injected tracer. However …

Recently, deep neural networks have been widely and successfully applied in computer
vision tasks and have attracted growing interest in medical imaging. One barrier for the …

This brief review summarizes the major applications of artificial intelligence (AI), in particular
deep learning approaches, in molecular imaging and radiation therapy research. To this …