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 …

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 …

Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

This paper reviewed the deep learning‐based studies for medical imaging synthesis and its
clinical application. Specifically, we summarized the recent developments of deep learning …

The diagnosis and treatment of patients with cancer requires access to imaging to ensure
accurate management decisions and optimal outcomes. Our global assessment of imaging …

This review sets out to discuss the foremost applications of artificial intelligence (AI),
particularly deep learning (DL) algorithms, in single-photon emission computed tomography …

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 …

This review highlights recent efforts on applying electron microscopy (EM) to soft (including
biological) nanomaterials. We will show how developments of both the hardware and …

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 …