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 …

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 …

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

Artificial intelligence (AI) will change the face of nuclear medicine and molecular imaging as
it will in everyday life. In this review, we focus on the potential applications of AI in the field …

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a
clinical tool for diagnosing patients with Alzheimer's disease (AD) and mild cognitive …

Techniques from the field of artificial intelligence, and more specifically machine (deep)
learning methods, have been core components of most recent developments in the field of …

Despite the great media attention for artificial intelligence (AI), for many health care
professionals the term and the functioning of AI remain a “black box,” leading to exaggerated …

The rapid expansion of machine learning is offering a new wave of opportunities for nuclear
medicine. This paper reviews applications of machine learning for the study of attenuation …

Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively
accurate positron emission tomography (PET) images. PET AC based on computed …