This paper presents a review of deep learning (DL)-based medical image registration
methods. We summarized the latest developments and applications of DL-based registration …

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 …

Deep learning has revolutionized image processing and achieved the-state-of-art
performance in many medical image segmentation tasks. Many deep learning-based …

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

Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …

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 …

Despite the potential of deep learning (DL)-based methods in substituting CT-based PET
attenuation and scatter correction for CT-free PET imaging, a critical bottleneck is their …

Purpose To develop an accurate and fast deformable image registration (DIR) method for
four‐dimensional computed tomography (4D‐CT) lung images. Deep learning‐based …

Artificial intelligence and machine learning based approaches are increasingly finding their
way into various areas of nuclear medicine imaging. With the technical development of new …

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 …