This review focuses on positron emission tomography (PET) imaging algorithms and traces
the evolution of PET image reconstruction methods. First, we provide an overview of …

Artificial intelligence (AI) has been applied to various medical imaging tasks, such as
computer-aided diagnosis. Specifically, deep learning techniques such as convolutional …

Despite being thirteen years since the installation of the first PET-MR system, the scanners
constitute a very small proportion of the total hybrid PET systems installed. This is in stark …

Convolutional neural networks (CNNs) have recently achieved remarkable performance in
positron emission tomography (PET) image reconstruction. In particular, CNN-based PET …

PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative
values have been used as a surrogate marker for clinical decision-making in numerous …

Although deep learning for application in positron emission tomography (PET) image
reconstruction has attracted the attention of researchers, the image quality must be further …

A novel deep learning (DL)-based attenuation correction (AC) framework was applied to
clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F …

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Background Low‐dose positron emission tomography (LD‐PET) imaging is commonly
employed in preclinical research to minimize radiation exposure to animal subjects …

Objective Various motion correction (MC) algorithms for positron emission tomography
(PET) have been proposed to accelerate the diagnostic performance and research in brain …