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 …

Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive
therapeutic compounds that deliver ionizing radiation. Given the introduction of very …

Iterative neural networks (INN) are rapidly gaining attention for solving inverse problems in
imaging, image processing, and computer vision. INNs combine regression NNs and an …

Machine learning has found unique applications in nuclear medicine from photon detection
to quantitative image reconstruction. Although there have been impressive strides in …

This review discusses methods for learning parameters for image reconstruction problems
using bilevel formulations. Image reconstruction typically involves optimizing a cost function …

Direct reconstruction methods have been developed to estimate parametric images directly
from the measured PET sinograms by combining the PET imaging model and tracer kinetics …

Position emission tomography (PET) is widely used in clinics and research due to its
quantitative merits and high sensitivity, but suffers from low signal-to-noise ratio (SNR) …

Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear
medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging …

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 …

Low-dose emission tomography (ET) plays a crucial role in medical imaging, enabling the
acquisition of functional information for various biological processes while minimizing the …