PET is a nuclear-medicine imaging technique that can provide functional images revealing
physiologic processes in vivo. By providing a quantitative measurement of the tracer uptake …

The aim of this work is the presentation and comparison of state-of-the-art dedicated PET
systems actually available on the market, in terms of physical performance and technical …

PET is a medical imaging modality with proven clinical value for disease diagnosis and
treatment monitoring. The integration of PET and CT on modern scanners provides a …

This chapter describes the 2D and 3D image reconstruction algorithms used in PET and the
most important evolutions in the last ten years: the introduction of 3D acquisition and …

Image reconstruction in low-count PET is particularly challenging because gammas from
natural radioactivity in Lu-based crystals cause high random fractions that lower the …

Until recently, the most widely used methods for image reconstruction were direct analytic
techniques. Iterative techniques, although computationally much more intensive, produce …

The filtered backprojection (FBP) algorithm and statistical model based iterative algorithms
such as the maximum likelihood (ML) reconstruction or the maximum a posteriori (MAP) …

There is renewed interest in the PET imaging community in obtaining quantitative
information about lesion uptake values. Because of this, improvements in image …

Direct reconstruction of positron emission tomography (PET) data using deep neural
networks is a growing field of research. Initial results are promising, but often the networks …

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 …