Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering

E William Webb; Peter J H Scott

doi:10.1016/j.cpet.2021.06.012

Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering

PET Clin. 2021 Oct;16(4):525-532. doi: 10.1016/j.cpet.2021.06.012.

Authors

E William Webb¹, Peter J H Scott²

Affiliations

¹ Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
² Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: pjhscott@umich.edu.

Abstract

Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective, the authors offer insights into how the technology could be applied to improve the radiosynthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.

Keywords: Copper-mediated radiofluorination; Positron emission tomography; Radiochemistry; Radiolabeling.

Publication types

Review

MeSH terms

Artificial Intelligence*
Fluorine Radioisotopes
Humans
Machine Learning
Positron-Emission Tomography
Radiochemistry
Radiopharmaceuticals*

Substances

Fluorine Radioisotopes
Radiopharmaceuticals

Grants and funding

R01 EB021155/EB/NIBIB NIH HHS/United States