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Cited by 294

The DOI matches its Crossref record; 163 of the 163 references it cites were found in a registry or an index.

Radiochemistry for positron emission tomography

Open access
Rong, Jian, Haider, Achi, Jeppesen, Troels E., Josephson, Lee, Liang, Steven H.·2023·Nature Communications

Abstract

Positron emission tomography (PET) constitutes a functional imaging technique that is harnessed to probe biological processes in vivo. PET imaging has been used to diagnose and monitor the progression of diseases, as well as to facilitate drug development efforts at both preclinical and clinical stages. The wide applications and rapid development of PET have ultimately led to an increasing demand for new methods in radiochemistry, with the aim to expand the scope of synthons amenable for radiolabeling. In this work, we provide an overview of commonly used chemical transformations for the syntheses of PET tracers in all aspects of radiochemistry, thereby highlighting recent breakthrough discoveries and contemporary challenges in the field. We discuss the use of biologicals for PET imaging and highlight general examples of successful probe discoveries for molecular imaging with PET – with a particular focus on translational and scalable radiochemistry concepts that have been entered to clinical use.

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Metadata

Authors
Rong, J., Haider, A., Jeppesen, T. E., Josephson, L., & Liang, S. H.
Venue
Nature Communications
Published
2023-06-05
Volume
14
Issue
1
Pages
3257-3257
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4379468256
Cited by
294
References
163
FWCI
43.40
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Radiochemistry for positron emission tomography. https://app.cochat.ai/papers/10.1038/s41467-023-36377-4
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Rong, J., Haider, A., Jeppesen, T. E., Josephson, L., & Liang, S. H. (2023). Radiochemistry for positron emission tomography. Nature Communications. https://doi.org/10.1038/s41467-023-36377-4

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.