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Cited by 351

The DOI matches its Crossref record; 134 of the 134 references it cites were found in a registry or an index.

Nanoparticles for Biomedical Imaging: Fundamentals of Clinical Translation

Open access
Choi, Hak Soo, Frangioni, John V.·2010·Molecular Imaging

Abstract

Because of their large size compared to small molecules and their multifunctionality, nanoparticles (NPs) hold promise as biomedical imaging, diagnostic, and theragnostic agents. However, the key to their success hinges on a detailed understanding of their behavior after administration into the body. NP biodistribution, target binding, and clearance are complex functions of their physicochemical properties in serum, which include hydrodynamic diameter, solubility, stability, shape and flexibility, surface charge, composition, and formulation. Moreover, many materials used to construct NPs have real or potential toxicity or may interfere with other medical tests. In this review, we discuss the design considerations that mediate NP behavior in the body and the fundamental principles that govern clinical translation. By analyzing those nanomaterials that have already received regulatory approval, most of which are actually therapeutic agents, we attempt to predict which types of NPs hold potential as diagnostic agents for biomedical imaging. Finally, using quantum dots as an example, we provide a framework for deciding whether an NP-based agent is the best choice for a particular clinical application.

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Metadata

Authors
Choi, H. S., & Frangioni, J. V.
Venue
Molecular Imaging
Published
2010-11-01
Volume
9
Issue
6
Pages
291-310
ISSN
  • 1535-3508 (ISSN-L)
  • 1536-0121
OpenAlex
W2119012416
Cited by
351
References
134
FWCI
8.30
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Nanoparticles for Biomedical Imaging: Fundamentals of Clinical Translation. https://app.cochat.ai/papers/10.2310/7290.2010.00031
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Choi, H. S., & Frangioni, J. V. (2010). Nanoparticles for Biomedical Imaging: Fundamentals of Clinical Translation. Molecular Imaging. https://doi.org/10.2310/7290.2010.00031

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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.