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Cited by 277

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Advanced Photoacoustic Imaging Applications of Near‐Infrared Absorbing Organic Nanoparticles

Jiang, Yuyan, Pu, Kanyi·2017·Small

Abstract

Progress of nanotechnology in recent years has stimulated fast development of nanoparticles in biomedical research. Photoacoustic (PA) imaging as an emerging non‐invasive technique in molecular imaging has improved imaging depth relative to conventional optical imaging, demonstrating great potential in clinical applications. The convergence of nanotechnology and PA imaging has enabled a broad spectrum of new opportunities in fundamental biology and translation medicine. This review focuses on the recent advances of organic nanoparticles in PA imaging applications. Near‐infrared absorbing organic nanoparticles are classified and discussed according to their different imaging applications, which include tumor imaging, gastrointestinal imaging, sentinel lymph node imaging, disease microenvironment imaging and real‐time drug imaging. The chemistry and PA properties of organic nanoparticles are discussed in details to highlight their own merits, and their challenges and perspectives in PA imaging are also discussed.

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Metadata

Authors
Jiang, Y., & Pu, K.
Venue
Small
Publisher
Wiley
Published
2017-06-08
Volume
13
Issue
30
ISSN
  • 1613-6810 (ISSN-L)
  • 1613-6829
OpenAlex
W2621940244
Cited by
277
References
13
FWCI
19.09
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Advanced Photoacoustic Imaging Applications of Near‐Infrared Absorbing Organic Nanoparticles. https://app.cochat.ai/papers/10.1002/smll.201700710
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Jiang, Y., & Pu, K. (2017). Advanced Photoacoustic Imaging Applications of Near‐Infrared Absorbing Organic Nanoparticles. Small. https://doi.org/10.1002/smll.201700710

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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.