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Current Advances in Molecular Imaging: Noninvasive in Vivo Bioluminescent and Fluorescent Optical Imaging in Cancer Research

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Choy, Garry, Choyke, Peter, Libutti, Steven K·2003·Molecular Imaging

Abstract

Recently, there has been tremendous interest in developing techniques such as MRI, micro-CT, micro-PET, and SPECT to image function and processes in small animals. These technologies offer deep tissue penetration and high spatial resolution, but compared with noninvasive small animal optical imaging, these techniques are very costly and time consuming to implement. Optical imaging is cost-effective, rapid, easy to use, and can be readily applied to studying disease processes and biology in vivo. In vivo optical imaging is the result of a coalescence of technologies from chemistry, physics, and biology. The development of highly sensitive light detection systems has allowed biologists to use imaging in studying physiological processes. Over the last few decades, biochemists have also worked to isolate and further develop optical reporters such as GFP, luciferase, and cyanine dyes. This article reviews the common types of fluorescent and bioluminescent optical imaging, the typical system platforms and configurations, and the applications in the investigation of cancer biology.

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Metadata

Authors
Choy, G., Choyke, P., & Libutti, S. K.
Venue
Molecular Imaging
Published
2003-10-01
Volume
2
Issue
4
Pages
303-312
ISSN
  • 1535-3508 (ISSN-L)
  • 1536-0121
OpenAlex
W2160564412
Cited by
236
References
0
FWCI
10.86
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Current Advances in Molecular Imaging: Noninvasive in Vivo Bioluminescent and Fluorescent Optical Imaging in Cancer Research. https://app.cochat.ai/papers/10.1162/15353500200303142
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Choy, G., Choyke, P., & Libutti, S. K. (2003). Current Advances in Molecular Imaging: Noninvasive in Vivo Bioluminescent and Fluorescent Optical Imaging in Cancer Research. Molecular Imaging. https://doi.org/10.1162/15353500200303142

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