Ex vivo imaging and quantification of liver fibrosis using second-harmonic generation microscopy
Name
Sun-2010-Ex vivo imaging and.pdf
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Checksum (MD5)
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Author(s)
Kim, Ki Hean
So, Peter T. C.
Sun, Tzu-Lin
Liu, Yuan
Sung, Ming-Chin
Yang, Chun-Hui
Hovhannisyan, Vladimir
Chen, Wei-Liang
Chen, Yang-Fang
Chen, Hsiao-Ching
Date Issued
June 2010
Journal
Journal of Biomedical Optics
Publisher
SPIE
Citation
Sun, Tzu-Lin et al. “Ex vivo imaging and quantification of liver fibrosis using second-harmonic generation microscopy.” Journal of Biomedical Optics 15.3 (2010): 036002-6. © 2010 Society of Photo-Optical Instrumentation Engineers
Version
Final published version
Abstract
Conventionally, liver fibrosis is diagnosed using histopathological techniques. The traditional method is time-consuming in that the specimen preparation procedure requires sample fixation, slicing, and labeling. Our goal is to apply multiphoton microscopy to efficiently image and quantitatively analyze liver fibrosis specimens bypassing steps required in histological preparation. In this work, the combined imaging modality of multiphoton autofluorescence (MAF) and second-harmonic generation (SHG) was used for the qualitative imaging of liver fibrosis of different METAVIR grades under label-free, ex vivo conditions. We found that while MAF is effective in identifying cellular architecture in the liver specimens, it is the spectrally distinct SHG signal that allows the characterization of the extent of fibrosis. We found that qualitative SHG imaging can be used for the effective identification of the associated features of liver fibrosis specimens graded METAVIR 0 to 4. In addition, we attempted to associate quantitative SHG signal to the different METAVIR grades and found that an objective determination of the extent of disease progression can be made. Our approach demonstrates the potential of using multiphoton imaging in rapid classification of ex vivo liver fibrosis in the clinical setting and investigation of liver fibrosis–associated physiopathology in animal models in vivo.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1117/1.3427146
