ArticleOptics express2014
Estimation of the number of fluorescent end-members for quantitative analysis of multispectral FLIM data.
Article in Optics express, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Blind deconvolution estimation by multi-exponential models and alternated least squares approximations: Free-form and sparse approach.PloS one · 2021Article
- Extended Blind End-member and Abundance Extraction for Biomedical Imaging Applications.IEEE access : practical innovations, open solutions · 2019Article
- Quadratic blind linear unmixing: A graphical user interface for tissue characterization.Computer methods and programs in biomedicine · 2016Article
- Deconvolution of fluorescence lifetime imaging microscopy by a library of exponentials.Optics express · 2015Article
- Blind deconvolution estimation of fluorescence measurements through quadratic programming.Journal of biomedical optics · 2015Article
- Extended output phasor representation of multi-spectral fluorescence lifetime imaging microscopy.Biomedical optics express · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Multispectral fluorescence lifetime imaging (m-FLIM) can potentially allow identifying the endogenous fluorophores present in biological tissue. Quantitative description of such data requires estimating the number of components in the sample, their characteristic fluorescent decays, and their relative contributions or abundances. Unfortunately, this inverse problem usually requires prior knowledge about the data, which is seldom available in biomedical applications. This work presents a new methodology to estimate the number of potential endogenous fluorophores present in biological tissue samples from time-domain m-FLIM data. Furthermore, a completely blind linear unmixing algorithm is proposed. The method was validated using both synthetic and experimental m-FLIM data. The experimental m-FLIM data include in-vivo measurements from healthy and cancerous hamster cheek-pouch epithelial tissue, and ex-vivo measurements from human coronary atherosclerotic plaques. The analysis of m-FLIM data from in-vivo hamster oral mucosa identified healthy from precancerous lesions, based on the relative concentration of their characteristic fluorophores. The algorithm also provided a better description of atherosclerotic plaques in term of their endogenous fluorophores. These results demonstrate the potential of this methodology to provide quantitative description of tissue biochemical composition.
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Registered trials
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