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.
Highlights
Fluorescence lifetime imaging (FLIM) is a powerful tool in biological and clinical sciences for characterization of tissue samples
One of the main reasons for this is the element of subjectivity involved in interpreting multispectral FLIM images, which is based on qualitative comparison of spectral intensity and lifetime values obtained from sample fluorescence time-resolved measurements with those of known fluorophores
Blind linear unmixing of m-FLIM data allows to obtain a quantitative description of the biochemical composition of the biological samples, which is easy to interpret and validate
Summary
Fluorescence lifetime imaging (FLIM) is a powerful tool in biological and clinical sciences for characterization of tissue samples. One of the main reasons for this is the element of subjectivity involved in interpreting multispectral FLIM images, which is based on qualitative comparison of spectral intensity and lifetime values obtained from sample fluorescence time-resolved measurements with those of known fluorophores. Such comparison-based interpretation of FLIM images is satisfactory only when the fluorescence decay recorded at a pixel in a FLIM image can be attributed to a single fluorophore. It becomes imperative to develop a method that can quantitatively identify the number, the type and the relative abundance of fluorophores present in the sample
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