Abstract
Existing methods of interpreting fluorescence lifetime imaging microscopy (FLIM) images are based on comparing the intensity and lifetime values at each pixel with those of known fluorophores. This method becomes unwieldy and subjective in many practical applications where there are several fluorescing species contributing to the bulk fluorescence signal, and even more so in the case of multispectral FLIM. Non-negative matrix factorization (NMF) is a multivariate data analysis technique aimed at extracting non-negative signatures of pure components and their non-negative abundances from an additive mixture of those components. In this paper, we present the application of NMF to multispectral time-domain FLIM data to obtain a new set of FLIM features (relative abundance of constituent fluorophores). These features are more intuitive and easier to interpret than the standard fluorescence intensity and lifetime values. The proposed approach, unlike several FLIM data analysis methods, is not limited by the number of constituent fluorescing species or their possibly complex decay dynamics. Moreover, the new set of FLIM features can be obtained by processing raw multispectral FLIM intensity data, thereby rendering time deconvolution unnecessary and resulting in lesser computational time and relaxed SNR requirements. The performance of the NMF method was validated on simulated and experimental multispectral time-domain FLIM data. The NMF features were also compared against the standard intensity and lifetime features, in terms of their ability to discriminate between different types of atherosclerotic plaques.
Highlights
Despite several studies showing the potential of multispectral fluorescence lifetime imaging microscopy (FLIM) as a promising clinical optical imaging modality [1,2,3,4], its utility has not yet been fully established
We presented an application of negative matrix factorization (NMF) to identify the type of constituent fluorophores and their relative contributions to the bulk multispectral FLIM signal measured from a sample
We presented the application of NMF to multispectral FLIM data to derive a set of NMF features that provides an intuitive and objective way of characterizing latent tissue fluorophores and their abundances
Summary
Despite several studies showing the potential of multispectral FLIM as a promising clinical optical imaging modality [1,2,3,4], its utility has not yet been fully established. Existing methods of interpreting multispectral FLIM images are based on a qualitative comparison of spectral intensity and lifetime values at each pixel in a FLIM image with those of known fluorophores. Such comparison-based interpretation of FLIM images is satisfactory only when the fluorescence decay at each pixel can be attributed to just one fluorophore. In most practical applications, there are often more than one fluorescing species contributing to the bulk fluorescence signal In such cases, it becomes imperative to develop a method that can quantitatively characterize the type and relative abundance of fluorophores present in a sample. This is of particular interest in the context of biomedical diagnosis, since the progression from normal to pathological tissue is often characterized by changes in the relative abundance of tissue fluorophores
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