Abstract
Autofluorescence, the endogenous fluorescence present in cells and tissues, has historically been considered a nuisance in biomedical imaging. Many endogenous fluorophores, specifically, collagen, elastin, nicotinamide adenine dinucleotide, and flavin adenine dinucleotide (FAD), are found throughout the human body. In fluorescence imaging scenarios, these signals can be prohibitive as they can outcompete signals introduced for diagnostic purposes. However, autofluorescence also contains information that has diagnostic value. Recent advances in hyperspectral imaging have allowed the acquisition of significantly more data in a shorter time period by scanning the excitation spectra of fluorophores. The reduced acquisition time and increased signal-to-noise ratio allow for separation of significantly more fluorophores than previously possible. We propose to utilize excitation-scanning hyperspectral imaging of autofluorescence to differentiate neoplastic lesions from surrounding non-neoplastic "normal" tissue. The spectra of isolated autofluorescent molecules are obtained using a custom inverted microscope (TE-2000, Nikon Instruments) with an Xe arc lamp and thin-film tunable filter array (VersaChrome, Semrock, Inc.). Scans utilize excitation wavelengths from 360 to 550 nm in 5-nm increments. The resultant molecule-specific spectra are used to analyze hyperspectral image stacks from normal and neoplastic colorectal tissues. Due to a limited number of samples, neoplastic tissues examined here are a pool of both colorectal adenocarcinoma and adenomatous polyps. The hyperspectral images are analyzed with ENVI software and custom MATLAB scripts, including linear spectral unmixing. Initial results indicate the ability to separate signals of endogenous fluorophores and measure the relative concentrations of fluorophores among healthy and diseased states, in this case, normal colon versus neoplastic colon. These results suggest pathology-specific changes to endogenous fluorophores can be detected using excitation-scanning hyperspectral imaging. Future work will focus on expanding the library of pure molecules, exploring histogram distance metrics as a means for identifying deviations in spectral signatures, and examining more defined disease states.