Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography

We analyze a method to extract additional depth resolved spectroscopic information from frequency domain optical coherence tomography (FDOCT) data. The reconstruction of depth resolved spectra is obtained by a Fourier transform of the individual peaks in the complex FDOCT depth profiles. We demonstrate a validation of this concept with theoretical simulations and with accurate experimental studies on a multilayer sample with four different characteristic absorbers. The spatially resolved spectroscopic patterns of all individual sample layers are calculated from the depth resolved reconstructed spectra. With an additional pattern recognition algorithm, these reconstructed patterns are compared automatically to the spectral characteristics of the expected substances. This provides an allocation of the reconstructed spectra to the substances with high reliability. Thus, we present an automated substance identification directly from conventional FDOCT data, which increases significantly the information content of the image.