Mathematical model for separating signal and noise in dynamic optical coherence tomography (dOCT)

Dynamic optical coherence tomography (dOCT) uses signal fluctuation for contrasting different cellular and acellular components in living biological tissue. The autocorrelation or Fourier transform of time series of OCT measurements are converted to a color contrast. However, a quantitative analysis is still challenging. Here we investigate theoretically, how noise of the OCT measurement influences the fluctuation spectra. Probability functions are derived for the different components in the spectra and validated by numerical simulation. With an appropriate calibration of the OCT device a separation of OCT noise and a quantification the dynamic OCT should be feasible.