Mathematical modeling of intracellular and intratissue activities for understanding dynamic optical coherence tomography signals

Dynamic optical coherence tomography (DOCT) is developed to evaluate the functional activities of wide spectrum of tissues. However, the relation between the DOCT signals and the intracellular motion is not fully identified yet. This unidentified relationship inhibits further dissemination of DOCT signals. In this study, we proposed a theoretical and numerical framework to understand DOCT. It includes the classification of intracellular motility, their mathematical modeling, and numerical simulation. We classified intracellular motilities into six types: active transport, passive transport, jiggling, floating of dissociated cells, migration, and flow. Then, the motilities were modeled by three physical models: flow, random ballistic and diffusion. The sample motion and it resulting time-sequential OCT images were numerically simulated. Two DOCT contrasts were computed from the OCT time-sequence: logarithmic intensity variance of OCT (LIV) and temporal variance of complex OCT signals (complex variance). We considered the random ballistic motions measured by two different probing wavelengths of 840nm and 1310nm. Tessellated pattern of low and high LIV was found in LIV images. The LIV and complex variance increase within the velocity range of 4.5 to 270nm/s, while it becomes almost constant for larger velocities. Additionally, we found that both LIV and complex variance are higher when shorter wavelength is considered. Using the proposed theoretical model, we can better understand the specific intracellular tissue activities that contribute to the high DOCT signal.