Dynamic optical coherence tomography algorithm for label-free assessment of swiftness and occupancy of intratissue moving scatterers

Dynamic optical coherence tomography (DOCT) statistically analyzes fluctuations in time-sequential OCT signals, enabling label-free and three-dimensional visualization of intratissue and intracellular activities. Current DOCT methods, such as logarithmic intensity variance (LIV) and OCT correlation decay speed (OCDS) have several limitations.Namely, the DOCT values and intratissue motions are not directly related, and hence DOCT values are not interpretable in the context of the tissue motility. We introduce a new DOCT algorithm that provides more direct interpretation of DOCT in the contexts of dynamic scatterer ratio and scatterer speed in the tissue.The detailed properties of the new and conventional DOCT methods are investigated by numerical simulations, and the experimental validation with in vitro and ex vivo samples demonstrates the feasibility of the new method.

💡 Research Summary

Dynamic optical coherence tomography (DOCT) has emerged as a powerful label‑free technique for visualizing intracellular and intratissue activity by analyzing temporal fluctuations in OCT signal intensity. However, the two most widely used DOCT metrics—logarithmic intensity variance (LIV) and OCT correlation decay speed (OCDS)—suffer from fundamental interpretability issues. LIV depends strongly on the size of the acquisition time window (Atw), making its values non‑intrinsic, while OCDS is highly sensitive to the arbitrary choice of correlation delay range and does not provide a monotonic relationship with actual motion speed. Consequently, neither metric can be directly linked to the underlying tissue dynamics, limiting their utility in quantitative studies such as drug‑response assays.

The authors address these shortcomings by introducing a novel DOCT framework that extracts two physically meaningful parameters from the same OCT time‑sequence: “authentic LIV” (aLIV) and “Swiftness.” The method proceeds as follows. Repeated OCT frames are acquired (32 frames in the experiments, with an inter‑frame interval of 204.8 ms). For each pixel, the conventional LIV is computed over multiple sub‑windows of varying duration Tw (ranging from the inter‑frame interval up to the full acquisition window). This yields an LIV‑Tw curve for every pixel. The curve is then fitted with a first‑order saturation function f(Tw)=a·