Depth-dependent attenuation and backscattering characterization of optical coherence tomography by stationary iterative method

Extracting optical properties of tissue [e.g., the attenuation coefficient ($\mu$) and the backscattering fraction] from the optical coherence tomography (OCT) images is a valuable tool for parametric imaging and related diagnostic applications. Previous attenuation estimation models depend on the assumption of the uniformity of the backscattering fraction ($R$) within layers or whole samples, which does not accurately represent real-world conditions.

Our aim is to develop a robust and accurate model that calculates depth-wise values of attenuation and backscattering fractions simultaneously from OCT signals. Furthermore, we aim to develop an attenuation compensation model for OCT images that utilizes the optical properties we obtained to improve the visual representation of tissues.

Using the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of $\mu$ and $R$ on a single scattering model. During the iteration, the estimated value of $\mu$ can be rectified by introducing the large variations of $R$, whereas the small ones were automatically ignored. Based on the calculation of the structure information, the OCT intensity with attenuation compensation was deduced and compared with the original OCT profiles.

The preliminary validation was performed in the OCT A-line simulation and Monte Carlo modeling, and the subsequent experiment was conducted on multi-layer silicone-dye-${\mathrm{TiO}}_2$ phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of $\mu$ and $R$ for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range.

Our proposed method was able to correct the estimation bias induced by the variations of $R$ and provided accurate depth-resolved measurements of both $\mu$ and $R$ simultaneously. The method does not require prior knowledge of the morphological information of tissue and represents more real-life tissues. Thus, it has the potential to help OCT imaging based disease diagnosis of complex and multi-layer biological tissue.