Significance Extracting optical properties of tissue [e.g., the attenuation coefficient (μ) 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. Aim 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. Approach Using the stationary iteration method under suitable constraint conditions, we derived the approximated solutions of μ and R on a single scattering model. During the iteration, the estimated value of μ 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. Results 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-TiO2 phantoms and ex vivo cow eyes. Our method achieved robust and precise estimation of μ and R for both simulated and experimental data. Moreover, corresponding OCT images with attenuation compensation provided an improved resolution over the entire imaging range. Conclusions Our proposed method was able to correct the estimation bias induced by the variations of R and provided accurate depth-resolved measurements of both μ 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.

中文翻译：

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通过固定迭代方法进行光学相干断层扫描的深度相关衰减和后向散射表征。

意义 从光学相干断层扫描 (OCT) 图像中提取组织的光学特性 [例如，衰减系数 (μ) 和反向散射分数] 是参数成像和相关诊断应用的宝贵工具。以前的衰减估计模型取决于层或整个样本内后向散射分数（R）均匀性的假设，这不能准确地代表现实世界的条件。目标 我们的目标是开发一个稳健且准确的模型，根据 OCT 信号同时计算衰减和后向散射分数的深度值。此外，我们的目标是开发一种 OCT 图像的衰减补偿模型，利用我们获得的光学特性来改善组织的视觉表示。方法 在适当的约束条件下，使用平稳迭代方法，我们得出了单散射模型上 μ 和 R 的近似解。在迭代过程中，μ的估计值可以通过引入R的较大变化来修正，而较小的变化则被自动忽略。基于结构信息的计算，推导出衰减补偿后的OCT强度，并与原始OCT轮廓进行比较。结果在OCT A线模拟和蒙特卡罗建模中进行了初步验证，并在多层有机硅-染料-TiO2体模和离体牛眼上进行了后续实验。我们的方法实现了对模拟和实验数据的 μ 和 R 的稳健且精确的估计。此外，具有衰减补偿的相应 OCT 图像在整个成像范围内提供了改进的分辨率。结论 我们提出的方法能够纠正 R 变化引起的估计偏差，并同时提供 μ 和 R 的精确深度分辨测量。该方法不需要事先了解组织的形态信息，并且代表了更多真实的组织。因此，它有潜力帮助基于 OCT 成像的复杂和多层生物组织的疾病诊断。