arXiv:2611.05983v1 Announce Type: new Abstract: Depth-resolved attenuation in optical coherence tomography (OCT) can provide quantitative optical contrast beyond structural imaging. However, conventional reconstruction methods can be biased when the OCT signal does not fully decay within the imaging range or when the distal signal tail is dominated by noise or residual reflections from distal sample or support interfaces. Here, we present a perturbation-based Beer-Lambert framework that combines a zeroth-order exponential estimate with a first-order correction, reducing reliance on the unreliable distal signal tail below the current depth. Numerical A-line simulations, Monte Carlo modeling, multilayer silicone-dye-TiO2 phantoms, and ex vivo bovine retina experiments were used to evaluate the method under finite-depth and heterogeneous imaging conditions. The numerical A-line tests showed reduced depth-dependent error against prescribed attenuation profiles; the Monte Carlo comparisons showed less distal drift relative to model-defined benchmarks, while the phantom profiles were compared with separately acquired single-layer OCT calibration values. In a Monte Carlo component test that withheld vessel-mask and vessel-boundary information from reconstruction and used that information only for post hoc evaluation, the first-order correction recovered 61% of the segmentation-informed target vessel contrast and reduced vessel ROI mean absolute error by 65% relative to the fixed zeroth-order baseline. These results suggest that perturbation-based reconstruction may provide a practical approach for depth-resolved reconstruction of effective OCT attenuation when the fully decayed A-line condition is not satisfied.