The signal of optical coherence tomography (OCT) decays exponentially in depth due to tissue scattering, resulting in indistinct tissue features in three-dimension. Moreover, due to limited light penetration depth, extensive volumetric investigation is usually constrained for large-scale biological samples. By integrating serial sectioning technology with block-face imaging, we establish a volumetric OCT acquisition and reconstruction pipeline that incorporates depth-resolved attenuation coefficient estimation, volumetric stitching and filtering, and feature enhancement visualization. We demonstrate this pipeline on ex vivo human brain volumes of several cubic centimeters with 5 um isotropic resolution.
Significance: The optical properties of biological samples provide information about the structural characteristics of the tissue and any changes arising from pathological conditions. Optical coherence tomography (OCT) has proven to be capable of extracting tissue’s optical properties using a model that combines the exponential decay due to tissue scattering and the axial point spread function that arises from the confocal nature of the detection system, particularly for higher numerical aperture (NA) measurements. A weakness in estimating the optical properties is the inter-parameter cross-talk between tissue scattering and the confocal parameters defined by the Rayleigh range and the focus depth.
Aim: In this study, we develop a systematic method to improve the characterization of optical properties with high-NA OCT.
Approach: We developed a method that spatially parameterizes the confocal parameters in a previously established model for estimating the optical properties from the depth profiles of high-NA OCT.
Results: The proposed parametrization model was first evaluated on a set of intralipid phantoms and then validated using a low-NA objective in which cross-talk from the confocal parameters is negligible. We then utilize our spatially parameterized model to characterize optical property changes introduced by a tissue index matching process using a simple immersion agent, 2,2’-thiodiethonal.
Conclusions: Our approach improves the confidence of parameter estimation by reducing the degrees of freedom in the non-linear fitting model.
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