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Improving the characterization of ex vivo human brain optical properties using high numerical aperture optical coherence tomography by spatially constraining the confocal parameters
Neurophotonics ( IF 4.8 ) Pub Date : 2020-10-01 , DOI: 10.1117/1.nph.7.4.045005
Jiarui Yang 1 , Ichun Anderson Chen 1 , Shuaibin Chang 2 , Jianbo Tang 1 , Blaire Lee 1 , Kıvılcım Kılıç 1 , Smrithi Sunil 1 , Hui Wang 3 , Divya Varadarajan 3 , Caroline Magnain 3 , Shih-Chi Chen 4 , Irene Costantini 5, 6 , Francesco Pavone 5 , Bruce Fischl 3, 7 , David A Boas 1
Affiliation  

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.
更新日期:2020-10-28
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