Open AccessThree-dimensional opto-thermo-mechanical model for predicting photo-thermal optical coherence tomography responses in multilayer geometries
Author(s) -
Mohammad Hossein Salimi,
Martin Villiger,
Nima Tabatabaei
Publication year - 2022
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.454491
Subject(s) - optical coherence tomography , signal (programming language) , coherence (philosophical gambling strategy) , tomography , computer science , biological system , parametric statistics , optics , sample (material) , signal processing , materials science , biomedical engineering , physics , digital signal processing , mathematics , medicine , statistics , quantum mechanics , computer hardware , biology , programming language , thermodynamics
Photothermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the ability to generate qualitative maps of molecular absorptions co-registered with the micron-resolution structural tomograms of OCT. Obtaining refined insight into chemical information from PT-OCT images, however, requires solid understanding of the multifactorial physics behind generation of PT-OCT signals and their dependence on system and sample parameters. Such understanding is needed to decouple the various physical effects involved in the PT-OCT signal to obtain more accurate insight into sample composition. In this work, we propose an analytical model that considers the opto-thermo-mechanical properties of multi-layered samples in 3-D space, eliminating several assumptions that have been limiting previous PT-OCT models. In parametric studies, the model results are compared with experimental signals to investigate the effect of sample and system parameters on the acquired signals. The proposed model and the presented findings open the door for: 1) better understanding of the effects of system parameters and tissue opto-thermo-mechanical properties on experimental signals; 2) informed optimization of experimentation strategies based on sample and system parameters; 3) guidance of downstream signal processing for predicting tissue molecular composition.