Spectroscopic thermo-elastic optical coherence tomography for tissue characterization.
Aaron Doug DeenHeleen M M Van BeusekomTom PfeifferMathijs StamDominique De KleijnJolanda WentzelRobert HuberAntonius F W Van Der SteenGijs Van SoestTianshi WangPublished in: Biomedical optics express (2022)
Optical imaging techniques that provide free space, label free imaging are powerful tools in obtaining structural and biochemical information in biological samples. To date, most of the optical imaging technologies create images with a specific contrast and require multimodality integration to add additional contrast. In this study, we demonstrate spectroscopic Thermo-elastic Optical Coherence Tomography (TE-OCT) as a potential tool in tissue identification. TE-OCT creates images based on two different forms of contrast: optical reflectance and thermo-elastic deformation. TE-OCT uses short laser pulses to induce thermo-elastic tissue deformation and measures the resulting surface displacement using phase-sensitive OCT. In this work we characterized the relation between thermo-elastic displacement and optical absorption, excitation, fluence and illumination area. The experimental results were validated with a 2-dimensional analytical model. Using spectroscopic TE-OCT, the thermo-elastic spectra of elastic phantoms and tissue components in coronary arteries were extracted. Specific tissue components, particularly lipid, an important biomarker for identifying atherosclerotic lesions, can be identified in the TE-OCT spectral response. As a label-free, free-space, dual-contrast, all-optical imaging technique, spectroscopic TE-OCT holds promise for biomedical research and clinical pathology diagnosis.
Keyphrases
- optical coherence tomography
- high resolution
- diabetic retinopathy
- label free
- molecular docking
- optic nerve
- high speed
- magnetic resonance
- coronary artery
- healthcare
- deep learning
- magnetic resonance imaging
- fluorescence imaging
- social media
- health information
- fatty acid
- convolutional neural network
- big data
- molecular dynamics
- human health
- molecular dynamics simulations