KEYWORDS: Optical coherence tomography, Image quality, Machine learning, In vivo imaging, Tissues, Tomography, Signal analyzers, RGB color model, Lung, Inspection
Dynamic Optical Coherence Tomography combines high resolution tomographic imagery with a cell specific contrast by Fourier analysis. However, the conversion from frequency space into RGB images by binning requires a priori knowledge and artifacts due to global movements provide another obstacle for in vivo application.
We could show that an automated binning based on the Neural Gas algorithm can yield the highest spectral contrast without a priori knowledge and that motion artifacts can be reduced with shorter sequence lengths. Imaging murine airways, we observed that even just 6 frames are enough to generate dOCT images without losing important image information.
Here we present a forward-looking endoscope for dynamic microscopic OCT reaching a lateral resolution of 1.3 µm and 0.8 mm field of view. Since tissue motion degrades dynamic imaging, tissue was immobilized by suction. The endoscope was placed in a 4 mm stainless-steel sheath, which was connected to a vacuum pump. In mice, the endoscope can access various inner organs using open surgery or laparoscopy. The potential of the dynamic endo-microscopic OCT was demonstrated on relevant murine tissue such as liver, spleen and kidney. Otherwise invisible cellular and subcellular structures were imaged by dynamic mOCT with high contrast.
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