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arXiv · 2402.09428

Neural-network based high-speed volumetric dynamic optical coherence tomography

Yusong Liu·Ibrahim Abd El-Sadek·Rion Morishita·Shuichi Makita·Tomoko Mori·Atsuko Furukawa·Satoshi Matsusaka·Yoshiaki Yasuno
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Abstract

Wedemonstratedeep-learningneuralnetwork(NN)-baseddynamicopticalcoherence tomography (DOCT), which generates high-quality logarithmic-intensity-variance (LIV) DOCT images from only four OCT frames. The NN model is trained for tumor spheroid samples using a customized loss function: the weighted mean absolute error. This loss function enables highly accurate LIV image generation. The fidelity of the generated LIV images to the ground truth LIV images generated using 32 OCT frames is examined via subjective image observation and statistical analysis of image-based metrics. Fast volumetric DOCT imaging with an acquisition time of 6.55 s/volume is demonstrated using this NN-based method.

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BibTeXRIS

Yusong Liu, Ibrahim Abd El-Sadek, Rion Morishita, Shuichi Makita, Tomoko Mori, Atsuko Furukawa, Satoshi Matsusaka, Yoshiaki Yasuno. 2024-01-24. Neural-network based high-speed volumetric dynamic optical coherence tomography. https://doi.org/10.1364/boe.519964

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