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Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Updated: May 19, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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Published on: January 15, 2013

High-speed full-field swept-source dynamic optical coherence tomography enabled by neural-network-based image

Suzuyo Komeda1, Nobuhisa Tateno1, Yusong Liu1

  • 1Computational Optics Group, University of Tsukuba, Tsukuba, Ibaraki, Japan.

Biomedical Optics Express
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

We developed a neural network method to create high-definition dynamic optical coherence tomography (DOCT) images faster. This approach significantly reduces data size and processing time for high-throughput imaging.

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Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Computational Imaging

Background:

  • Full-field swept-source optical coherence microscopy (FF-SSOCM) offers high-definition OCT imaging.
  • High data volumes in dynamic OCT (DOCT) imaging limit throughput due to streaming and processing times.

Purpose of the Study:

  • To develop a neural network (NN)-based method for high-speed, high-definition DOCT imaging.
  • To significantly reduce data size and processing time for DOCT image acquisition.

Main Methods:

  • Implemented a neural network (NN)-based approach for DOCT imaging.
  • The NN model generates high-definition logarithmic intensity variance (LIV)-based DOCT images using only four OCT volumes.
  • Compared NN-generated images with conventional methods requiring 32 volumes.

Main Results:

  • The NN model successfully generated LIV images comparable to conventional methods.
  • Achieved an eightfold reduction in data size and transfer time (41 GB to 5.1 GB).
  • Reduced signal processing time from 3.3 hours to 57 minutes.

Conclusions:

  • NN-based DOCT significantly accelerates high-throughput imaging.
  • This method enhances efficiency in dynamic optical coherence microscopy.
  • Enables faster acquisition and analysis of high-definition DOCT data.