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Related Experiment Video

Updated: Jun 24, 2025

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

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

Yusong Liu1, Ibrahim Abd El-Sadek1,2, Rion Morishita1

  • 1Computational Optics Group, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan.

Biomedical Optics Express
|June 10, 2024
PubMed
Summary

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Dynamic optical coherence tomography algorithm for label-free assessment of swiftness and occupancy of intratissue moving scatterers.

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Deep-learning dynamic optical coherence tomography (DOCT) generates high-quality logarithmic-intensity-variance (LIV) images from minimal data. This novel approach significantly speeds up volumetric imaging for enhanced biological sample analysis.

Area of Science:

  • Biomedical Optics
  • Medical Imaging
  • Machine Learning in Medicine

Background:

  • Dynamic Optical Coherence Tomography (DOCT) provides functional imaging capabilities.
  • High-quality image reconstruction in DOCT often requires extensive data acquisition.
  • Accelerating DOCT imaging is crucial for in vivo and dynamic biological studies.

Purpose of the Study:

  • To develop a deep-learning neural network (NN) for rapid, high-fidelity Logarithmic-Intensity-Variance (LIV) DOCT image reconstruction.
  • To reduce the number of optical coherence tomography (OCT) frames required for accurate LIV DOCT imaging.
  • To demonstrate fast volumetric DOCT imaging using the developed NN model.

Main Methods:

  • A deep-learning neural network (NN) model was designed for LIV DOCT image generation.

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Last Updated: Jun 24, 2025

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
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Published on: February 9, 2019

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  • The NN was trained using tumor spheroid samples with a custom weighted mean absolute error loss function.
  • Image quality was validated against ground truth images generated from 32 OCT frames using subjective and objective metrics.

    • Main Results:

    • High-quality LIV DOCT images were successfully generated from only four OCT frames.
    • The NN-based method achieved high fidelity compared to traditional methods requiring significantly more frames.
    • Fast volumetric DOCT imaging was demonstrated with an acquisition time of 6.55 seconds per volume.

    Conclusions:

    • Deep-learning significantly enhances the speed and efficiency of LIV DOCT image reconstruction.
    • The NN-based approach enables high-quality functional imaging with reduced data acquisition.
    • This method holds promise for accelerating dynamic biological and medical imaging applications.