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  3. Using Principal Components Analysis To Visualize Motion And Mitigate Artifacts In Dynamic Optical Coherence Tomography.
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  3. Using Principal Components Analysis To Visualize Motion And Mitigate Artifacts In Dynamic Optical Coherence Tomography.

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Simultaneous Brightfield, Fluorescence, and Optical Coherence Tomographic Imaging of Contracting Cardiac Trabeculae Ex Vivo
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Using Principal Components Analysis to Visualize Motion and Mitigate Artifacts in Dynamic Optical Coherence

Alejandro Martínez Jiménez1,2,3, Adrian Bradu1

  • 1Applied Optics Group, School of Engineering, Mathematics and Physics, University of Kent, Canterbury, UK.

Journal of Biophotonics
|June 24, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Principal Component Analysis (PCA) in Dynamic Optical Coherence Tomography (DOCT) reduces artifacts and isolates sample movement. This method enables histology-like imaging of tissue without complex histopathology techniques.

Keywords:
dynamic OCThistopathologyoptical coherence tomographyprincipal component analysis

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Area of Science:

  • Biomedical Imaging
  • Optical Coherence Tomography
  • Medical Image Analysis

Background:

  • Dynamic Optical Coherence Tomography (DOCT) enhances visualization of dynamic biological processes using OCT signal fluctuations.
  • Principal Component Analysis (PCA) is a common medical imaging technique with underexplored applications in DOCT.
  • High-numerical-aperture objectives can introduce artifacts in DOCT imaging.

Purpose of the Study:

  • To investigate the efficacy of PCA in reducing artifacts in DOCT imaging.
  • To explore PCA's potential for isolating dynamic processes and reconstructing volumetric images.
  • To assess PCA's utility in generating histology-like tissue sections.

Main Methods:

  • Implementation of PCA for DOCT data processing.
  • Selection of significant principal components to minimize artifacts.
  • Application of the method to bovine kidney tissue samples.

    • Main Results:

    • PCA significantly reduced artifacts from specular reflections, especially with high-NA objectives.
    • Using a limited number of principal components effectively isolated sample movement.
    • Volumetric reconstruction and generation of thin, histology-like sections were achieved.

    Conclusions:

    • PCA is an effective tool for artifact reduction and dynamic process visualization in DOCT.
    • This PCA-based DOCT approach simplifies tissue imaging, bypassing complex histopathology.
    • The method offers a promising alternative for rapid, high-resolution tissue analysis.