Using Principal Components Analysis to Visualize Motion and Mitigate Artifacts in Dynamic Optical Coherence...

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.