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

Dynamic Optical Coherence Tomography (DOCT) is an advanced imaging technique that uses temporal fluctuations in OCT signals to improve contrast and enhance visualization of dynamic processes such as motion and metabolic activity. Although various methods for implementing the DOCT algorithm have been proposed, the use of Principal Component Analysis (PCA), a commonly used technique in medical imaging, remains relatively underexplored in this area. Our study demonstrates that selecting only the most significant principal components in PCA can substantially reduce artifacts from strong specular reflections, particularly when high-numerical-aperture microscope objectives are used. Furthermore, by using a small number of principal components, we can isolate movement within the sample, successfully reconstruct volumetric images, and create thin, histology-like sections of bovine kidney tissue, avoiding the need for complex, time-consuming techniques used in clinical histopathology.