A signal separation technique for sub-cellular imaging using dynamic optical coherence tomography

TL;DR

This paper introduces a novel signal separation method using dynamic optical coherence tomography to image cellular metabolic activity and collagen movements, enabling detailed sub-cellular imaging.

Contribution

It develops a new multi-particle dynamical model and an efficient eigenvalue-based technique for isolating metabolic signals in optical coherence tomography images.

Findings

Successful simulation-based validation of the method

Eigenvalue analysis effectively separates collagen and metabolic signals

Enhanced imaging of sub-cellular structures achieved

Abstract

This paper aims at imaging the dynamics of metabolic activity of cells. Using dynamic optical coherence tomography, we introduce a new multi-particle dynamical model to simulate the movements of the collagen and the cell metabolic activity and develop an efficient signal separation technique for sub-cellular imaging. We perform a singular-value decomposition of the dynamic optical images to isolate the intensity of the metabolic activity. We prove that the largest eigenvalue of the associated Casorati matrix corresponds to the collagen. We present several numerical simulations to illustrate and validate our approach.