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This paper aims at imaging the dynamics of metabolic activity of cells. Usingdynamic optical coherence tomography, we introduce a new multi-particledynamical model to simulate the movements of the collagen and the cellmetabolic activity and develop an efficient signal separation technique forsub-cellular imaging. We perform a singular-value decomposition of the dynamicoptical images to isolate the intensity of the metabolic activity. We provethat the largest eigenvalue of the associated Casorati matrix corresponds tothe collagen. We present several numerical simulations to illustrate andvalidate our approach.

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