We have developed a highly realistic, Maxwell-based, model of an existing experimental optical coherence tomography based approach for characterizing blood cells flowing through a microfluidic channel. The characterization technique is indirect as it relies upon the perturbation, by blood cells, of light back-scattered by specially designed highly scattering substrate. This is in contrast with characterization techniques which directly sense light back-scattered by the cells. Up until now, our hypothesis for distinguishing between different blood cell types has been based upon experimental measurements and knowledge of cell morphology.
The absence of a mathematical model capable of modelling image formation, when the wave nature of light is integral, has impeded our ability to validate and optimize the characterization method. Recently, such a model has been developed and we have adapted it to simulate our experimental system and blood cells. The model has the following features: the field back scattered by the sample, for broadband and arbitrary profile beams, is calculated according to Maxwell’s equations; the sample is a deterministic refractive index distribution; the scattered and reference electric fields are explicitly interfered; single and multiple scattering are implicitly modeled; most system parameters of practical significance (e.g. numerical aperture or wavefront aberration) are included the model.
This model has been highly successful in replicating and allowing for interpretation of experimental results. We will present the key elements of the three-dimensional computational model, based upon Maxwell’s equations, as well as the key findings of the computational study. We shall also provide comparison with experimental results.
We demonstrate a novel label-free OCT method allowing optical detection and differentiation of moving micro-objects,
such as blood cells. In this study we use phase-sensitive Fd-OCT/OCM system with broadband light source (axial
resolution: 3 μm in tissue and lateral resolution 4−8 μm) and fluidic sample setup. The novel part of this method is that
the optical identification is based on optical signal coming from optically uniform scattering media localized beneath the
flowing/moving objects and not from the objects itself. This signal reveals as an enhancement in speckle pattern
on intensity images and non-zero phase change on phase images (phase-change). Statistical parameters of such signals
depend on the features of the object, like its size, shape, internal structure, etc. In order to demonstrate the effectiveness
and reliability of proposed method, we performed an experiment to differentiate erythrocytes from leukocytes. Obtained
OCT cross-sectional images present signal enhancement in the scattering base, both on intensity and phase-change
images. This modulation signals, corresponding to erythrocytes and leukocytes, are significantly different and could be
easily distinguished qualitatively. Statistical parameters used for the analysis also represent satisfactory separation
to distinguish between different kinds of cells. Above-mentioned results of differentiation are presented in this paper.