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.
Pawel Ossowski, Maciej Wojtkowski, and Peter R. T. Munro, "Characterization of flowing blood cells using a novel OCT technique: rigorous three-dimensional computational study (Conference Presentation)," Proc. SPIE 10053, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXI, 1005311 (Presented at SPIE BiOS: January 31, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2254800.5371723148001.
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