Abstract
The probability density function (PDF) of light scattering intensity can be used to characterize the scattering medium. We have recently shown that in optical coherence tomography (OCT), a PDF formalism can be sensitive to the number of scatterers in the probed scattering volume and can be represented by the K-distribution, a functional descriptor for non-Gaussian scattering statistics. Expanding on this initial finding, here we examine polystyrene microsphere phantoms with different sphere sizes and concentrations, and also human skin and fingernail in vivo. It is demonstrated that the K-distribution offers an accurate representation for the measured OCT PDFs. The behavior of the shape parameter of K-distribution that best fits the OCT scattering results is investigated in detail, and the applicability of this methodology for biological tissue characterization is demonstrated and discussed.
Highlights
Biomedical applications of light scattering phenomena have been actively investigated, yielding useful insights for tissue diagnosis, imaging and characterization [1]
The Gaussian distribution can represent the statistics of complex electric field of the light A, the complex phasor of which (ReA, ImA) is treated as a 2D random walk vector, and the real and imaginary parts are independent of each other to provide two degrees of freedom (DOF) in the model
If the corresponding light field intensity (I = |A|2) distribution is written as a probability density function (PDF) of P(I), this leads to the χ2-distribution, represented by a negative exponential function
Summary
Biomedical applications of light scattering phenomena have been actively investigated, yielding useful insights for tissue diagnosis, imaging and characterization [1] Coherent optical techniques, such as optical coherence tomography (OCT), were developed to acquire high-resolution, depth-resolved images even in the presence of severe scattering / turbidity, enabling micron-scale 3D visualization of biological tissue microstructure [2,3]. Despite the rapid development and implementation of such techniques, there are still under-explored approaches to OCT signal analysis that hold promise for extraction of additional signal information that is otherwise not directly visible on an OCT image. One such approach is the probability density function (PDF) formalism.
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