Analysis of scattering statistics and governing distribution functions in optical coherence tomography

Abstract

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 Kdistribution 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.

K‐distribution three‐dimensional mapping of biological tissues in optical coherence tomography

“…We thus expect the resultant α maps to be homogeneous, and from the resultant fluctuations we can evaluate the range of random errors. Further, knowing N permits the accuracy assessment of the K‐distribution approach through its expected dependence N = α/2 . Studies in this control phantom also enable examination of optimal and practical conditions for the size of the 3D evaluation window.…”

“…Signal processing was performed through the following steps. (1) Spectral domain OCT signal processing: wavenumber k ‐linearization and numerical dispersion compensation (second and third orders) for the acquired interferogram, zero‐padding (from 1024 to 4096) and inverse Fourier transform; (2) sample surface segmentation and flattening: the surface was detected by OCT intensity change in each B‐scan image after median filtering, and then the 3D volume data was reconfigured to have a flat surface; (3) K‐distribution fitting: OCT intensity histogram in a 3D sliding evaluation window ( x × y × z : 36 × 36 × 72 or 18 × 18 × 72 voxels—the larger number of pixels in the depth direction is chosen based on the higher axial resolution of OCT) was fitted to the normalized K‐distribution PDF expressed as : where Γ( x ) is the Gamma function, K ν ( x ) is a modified Bessel function of the second kind and α is the shape parameter used for fitting. The 3D evaluation window was moved along either a B‐scan or a C‐scan ( en face ) plane in its two dimensions, and the fitting was performed at every 8 pixel step in each direction.…”

“…Signal processing was performed through the following steps. (1) Spectral domain OCT signal processing: wavenumber k-linearization and numerical dispersion compensation (second and third orders) for the acquired interferogram, zero-padding (from 1024 to 4096) and inverse Fourier transform; (2) sample surface segmentation and flattening: the surface was detected by OCT intensity change in each Bscan image after median filtering, and then the 3D volume data was reconfigured to have a flat surface; (3) Kdistribution fitting: OCT intensity histogram in a 3D sliding evaluation window (x × y × z: 36 × 36 × 72 or 18 × 18 × 72 voxels-the larger number of pixels in the depth direction is chosen based on the higher axial resolution of OCT) was fitted to the normalized K-distribution PDF expressed as [8]:…”

“…After selecting a suitably sized 3D evaluation window, and verifying methodology in phantoms, the resultant parametric α images obtained in different animal tissues (rat liver and brain) show new contrasting ability not seen in conventional OCT intensity images. In optical investigations of turbid media, the K-distribution has been employed to represent the non-Gaussian optical scattering regime in both theoretical and experimental studies [1][2][3][4][5][6] Among the variety of optical measurement and analysis methodologies with the K-distribution model, its application to optical coherence tomography (OCT) analysis of the intensity probability density function (PDF) has yielded successful estimation of the number of scatterers within the coherence volume (eg, in the microspheres-inwater phantom system, ranging from a few to >50) [7,8]. Initial possibilities for biological tissue characterization have also been demonstrated in in vivo human skin and nail [8].…”

Alternative Contrast Mechanism in Optical Coherence Tomography: Temporal Speckle Synchronization Effects

“…While adding to noise, speckle characteristics are also known to contain useful information related to tissue type [3], cellularity [4], response to therapy [5] and other quantities of interest that are not directly visible nor spatially resolved in structural B-mode OCT images. Potential applications of speckle patterns for quantitative analysis and interpretation of OCT images is currently a subject of growing interest among research community [6][7][8][9][10]. Newly developed and well-established methods have been used to analyse OCT speckle patterns.…”