Speckle Statistics of Biological Tissues in Optical Coherence Tomography

TL;DR

This study evaluates power-law based statistical models for OCT speckle data of biological tissues, finding they better fit the data than traditional models and could serve as disease biomarkers.

Contribution

It introduces and validates power-law distributions for modeling OCT speckle statistics, advancing tissue characterization methods.

Findings

Power-law distributions better fit OCT speckle data than traditional models.

The distributions may serve as biomarkers for tissue disease.

Power-law parameters could indicate tissue pathology.

Abstract

The speckle statistics of optical coherence tomography images of biological tissue have been studied using several historical probability density functions. A recent hypothesis implies that underlying power-law distributions in the medium structure, such as the fractal branching vasculature, will contribute to power-law probability distributions of speckle statistics. Specifically, these are the Burr type XII distribution for speckle amplitude, the Lomax distribution for intensity, and the generalized logistic distribution for log amplitude. In this study, these three distributions are fitted to histogram data from nine optical coherence tomography scans of various biological tissues and samples. The distributions are also compared with conventional distributions such as the Rayleigh, K, and gamma distributions. The results indicate that these newer distributions based on power laws are, in general, more appropriate models and support the plausibility of their use for characterizing biological tissue. Potentially, the governing power-law parameter of these distributions could be used as a biomarker for tissue disease or pathology.