Complex decorrelation averaging in optical coherence tomography: a way to reduce the effect of multiple scattering and improve image contrast in a dynamic scattering medium

Abstract

We demonstrate that complex decorrelation averaging can reduce the effect of multiple scattering and improve OCT imaging contrast. Complex decorrelation averaging calculates the product of an A-scan and the complex conjugate of a subsequent A-scan. The resultant signal is the product of the amplitudes and the phase difference. All these resulting complex signals at a particular location are then averaged. We take advantage of the fact that complex averaging, in contrast to conventional magnitude averaging, is sensitive to phase decorrelation. Sample motion that increases signal phase variance results in lower signal magnitude after complex averaging. Such motion preferentially results in a faster decorrelation of the multiple scattering signal when compared to the single scattering signal with each scattering event spreading the phase. This indicates that we may reduce multiple scattering by implementing complex decorrelation averaging to preferentially reduce the magnitude of the multiply scattered light signal in OCT images. By adjusting the time between phase-differenced A-scans, one can regulate the amount of measured decorrelation. We have performed experiments on liquid phantoms that give experimental evidence for this hypothesis. A substantial improvement in OCT image contrast using complex decorrelation averaging is demonstrated.

Aberration-diverse optical coherence tomography for suppression of multiple scattering and speckle

“…This adds complexities in live imaging environments where dynamic sample fluctuations can occur. Another class of techniques for MS suppression exploits different correlation characteristics between SS and MS photons [20][21][22], in which the MS signal becomes decorrelated across multiple acquisitions, while the SS signal maintains its correlation. Other related methods have been demonstrated to produce decorrelated speckle patterns [23,24].…”

“…In this paper, we use aberration-diverse OCT (AD-OCT) to extend this astigmatic imaging into the MS regime, by exploiting phase correlation behaviors (similar with [20,22]) to distinguish SS from MS photons in volumetric reconstructions. AD-OCT takes advantage of the principle that a diversified illumination point spread function (PSF) passes through different spatial regions of a scattering medium, and therefore generates differing realizations of MS signal.…”

Dynamic imaging and quantification of subcellular motion with eigen‐decomposition optical coherence tomography‐based variance analysis

“…Because all A‐lines in the 3D OCT dataset were piled up in the same linear scale, here the covariance matrix is equivalent to its corresponding correlation matrix (correlation coefficient R is calculated by the normalization of covariance against the intra‐group standard deviations, that is, ). Therefore, the step (a) shares a similar concept with the well‐accepted complex/phase decorrelation theory . However, because of the slow cell‐induced signal variation relative to the high sampling speed, apart from the static components, the dynamic components may also contribute to high correlation, therefore, the sensitivity of directly using correlation coefficient can be limited.…”

“…The pedesis‐like motions were detected by applying ED and ID analyses, respectively, and the extracted dynamic signals were then projected onto the en‐face plane through maximum intensity projection (MIP). For comparison purpose, the CNR of resulting images were calculated with respect to each gelatin concentration as : where μ s and σ s are the mean and SD of the signal region of interest (typically a region in the middle of FoV with a radius = 1 mm), and μ b and σ b are the mean and SD of the surrounding background region (2 mm < radius < 2.5 mm, which is outside of the intralipid contained reservoir). This experiment and related data analysis have been repeated five times to ensure the consistency of the conclusion.…”

Analysis of Industry-Related Flows by Optical Coherence Tomography—A Review

“…Another option is to average the complexvalued, Fourier-transformed, spectral-fringe signals before calculating the magnitude. Complex averaging increases the dynamic range by reducing the noise floor while maintaining similar signal values when compared to magnitude averaging (Thrane et al, 2017).…”