Optical coherence tomography velocimetry based on decorrelation estimation of phasor pair ratios (DEPPAIR)

Abstract

Quantitative velocity estimations in optical coherence tomography requires the estimation of the axial and lateral flow components. Optical coherence tomography measures the depth resolved complex field reflected from a sample. While the axial velocity component can be determined from the Doppler shift or phase shift between a pair of consecutive measurements at the same location, the estimation of the lateral component for in vivo applications is still challenging. One approach to determine lateral velocity is multiple simultaneous measurements at different angles. In another approach the lateral component can be retrieved through repeated measurements at (nearly) the same location by an analysis of the decorrelation over time. In this paper we follow the latter approach. We describe a model for the complex field changes between consecutive measurements and use it to predict the uncertainties for amplitude-based, phase-based and complex algorithms. The uncertainty of the flow estimations follows from a statistical analysis and is determined by the number of available measurements and the applied analysis method. The model is verified in phantom measurements and the dynamic range of velocity estimations is investigated. We demonstrate that phase-based and complex (phasor) based lateral flow estimation methods are superior to amplitude-based algorithms.

Endoscopic optical coherence tomography angiography using inverse SNR-amplitude decorrelation features and electrothermal micro-electro-mechanical system raster scan

“…Furthermore, existing methods lack a clear relation of motion contrast to SNR, but the asymptotic IDa relation and its distribution variance were rigorously derived in this study. Furthermore, in our study, the simulated results agree well with the theoretical calculation, which differed from the variance analysis based on Cramer-Rao lower bound (CRLB), where the variance is not accurate and serious outliers appear in the case of a practical and limited kernel size (52). In addition, by taking the advantage of accurate derivation, there is potential to further enhance the classification accuracy by dividing the IDa space more precisely and combining additional features (49).…”

Optical coherence tomography

“…Best OCTA performance so far seems to be obtained by recurring to both domains and using the full complex OCT signal for decorrelation or motion analysis. [78][79][80]…”

Using the Dynamic Forward Scattering Signal for Optical Coherence Tomography based Blood Flow Quantification

“…1 is an assumed statistical model for the DFS signals. While there have been many efforts to define the statistical model for DBS signals [3, 4, 5, 6, 7, 8, 9], there is no such body of work for DFS. Therefore, we note it is possible that the DFS signal follows a different functional dependence on τ than that of Eq.…”