Abstract
This paper describes a complex correlation mapping algorithm for optical coherence angiography (cmOCA). The proposed algorithm avoids the signal-to-noise ratio dependence and exhibits low noise in vasculature imaging. The complex correlation coefficient of the signals, rather than that of the measured data are estimated, and two-step averaging is introduced. Algorithms of motion artifact removal based on non perfusing tissue detection using correlation are developed. The algorithms are implemented with Jones-matrix OCT. Simultaneous imaging of pigmented tissue and vasculature is also achieved using degree of polarization uniformity imaging with cmOCA. An application of cmOCA to in vivo posterior human eyes is presented to demonstrate that high-contrast images of patients' eyes can be obtained.
Highlights
Optical coherence angiography (OCA) is a non-invasive vasculature contrast technique based on optical coherence tomography (OCT)
We present signal-to-noise ratio (SNR)-correction and variance-reduction theories for the complex correlation coefficient estimation (Section 2)
Jones matrix OCT (JM-OCT) simultaneously measures four complex OCT signals that correspond to four polarization channels (P = 4)
Summary
Optical coherence angiography (OCA) is a non-invasive vasculature contrast technique based on optical coherence tomography (OCT). Several angiography methods and algorithms have been developed, including OCA based on Doppler phase-shift detection [9,10,11], high-pass filtering techniques [12,13,14,15,16,17,18], intensity variance [19], and phase-shift variance [20, 21]. The complex-correlation-based technique uses all of the complex information of OCT, and provides good vasculature contrast [27]. The correlation-based methods are sensitive to the mobility of scatterers and less sensitive to the Doppler angle [28]
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