Artefact reduction for cell migration visualization using spectral domain optical coherence tomography

Abstract

Visualization of cell migration during chemotaxis using spectral domain optical coherence tomography (OCT) requires non‐standard processing techniques. Stripe artefacts and camera noise floor present in OCT data prevent detailed computer‐assisted reconstruction and quantification of cell locomotion. Furthermore, imaging artefacts lead to unreliable results in automated texture based cell analysis. Here we characterize three pronounced artefacts that become visible when imaging sample structures with high dynamic range, e.g. cultured cells: (i) time‐varying fixed‐pattern noise; (ii) stripe artefacts generated by background estimation using tomogram averaging; (iii) image modulations due to spectral shaping. We evaluate techniques to minimize the above mentioned artefacts using an 800 nm optical coherence microscope. Effect of artefact reduction is shown exemplarily on two cell cultures, i.e. Dictyostelium on nitrocellulose substrate, and retinal ganglion cells (RGC‐5) cultured on a glass coverslip. Retinal imaging also profits from the proposed processing techniques. (© 2011 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Multi-shaping technique reduces sidelobe magnitude in optical coherence tomography

“…This paper presents a simple and straightforward technique that can suppress axial sidelobe magnitude without sacrificing the axial resolution in FD-OCT or adding significant complexity to the standard FD-OCT processing procedures. Our Multi-shaping approach is not only easier to implement than the alternative approaches that involve Gaussian fit, adaptive apodization or search of optimal shaping functions [7][8][9][10][11][12][13][14][15][16][17], but also avoids the usual tradeoffs of most techniques in which good mainlobe resolution is sacrificed for good sidelobe reduction. Most shaping techniques require precise measurement of the light source spectrum; in contrast, the performance of multi-shaping is independent of such measurements.…”

“…Most shaping techniques require precise measurement of the light source spectrum; in contrast, the performance of multi-shaping is independent of such measurements. To better define these differences, we compared the multi-shaping technique with an automatic spectral reshaping method using a Wiener filter [16,17] for the zebrafish data (presented in Fig. 7) and found that its performance offered results similar to the standard Gaussian shaping (CNR = 9.0, Fig.…”

“…High sidelobes lower the image contrast and confound small target signals. To suppress axial sidelobes, a number of methods have been used to shape the non-Gaussian spectra, including Gaussian fitting [7][8][9], Hamming/Hanning windowing functions [10], and correction of spectra shape directly on the light source [11,12] or in post-processing [13][14][15][16][17]. These methods suppress sidelobes, but at a cost: they typically sacrifice some axial resolution by widening the mainlobe in the axial PSF.…”

Data Analysis and Signal Postprocessing for Optical Coherence Tomography

“…Subsequently, each interferometric spectrum is subtracted by the reference spectrum to remove the fixed-pattern noise. However, a small number of high-amplitude back-reflections mainly due to the air-tissue interface can cause errors in estimating the mean reference spectrum [21,22]. A number of alternative approaches to overcome this limitation of the mean-spectrum subtraction method have been proposed.…”

“…Another method is based on the median estimator which is known to be less sensitive to high-amplitude data points. In this method, a complex median value is calculated for each axial position and this complex median A-line is then subtracted from each of the A-lines to remove the fixed-pattern noise artifacts [21][22][23].…”