KEYWORDS: Optical coherence tomography, Autocorrelation, Simulations, Statistical analysis, Nonuniform sampling, Image compression, Biological imaging, Signal to noise ratio, Signal processing, Monte Carlo methods
SignificanceOptical coherence tomography has great utility for capturing dynamic processes, but such applications are particularly data-intensive. Samples such as biological tissues exhibit temporal features at varying time scales, which makes data reduction challenging.AimWe propose a method for capturing short- and long-term correlations of a sample in a compressed way using non-uniform temporal sampling to reduce scan time and memory overhead.ApproachThe proposed method separates the relative contributions of white noise, fluctuating features, and stationary features. The method is demonstrated on mammary epithelial cell spheroids in three-dimensional culture for capturing intracellular motility without loss of signal integrity.ResultsResults show that the spatial patterns of motility are preserved and that hypothesis tests of spheroids treated with blebbistatin, a motor protein inhibitor, are unchanged with up to eightfold compression.ConclusionsThe ability to measure short- and long-term correlations compressively will enable new applications in (3+1)D imaging and high-throughput screening.
OCT has been widely employed for capturing dynamic processes in biological tissues via (2+1)D imaging, but are currently limited in (3+1)D imaging by both scan time and storage efficiency. Temporal non-uniform compressive sampling has been previously demonstrated on both simulations and existing data with accurate reconstruction of intracellular motility with up to 8-fold compression. Here we present the experimental implementation of this technique on a spectral-domain OCT system, with demonstration of significantly reduced memory overhead and total imaging time at 4- and 8-fold compression, which enables new applications in high-throughput assays employing (3+1)D imaging.
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