11 February 2011 Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography
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Abstract
We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD-OCT). Namely, CS was applied to the spectral data in reconstructing A-mode images. This would eliminate the need for a large amount of spectral data for image reconstruction and processing. We tested the CS method by randomly undersampling k-space SD-OCT signal. OCT images are reconstructed by solving an optimization problem that minimizes the l1 norm to enforce sparsity, subject to data consistency constraints. Variable density random sampling and uniform density random sampling were studied and compared, which shows the former undersampling scheme can achieve accurate signal recovery using less data.
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Xuan Liu, Jin U. Kang, "Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography", Proc. SPIE 7904, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII, 79041C (11 February 2011); doi: 10.1117/12.874058; https://doi.org/10.1117/12.874058
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