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Published on: August 17, 2011
Predictive coding compressive sensing optical coherence tomography hardware implementation
Diego M Song Cho1, Haiqiu Yang2, Zizheng Jia2
1Department of Biomedical Engineering, Columbia University, 500 W 120th Street, New York, NY 10027, USA.
Abstract:
Compressed sensing (CS) is an approach that enables comprehensive imaging by reducing both imaging time and data density, and is a theory that enables undersampling far below the Nyquist sampling rate and guarantees high-accuracy image recovery. Prior efforts in the literature have focused on demonstrations of synthetic undersampling and reconstructions enabled by compressed sensing. In this paper, we demonstrate the first physical, hardware-based sub-Nyquist sampling with a galvanometer-based OCT system with subsequent reconstruction enabled by compressed sensing. Acquired images of a variety of samples, with volume scanning time reduced by 89% (12.5% compression rate), were successfully reconstructed with relative error (RE) of less than 20% and mean square error (MSE) of around 1%.
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