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High throughput photonic time stretch optical coherence tomography with data compression

Mididoddi, Chaitanya K., Bai, Fangliang, Wang, Guoqing, Liu, Jinchao, Gibson, Stuart, Wang, Chao (2017) High throughput photonic time stretch optical coherence tomography with data compression. IEEE Photonics Journal, 9 (4). Article Number 3901015. ISSN 1943-0655. (doi:10.1109/JPHOT.2017.2716179) (KAR id:62031)

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Photonic time stretch enables real time high throughput optical coherence tomography (OCT), but with massive data volume being a real challenge. In this paper, data compression in high throughput optical time stretch OCT has been explored and experimentally demonstrated. This is made possible by exploiting spectral sparsity of encoded optical pulse spectrum using compressive sensing (CS) approach. Both randomization and integration have been implemented in the optical domain avoiding an electronic bottleneck. A data compression ratio of 66% has been achieved in high throughput OCT measurements with 1.51 MHz axial scan rate using greatly reduced data sampling rate of 50 MS/s. Potential to improve compression ratio has been exploited. In addition, using a dual pulse integration method, capability of improving frequency measurement resolution in the proposed system has been demonstrated. A number of optimization algorithms for the reconstruction of the frequency-domain OCT signals have been compared in terms of reconstruction accuracy and efficiency. Our results show that the L1 Magic implementation of the primal-dual interior point method offers the best compromise between accuracy and reconstruction time of the time-stretch OCT signal tested.

Item Type: Article
DOI/Identification number: 10.1109/JPHOT.2017.2716179
Uncontrolled keywords: Optical coherence tomography, dispersion, photonic time stretch, compressive sensing.
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK5101 Telecommunications > TK5103.59 Optical communications
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Chao Wang
Date Deposited: 10 Jun 2017 23:10 UTC
Last Modified: 19 Mar 2021 15:08 UTC
Resource URI: (The current URI for this page, for reference purposes)
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