Compressive SD-OCT: the application of compressed sensing in spectral domain optical coherence tomography

We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD...

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Bibliographic Details
Published inOptics express Vol. 18; no. 21; pp. 22010 - 22019
Main Authors Liu, Xuan, Kang, Jin U.
Format Journal Article
LanguageEnglish
Published United States Optical Society of America 11.10.2010
Subjects
Algorithms
Biosensing Techniques
Electronic Data Processing
Equipment Design
Fourier Analysis
Image Processing, Computer-Assisted - methods
Interferometry - methods
Microscopy - methods
Models, Statistical
Onions - physiology
Optics and Photonics
Tomography, Optical Coherence - methods
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Summary:We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the l(1) norm of a transformed image to enforce sparsity, subject to data consistency constraints. CS could allow an array detector with fewer pixels to reconstruct high resolution OCT images while reducing the total amount of data required to process the images.
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ISSN:1094-4087
1094-4087
DOI:10.1364/OE.18.022010