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Proceedings Article

Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography

[+] Author Affiliations
Xuan Liu, Jin U. Kang

The Johns Hopkins Univ. (USA)

Proc. SPIE 7904, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII, 79041C (February 10, 2011); doi:10.1117/12.874058
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From Conference Volume 7904

  • Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII
  • Jose-Angel Conchello; Carol J. Cogswell; Tony Wilson; Thomas G. Brown
  • San Francisco, California, USA | January 22, 2011

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.

© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Xuan Liu and 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 (February 10, 2011); doi:10.1117/12.874058; http://dx.doi.org/10.1117/12.874058


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