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

Xuan Liu, Jin U. Kang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationThree-Dimensional and Multidimensional Microscopy
Subtitle of host publicationImage Acquisition and Processing XVIII
DOIs
StatePublished - 2011
Externally publishedYes
EventThree-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII - San Francisco, CA, United States
Duration: Jan 24 2011Jan 27 2011

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume7904
ISSN (Print)1605-7422

Other

OtherThree-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII
Country/TerritoryUnited States
CitySan Francisco, CA
Period1/24/111/27/11

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Keywords

  • Image reconstruction techniques
  • Information theoretical analysis
  • Optical coherence tomography

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