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

Xuan Liu; Jin U. Kang

doi:10.1117/12.874058

11 February 2011 Sparse OCT: optimizing compressed sensing in spectral domain optical coherence tomography

Xuan Liu, Jin U. Kang

Proceedings Volume 7904, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XVIII; 79041C (2011) https://doi.org/10.1117/12.874058
Event: SPIE BiOS, 2011, San Francisco, California, United States

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.

Citation Download 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 (11 February 2011); https://doi.org/10.1117/12.874058

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available