Open AccessVolumetric (3D) compressive sensing spectral domain optical coherence tomography
Author(s) -
Daguang Xu,
Yong Huang,
Jin U. Kang
Publication year - 2014
Publication title -
biomedical optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.5.003921
Subject(s) - optical coherence tomography , compressed sensing , spectral imaging , image quality , iterative reconstruction , dimension (graph theory) , optics , computer science , computer vision , tomography , volume (thermodynamics) , nyquist rate , nyquist–shannon sampling theorem , artificial intelligence , sampling (signal processing) , image (mathematics) , mathematics , physics , quantum mechanics , pure mathematics , filter (signal processing)
In this work, we proposed a novel three-dimensional compressive sensing (CS) approach for spectral domain optical coherence tomography (SD OCT) volumetric image acquisition and reconstruction. Instead of taking a spectral volume whose size is the same as that of the volumetric image, our method uses a sub set of the original spectral volume that is under-sampled in all three dimensions, which reduces the amount of spectral measurements to less than 20% of that required by the Shan-non/Nyquist theory. The 3D image is recovered from the under-sampled spectral data dimension-by-dimension using the proposed three-step CS reconstruction strategy. Experimental results show that our method can significantly reduce the sampling rate required for a volumetric SD OCT image while preserving the image quality.