Two-dimensional compressive sensing in spectral domain optical coherence tomography

2015 ◽  
Author(s):  
Daguang Xu ◽  
Yong Huang ◽  
Jin U. Kang
Keyword(s):  
Optical Coherence Tomography ◽  
Compressive Sensing ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Two Dimensional

scholarly journals Combination of 2D Compressive Sensing Spectral Domain Optical Coherence Tomography and Interferometric Synthetic Aperture Microscopy

2019 ◽  
Vol 9 (19) ◽  
pp. 4003
Author(s):  
Luying Yi ◽  
Liqun Sun ◽  
Xiangyu Guo ◽  
Bo Hou
Keyword(s):  
Optical Coherence Tomography ◽  
Compressive Sensing ◽  
Spectral Data ◽  
Reconstruction Algorithm ◽  
Spectral Domain ◽  
Synthetic Aperture ◽  
Lateral Resolution ◽  
Optical Coherence ◽  
Synthetic Aperture Microscopy ◽  
Oct Image

Combining the advantages of compressive sensing spectral domain optical coherence tomography (CS-SDOCT) and interferometric synthetic aperture microscopy (ISAM) in terms of data volume, imaging speed, and lateral resolution, we demonstrated how compressive sampling and ISAM can be simultaneously used to reconstruct an optical coherence tomography (OCT) image. Specifically, an OCT image is reconstructed from two-dimensional (2D) under-sampled spectral data dimension-by-dimension through a CS reconstruction algorithm. During the iterative process of CS algorithm, the deterioration of lateral resolution beyond the depth of focus (DOF) of a Gaussian beam is corrected. In the end, with less spectral data, we can obtain an OCT image with spatially invariant lateral resolution throughout the imaging depth. This method was verified in this paper by imaging the cells of an orange. A 0.7 × 1.5 mm image of an orange was reconstructed using only 50% × 50% spectral data, in which the dispersion of the structure was decreased by approximately 2.4 times at a depth of approximately 5.7 Rayleigh ranges above the focus. This result was consistent with that obtained with 100% data.

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