Open AccessSpatial convolution for mirror image suppression in Fourier domain optical coherence tomography
Author(s) -
Miao Zhang,
Lixin Ma,
Ping Yu
Publication year - 2017
Publication title -
optics letters/optics index
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.524
H-Index - 272
eISSN - 1071-2763
pISSN - 0146-9592
DOI - 10.1364/ol.42.000506
Subject(s) - optical coherence tomography , optics , convolution (computer science) , computer science , fourier transform , spatial frequency , frequency domain , image processing , fast fourier transform , computer vision , artificial intelligence , physics , algorithm , image (mathematics) , quantum mechanics , artificial neural network
We developed a spatial convolution approach for mirror image suppression in phase-modulated Fourier domain optical coherence tomography, and demonstrated it in vivo for small animal imaging. Utilizing the correlation among neighboring A-scans, the mirror image suppression process was simplified to a three-parameter convolution. By adjusting the three parameters, we can implement different Fourier domain sideband windows, which is important but complicated in existing approaches. By properly selecting the window size, we validated the spatial convolution approach on both simulated and experimental data, and showed that it is versatile, fast, and effective. The new approach reduced the computational cost by 32% and improved the mirror image suppression by 10%. We adapted the spatial convolution approach to a GPU accelerated system for ultrahigh-speed processing in 0.1 ms. The advantage of the ultrahigh speed was demonstrated in vivo for small animal imaging in a mouse model. The fast scanning and processing speed removed respiratory motion artifacts in the in vivo imaging.