Scaled nonuniform Fourier transform for image reconstruction in swept source optical coherence tomography

Author(s):  
Biniyam Kahsay Mezgebo ◽  
Karim Nagib ◽  
Namal Fernando ◽  
Behzad Kordi ◽  
Sherif Sherif
2009 ◽  
Vol 7 (10) ◽  
pp. 941-944 ◽  
Author(s):  
吴彤 Tong Wu ◽  
丁志华 Zhihua Ding ◽  
王凯 Kai Wang ◽  
王川 Chuan Wang

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yijie Zhang ◽  
Tairan Liu ◽  
Manmohan Singh ◽  
Ege Çetintaş ◽  
Yilin Luo ◽  
...  

AbstractOptical coherence tomography (OCT) is a widely used non-invasive biomedical imaging modality that can rapidly provide volumetric images of samples. Here, we present a deep learning-based image reconstruction framework that can generate swept-source OCT (SS-OCT) images using undersampled spectral data, without any spatial aliasing artifacts. This neural network-based image reconstruction does not require any hardware changes to the optical setup and can be easily integrated with existing swept-source or spectral-domain OCT systems to reduce the amount of raw spectral data to be acquired. To show the efficacy of this framework, we trained and blindly tested a deep neural network using mouse embryo samples imaged by an SS-OCT system. Using 2-fold undersampled spectral data (i.e., 640 spectral points per A-line), the trained neural network can blindly reconstruct 512 A-lines in 0.59 ms using multiple graphics-processing units (GPUs), removing spatial aliasing artifacts due to spectral undersampling, also presenting a very good match to the images of the same samples, reconstructed using the full spectral OCT data (i.e., 1280 spectral points per A-line). We also successfully demonstrate that this framework can be further extended to process 3× undersampled spectral data per A-line, with some performance degradation in the reconstructed image quality compared to 2× spectral undersampling. Furthermore, an A-line-optimized undersampling method is presented by jointly optimizing the spectral sampling locations and the corresponding image reconstruction network, which improved the overall imaging performance using less spectral data points per A-line compared to 2× or 3× spectral undersampling results. This deep learning-enabled image reconstruction approach can be broadly used in various forms of spectral-domain OCT systems, helping to increase their imaging speed without sacrificing image resolution and signal-to-noise ratio.


2020 ◽  
Vol 10 (14) ◽  
pp. 4936
Author(s):  
Pingping Jia ◽  
Hong Zhao ◽  
Yuwei Qin

A high-speed, high-resolution swept-source optical coherence tomography (SS-OCT) is presented for focusing lens imaging and a k-domain uniform algorithm is adopted to find the wave number phase equalization. The radius of curvature of the laser focusing lens was obtained using a curve-fitting algorithm. The experimental results demonstrate that the measuring accuracy of the proposed SS-OCT system is higher than the laser confocal microscope. The SS-OCT system has great potential for surface topography measurement and defect inspection of the focusing lens.


Author(s):  
José Ignacio Fernández-Vigo ◽  
Hang Shi ◽  
Bárbara Burgos-Blasco ◽  
Lucía De-Pablo-Gómez-de-Liaño ◽  
Ignacio Almorín-Fernández-Vigo ◽  
...  

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