Image Reconstruction from Nonuniform Samples in Spectral Domain Optical Coherence Tomography

2011 ◽  
Author(s):  
Jun Ke ◽  
Rui Zhu ◽  
Edmund Y. Lam
Keyword(s):  
Optical Coherence Tomography ◽  
Image Reconstruction ◽  
Spectral Domain ◽  
Optical Coherence

Image Reconstruction in Dioptric Media for Spectral Domain Optical Coherence Tomography

2011 ◽  
Vol 38 (5) ◽  
pp. 0504001 ◽  
Author(s):  
杨柳 Yang Liu ◽  
王川 Wang Chuan ◽  
丁志华 Ding Zhihua ◽  
洪威 Hong Wei ◽  
黄良敏 Huang Liangmin
Keyword(s):  
Optical Coherence Tomography ◽  
Image Reconstruction ◽  
Spectral Domain ◽  
Optical Coherence

scholarly journals Neural network-based image reconstruction in swept-source optical coherence tomography using undersampled spectral data

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yijie Zhang ◽  
Tairan Liu ◽  
Manmohan Singh ◽  
Ege Çetintaş ◽  
Yilin Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Optical Coherence Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Spectral Data ◽  
Spectral Domain ◽  
Optical Coherence ◽  
Swept Source ◽  
Spatial Aliasing ◽  
Spectral Domain Oct

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.

Is high-resolution spectral domain optical coherence tomography reliable in nystagmus?

2011 ◽  
Vol 97 (4) ◽  
pp. 534.1-536 ◽  
Author(s):  
Mervyn G Thomas ◽  
Anil Kumar ◽  
John R Thompson ◽  
Frank A Proudlock ◽  
Kees Straatman ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
High Resolution ◽  
Spectral Domain ◽  
Optical Coherence
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