Medium chromatic dispersion calculation and correction in spectral-domain optical coherence tomography

Matkivsky, V. A.; Moiseev, Alexander A.; Ksenofontov, S. Yu.; Kasatkina, I. V.; Gelikonov, Grigory V.; Shabanov, Dmitry V.; Shilyagin, Pavel A.; Gelikonov, V. M.

doi:10.1007/s12200-017-0736-2

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…At the second stage (Routine #2 in Figure ), which starts after completion of first calculation stage in all parallel threads, A‐scans data were simultaneously processed. Processing of each A‐scan at this stage began with the algorithm of material dispersion compensation , which consisted of element‐wise multiplication of obtained spectrum with predefined complex coefficients. Then compensation for wavenumber nonlinearity was applied.…”

Section: Methodsmentioning

confidence: 99%

Optical coherence tomography‐based angiography device with real‐time angiography B‐scans visualization and hand‐held probe for everyday clinical use

Moiseev

Ksenofontov

Sirotkina

et al. 2018

Journal of Biophotonics

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This work is dedicated to the development of the OCT system with angiography for everyday clinical use. Two major problems were solved during the development: compensation of specific natural tissue displacements, induced by contact scanning mode and physiological motion of patients (eg, respiratory and cardiac motions) and online visualization of vessel cross-sections to provide feedback for the system operator.

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Section: Methodsmentioning

confidence: 99%

Optical coherence tomography‐based angiography device with real‐time angiography B‐scans visualization and hand‐held probe for everyday clinical use

Moiseev

Ksenofontov

Sirotkina

et al. 2018

Journal of Biophotonics

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“…17 In our earlier work, we used a similar approach for compensating the influence of aberrations in digital holography, motion-compensation in full-field swept-source OCT, and dispersion in spectral-domain OCT imaging. [18][19][20] However, the direct application of this approach to ophthalmic data gave a poor result. Below we demonstrate how this method may be refined to the level that will allow determining strong eye aberrations.…”

Section: Introductionmentioning

confidence: 99%

“…This approach was initially applied to optical coherence tomography (OCT) data for digital refocusing 17 . In our earlier work, we used a similar approach for compensating the influence of aberrations in digital holography, motion‐compensation in full‐field swept‐source OCT, and dispersion in spectral‐domain OCT imaging 18–20 . However, the direct application of this approach to ophthalmic data gave a poor result.…”

Section: Introductionmentioning

confidence: 99%

Determination and correction of aberrations in full field optical coherence tomography using phase gradient autofocus by maximizing the likelihood function

Matkivsky

Moiseev

Shilyagin

et al. 2020

Journal of Biophotonics

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A method for numerical estimation and correction of aberrations of the eye in fundus imaging with optical coherence tomography (OCT) is presented. Aberrations are determined statistically by using the estimate based on likelihood function maximization. The method can be considered as an extension of the phase gradient autofocusing algorithm in synthetic aperture radar imaging to 2D optical aberration correction. The efficacy of the proposed method has been demonstrated in OCT fundus imaging with 6λ aberrations. After correction, single photoreceptors were resolved. It is also shown that wave front distortions with high spatial frequencies can be determined and corrected.

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“…The second one involves postprocessing techniques, including filtering, methods of extraction of "pure" signal, and deconvolution procedures. 41 Using more than one OCT scans for noise reduction has a significant drawback for in vivo imaging, when object movements become critical. In this case, the averaging should include algorithms of image alignment.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

et al. 2018

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We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.

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Medium chromatic dispersion calculation and correction in spectral-domain optical coherence tomography

Cited by 10 publications

References 23 publications

Optical coherence tomography‐based angiography device with real‐time angiography B‐scans visualization and hand‐held probe for everyday clinical use

Optical coherence tomography‐based angiography device with real‐time angiography B‐scans visualization and hand‐held probe for everyday clinical use

Determination and correction of aberrations in full field optical coherence tomography using phase gradient autofocus by maximizing the likelihood function

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

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