Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm

doi:10.1364/oe.395523

CIOMP OpenIR

	Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm
	D. Zhu, R. Y. Wang, M. Zurauskas, P. Pande, J. C. Bi, Q. Yuan, L. J. Wang, Z. S. Gao and S. A. Boppart
	2020
发表期刊	Optics Express
ISSN	1094-4087
卷号	28 期号:16 页码:23306-23319
摘要	The transverse resolution of optical coherence tomography is decreased by aberrations introduced from optical components and the tested samples. In this paper, an automated fast computational aberration correction method based on a stochastic parallel gradient descent (SPGD) algorithm is proposed for aberration-corrected imaging without adopting extra adaptive optics hardware components. A virtual phase filter constructed through combination of Zernike polynomials is adopted to eliminate the wavefront aberration, and their coefficients are stochastically estimated in parallel through the optimization of the image metrics. The feasibility of the proposed method is validated by a simulated resolution target image, in which the introduced aberration wavefront is estimated accurately and with fast convergence. The computation time for the aberration correction of a 512 x 512 pixel image from 7 terms to 12 terms requires little change, from 2.13 s to 2.35 s. The proposed method is then applied for samples with different scattering properties including a particle-based phantom, ex-vivo rabbit adipose tissue, and in-vivo human retina photoreceptors, respectively. Results indicate that diffraction-limited optical performance is recovered, and the maximum intensity increased nearly 3-fold for out-of-focus plane in particle-based tissue phantom. The SPGD algorithm shows great potential for aberration correction and improved run-time performance compared to our previous Resilient backpropagation (Rprop) algorithm when correcting for complex wavefront distortions. The fast computational aberration correction suggests that after further optimization our method can be integrated for future applications in real-time clinical imaging. (C) 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
DOI	10.1364/oe.395523
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收录类别	SCI ; EI
语种	英语
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/64325
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	D. Zhu, R. Y. Wang, M. Zurauskas, P. Pande, J. C. Bi, Q. Yuan, L. J. Wang, Z. S. Gao and S. A. Boppart. Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm[J]. Optics Express,2020,28(16):23306-23319.
APA	D. Zhu, R. Y. Wang, M. Zurauskas, P. Pande, J. C. Bi, Q. Yuan, L. J. Wang, Z. S. Gao and S. A. Boppart.(2020).Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm.Optics Express,28(16),23306-23319.
MLA	D. Zhu, R. Y. Wang, M. Zurauskas, P. Pande, J. C. Bi, Q. Yuan, L. J. Wang, Z. S. Gao and S. A. Boppart."Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm".Optics Express 28.16(2020):23306-23319.

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