Current Optics and Photonics

  • 제8권2호
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  • Pages.183-191
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  • 2024
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  • 2508-7266(pISSN)
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  • 2508-7274(eISSN)
한국광학회 (Optical Society of Korea)
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Open-loop Wavefront Correction Based on SH-U-net for Retinal Imaging System

  • Ming Hu (School of Science, Jiangnan University) ;
  • Lifa Hu (School of Science, Jiangnan University) ;
  • Hongyan Wang (School of Science, Jiangnan University) ;
  • Qi Zhang (School of Science, Jiangnan University) ;
  • Xingyu Xu (School of Science, Jiangnan University) ;
  • Lin Yu (School of Science, Jiangnan University) ;
  • Jingjing Wu (School of Science, Jiangnan University) ;
  • Yang Huang (School of Science, Jiangnan University)
  • 투고 : 2024.01.19
  • 심사 : 2024.03.24
  • 발행 : 2024.04.25
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초록

High-resolution retinal imaging based on adaptive optics (AO) is important for early diagnosis related to retinal diseases. However, in practical applications, closed-loop AO correction takes a relatively long time, and traditional open-loop correction methods have low accuracy in correction, leading to unsatisfactory imaging results. In this paper, a SH-U-net-based open-loop AO wavefront correction method is presented for a retinal AO imaging system. The SH-U-net builds a mathematical model of the entire AO system through data training, and the Root mean square (RMS) of the distorted wavefront is 0.08λ after correction in the simulation. Furthermore, it has been validated in experiments. The method improves the accuracy of wavefront correction and shortens the correction time.

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과제정보

National Natural Science Foundation of China (No. 61475152, No. 62205127); Fund for Key Laboratory of Electro-Optical Countermeasures Test & Evaluation Technology (GKCP2021001).

참고문헌

  1. R. Arunkumar and P. Karthigaikumar, "Multi-retinal disease classification by reduced deep learning features," Neural Comput. Appl. 28, 329-334 (2017).  https://doi.org/10.1007/s00521-015-2059-9
  2. S. Resnikoff, D. Pascolini, D. Etya'ale, I. Kocur, R. Pararajasegaram, G. P. Pokharel, and S. P. Mariotti, "Global data on visual impairment in the year 2002," Bull. World Health Organ. 82, 844-851 (2004). 
  3. M. Lombardo, S. Serrao, N. Devaney, M. Parravano, and G. Lombardo, "Adaptive optics technology for high-resolution retinal imaging," Sensors 13, 334-366 (2012).  https://doi.org/10.3390/s130100334
  4. M. A. Neil, R. Juskaitis, M. J. Booth, T. Wilson, T. Tanaka, and S. Kawata, "Adaptive aberration correction in a two-photon microscope," J. Microsc. 200, 105-108 (2000).  https://doi.org/10.1046/j.1365-2818.2000.00770.x
  5. K. M. Hampson, "Adaptive optics and vision," J Mod. Opt. 55, 3425-3467 (2008).  https://doi.org/10.1080/09500340802541777
  6. S. Nakatake, Y. Murakami, J. Funatsu, Y. Koyanagi, M. Akiyama, Y. Momozawa, T. Ishibashi, K.-H. Sonoda, and Y. Ikeda, "Early detection of cone photoreceptor cell loss in retinitis pigmentosa using adaptive optics scanning laser ophthalmoscopy," Graefes Arch. Clin. Exp. Ophthalmol. 257, 1169-1181 (2019).  https://doi.org/10.1007/s00417-019-04307-0
  7. N. Wynne, J. Carroll, and J. L. Duncan, "Promises and pitfalls of evaluating photoreceptor-based retinal disease with adaptive optics scanning light ophthalmoscopy (AOSLO)," Prog. Retin. Eye Res. 83, 100920 (2021). 
  8. L. Zhao, Y. Dai, J. Zhao, and X. Zhou, "Super-resolution pupil filtering for visual performance enhancement using adaptive optics," J. Mod. Opt. 65, 907-913 (2018).  https://doi.org/10.1080/09500340.2017.1414895
  9. D. R. Williams, S. A. Burns, D. T. Miller, and A. Roordam, "Evolution of adaptive optics retinal imaging," Biomed. Opt. Express 14, 1307-1338 (2023).  https://doi.org/10.1364/BOE.485371
  10. A. Lassoued, F. Zhang, K. Kurokawa, Y. Liu, J. A. Crowell, and D. T. Miller, "Measuring dysfunction of cone photoreceptors in retinitis pigmentosa with phase-sensitive AO-OCT," Proc. SPIE 11218, 1121815 (2020). 
  11. M. Reddikumar, J. Cervantes, Y. Otani, and B. Cense, "A 2.8-mm beam diameter system for retinal imaging with OCT and adaptive optics," Proc. SPIE 10711, 107110C (2018). 
  12. S. A. Burns, A. E. Elsner, K. A. Sapoznik, R. L. Warner, and T. J. Gast, "Adaptive optics imaging of the human retina," Prog. Retin. Eye Res. 68, 1-30 (2019).  https://doi.org/10.1016/j.preteyeres.2018.08.002
  13. M. Pircher and R. J. Zawadzki, "Review of adaptive optics OCT (AO-OCT): Principles and applications for retinal imaging," Biomed. Opt. Express 8, 2536-2562 (2017).  https://doi.org/10.1364/BOE.8.002536
  14. K. M. Hampson, R. Turcotte, D. T. Miller, K. Kurokawa, J. R. Males, N. Ji, and M. J. Booth, "Adaptive optics for high-resolution imaging," Nat. Rev. Methods Primers 1, 68 (2021). 
  15. J. Liang, D. R. Williams, and D. T. Miller, "Supernormal vision and high-resolution retinal imaging through adaptive optics," J. Opt. Soc. Am. A 14, 2884-2892 (1997).  https://doi.org/10.1364/JOSAA.14.002884
  16. E. J. Fernandez, I. Iglesias, and P. Artal, "Closed-loop adaptive optics in the human eye," Opt. Lett. 26, 746-748 (2001).  https://doi.org/10.1364/OL.26.000746
  17. Y. Jian, J. Xu, M. A. Gradowski, S. Bonora, R. J. Zawadzki, and M. V. Sarunic, "Wavefront sensorless adaptive optics optical coherence tomography for in vivo retinal imaging in mice," Biomed. Opt. Express 5, 547-559 (2014).  https://doi.org/10.1364/BOE.5.000547
  18. Q. Mu, Z. Cao, D. Li, L. Hu, and L. Xuan, "Liquid crystal based adaptive optics system to compensate both low and high order aberrations in a model eye," Opt. Express 15, 1946-1953 (2007).  https://doi.org/10.1364/OE.15.001946
  19. K. Ningning , L. Dayu, X. Mingliang, Q. Yue, and X. Li, "Liquid crystal adaptive optics system for retinal imaging operated on open-loop and double-pulse mode," Acta Opt. Sin. 32, 111002 (2012). 
  20. D. J. Wahl, R. Ng, M. J. Ju, Y. Jian, and M. V. Sarunic, "Sensorless adaptive optics multimodal en-face small animal retinal imaging," Biomed. Opt. Express 10, 252-267 (2019).  https://doi.org/10.1364/BOE.10.000252
  21. M. Azimipour, Justin V. Migacz, Robert J. Zawadzki, J. S. Werner, and R. S. Jonnal, "Functional retinal imaging using adaptive optics swept-source OCT at 1.6 MHz," Optica 6, 300-303 (2019).  https://doi.org/10.1364/OPTICA.6.000300
  22. Z. Qin, S. He, C. Yang, J. S.-Y. Yung, C. Chen, C. K.-S. Leung, K. Liu, and J. Y. Qu, "Adaptive optics two-photon microscopy enables near-diffraction-limited and functional retinal imaging in vivo," Light Sci. Appl. 9, 79 (2020). 
  23. Z. Xu, P. Yang, K. Hu, B. Xu, and H. Li, "Deep learning control model for adaptive optics systems," Appl. Opt. 58, 1998-2009 (2019).  https://doi.org/10.1364/AO.58.001998
  24. H. Ma, H. Liu, Y. Qiao, X. Li, and W. Zhang, "Numerical study of adaptive optics compensation based on convolutional neural networks," Opt. Commun. 433, 283-289 (2019).  https://doi.org/10.1016/j.optcom.2018.10.036
  25. X. Fei, J. Zhao, H. Zhao, D. Yun, and Y. Zhang, "Deblurring adaptive optics retinal images using deep convolutional neural networks," Biomed. Opt. Express 8, 5675-5687 (2017).  https://doi.org/10.1364/BOE.8.005675
  26. X. Ke, S. Yang, and Y. Wu, "Predictive control algorithms for adaptive optical wavefront correction in free-space optical communication," Curr. Opt. Photonics 5, 641-651 (2021). 
  27. L.-C. Lin, C.-H. Huang, Y.-F. Chen, D. Chu, and C.-J. Cheng, "Deep learning-assisted wavefront correction with sparse data for holographic tomography," Opt. Laser Eng. 154, 107010 (2022). 
  28. X.-Y. Li, C.-H. Wang, H. Xian, and W.-H. Jiang, "Real-time mode restoration algorithm for adaptive optics system," High Power Laser Part. Beams 14, 53-56 (2002). 
  29. L. Luoren and L. Jinling, "Research of PID control algorithm based on neural network," Energy Procedia 13, 6988-6993 (2011).  https://doi.org/10.1016/S1876-6102(14)00454-8
  30. O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," in Proc. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference (Munich, Germany, Oct. 5-9, 2015), pp. 234-241. 
  31. M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury, and C. Pal, "The importance of skip connections in biomedical image segmentation," in Proc. Deep Learning and Data Labeling for Medical Applications (Athens, Greece, Oct. 21, 2016) pp. 179-187. 
  32. J. Porter, A. Guirao, I. G. Cox, and D. R. Williams, "Monochromatic aberrations of the human eye in a large population," J. Opt. Soc. Am A 18, 1793-1803 (2001). 
  33. H. Hofer, P. Artal, B. Singer, J. L. Aragon, and D. R. Williams, "Dynamics of the eye's wave aberration," J. Opt. Soc. Am. A 18, 497-506 (2001). 
  34. K. M. Hampson, I. Munro, C. Paterson, and C. Dainty, "Weak correlation between the aberration dynamics of the human eye and the cardiopulmonary system," J. Opt. Soc. Am. A 22, 1241-1250 (2005).  https://doi.org/10.1364/JOSAA.22.001241
  35. T. Nirmaier, G. Pudasaini, and J. Bille, "Very fast wave-front measurements at the human eye with a custom CMOS-based Hartmann-Shack sensor," Opt. Express 11, 2704-2716 (2003).  https://doi.org/10.1364/OE.11.002704
  36. C. R. Vogel, D. W. Arathorn, A. Roorda, and A. Parker, "Retinal motion estimation in adaptive optics scanning laser ophthalmoscopy," Opt. Express 14, 487-497 (2006).  https://doi.org/10.1364/OPEX.14.000487
  37. D. X. Hammer, R. D. Ferguson, J. C. Magill, M. A. White, A. E. Elsner, and R. H. Webb, "Image stabilization for scanning laser ophthalmoscopy," Opt. Express 10, 1542-1549 (2002).  https://doi.org/10.1364/OE.10.001542
  38. Y.-Y. Yang, Q.-L. Hu, M. Hu, L. Jiang, J.-H. Feng, S.-X. Hua, J.-J. Wu, Z.-P. Su, and L.-F. Hu, "Research on improving the universality of adaptive optical retinal imaging system," Liq. Cryst. Displays 38, 563-573 (2023). https://doi.org/10.37188/CJLCD.2023-0019