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The inevitable distortions in optical coherence tomography (OCT) imaging often lead to mismatches between the imaging space and the real space, significantly affecting measurement accuracy. To address this issue, this study proposes a machine learning-based OCT image distortion correction method. A calibration plate with uniformly distributed circular hole arrays was sequentially imaged at different marked planes. The point showing minimal deviation between its coordinates and the mean coordinates across all imaging planes was selected as the reference marker. A mathematical model was then used to reconstruct all marker point coordinates in the reference plane, establishing a mapping relationship between the calibration plate's imaging space and the real physical space. A multilayer perceptron (MLP) was employed to learn this mapping relationship. The network architecture consisted of multiple fully connected modules, each containing a linear layer and an activation function except for the output layer. The optimal model was selected based on validation set performance and subsequently applied to analyze the spatial distribution of points. Using a swept-source OCT system, lens images were acquired and corrected through the trained model to obtain the anterior surface point cloud. Combined with ray tracing reconstruction of the posterior surface, the lens curvature radius and central thickness were calculated. Experimental results demonstrated that after correction, the lens curvature radius was measured with an accuracy of 10μm (error < 1%), while the central thickness was determined with a precision of 3μm (relative error: 0.3%). This method demonstrates high precision and reliability, offering an effective solution for improving OCT measurement accuracy.
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[1] Huang D, Swanson E A, Lin C P, Schuman J S, Stinson W G, Chang W, Hee M R, Flotte T, Gregory K, Puliafito C A, Fujimoto J G 1991Science 254 1178
[2] Tomlins P H, Wang R K 2005J. Phys. D- Appl. Phys. 38 2519
[3] Ding Y T, Zhang J, Guo Y, Chen H 2024Laser Optoelectron. Prog. 61 247(in Chinese) [丁宇韬，张君，郭遥，陈昊2024激光与光电子学进展61 247]
[4] Volker W, Andrew R, Sunita R, Joseph I 2002Opt. Express 10 397
[5] Diaz J, Rahlves M, Majdani O, Reithmeier E, Ortmaier T 2012Optical Metrology and Inspection for Industrial Applications II Beijing, China, November 5-7，2012 p8563J
[6] Yao J, Anderson A, Rolland J P 2018Opt. Express 26 10242
[7] Jiang X Q, Fan Y Q, Wang W 2014J. Front. Comput. Sci. Technol 8 1254(in Chinese) [江祥奎, 范永青, 王婉2014计算机科学与探索8 1254]
[8] Qi Z S, Wang Z, Huang J H, Xue Q, Gao J M 2016Acta Photonica Sin. 45 87(in Chinese) [齐召帅，王昭，黄军辉，薛琦，高建民2016光子学报45 87]
[9] Lecun Y, Bottou L, Bengio Y, Haffner P 1998Proc. IEEE 86 2278
[10] Li X Y, Zhang B, Sander P V, Liao J 2019Computer Vision and Pattern Recognition Long Beach, CA,USA, June 15-20，2019 p4850
[11] Yang S R, Lin C Y, Liao K, Zhang C J, Zhao Y 2021 arXiv:2103.16026v2[cs.CV]
[12] Xu Q W, Wang P P, Zeng Z J, Huang Z B, Zhou X X, Liu J M, Li Y, Chen S Q, Fan D Y 2020Acta Phys. Sin. 69 014209(in Chinese) [徐启伟, 王佩佩, 曾镇佳, 黄泽斌, 周新星, 刘俊敏, 李瑛, 陈书青, 范滇元2020 69 014209]
[13] Zhou J, Zhang X F, Zhao Y G 2021Acta Phys. Sin. 70 054201(in Chinese) [周静, 张晓芳, 赵延庚2021 70 054201]
[14] Shukla S, Vishwakarma C, Sah A N, Ahirwar S, Pandey K, Pradhan A 2023Appl. Opt. 62 6826
[15] Breiman L, 2001Mach. Learn. 45 5
[16] Xie T Y, Li Y M, Zhang T, Gao T L, Shi Y C 2023Mod. Mach. Tool Autom. Manuf. Tech. 237(in Chinese) [谢探阳，李玉梅，张涛，高天亮，石玉超2023组合机床与自动化加工技术237]
[17] Kato D, Maeda N, Hirogaki T, Aoyama E, Takahashi K 202121st International Conference on Control, Automation and Systems Jeju, Korea, October 12-15，2021 p607
[18] Werbos P J 1981System Modeling and Optimization Berlin, Germany, July 20-24, 1981 p762
[19] Rumelhart D E, Hinton G E, Williams R J 1986Nature 323 533
[20] Wang Y L, Zhang N, Zhang X Y, Xu Y Z, Wang M 2025Acta Opt. Sin. 45 3(in Chinese) [王艳丽,张宁,张祥宇,许益彰,王密2025光学学报45 3]
[21] Singarimbun R N, Nababan E B, Sitompul O S 2019International Conference of Computer Science and Information Technology Medan, Indonesia, November 28-29，2019 p1
[22] Cotter A, Shamir O, Srebro N, Sridharan K 2011Advances in Neural Information Processing Systems Granada, Spain, December 12-15, 2011 p1647
[23] Fitzgibbon A, Pilu M, Fisher R B 1999IEEE Trans. Pattern Anal. Mach. Intell. 21 476
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