Optics and Precision Engineering, Volume. 32, Issue 12, 1812(2024)
Super-resolution strain measurement in phase-contrast optical coherence elastography
[1] J F WANG, Y XU, S A BOPPART. Review of optical coherence tomography in oncology. Journal of Biomedical Optics, 22, 1-23(2017).
[2] J YI, A J RADOSEVICH, J D ROGERS et al. Can OCT be sensitive to nanoscale structural alterations in biological tissue?. Optics Express, 21, 9043-9059(2013).
[3] F ABDOLAHI, X ZHOU, B S ASHIMATEY et al. Optical coherence tomography angiography-derived flux as a measure of physiological changes in retinal capillary blood flow. Translational Vision Science & Technology, 10, 5(2021).
[4] V Y ZAITSEV, A L MATVEYEV, L A MATVEEV et al. Strain and elasticity imaging in compression optical coherence elastography: the two-decade perspective and recent advances. Journal of Biophotonics, 14(2021).
[5] S WANG, K V LARIN. Optical coherence elastography for tissue characterization: a review. Journal of Biophotonics, 8, 279-302(2015).
[6] K M KENNEDY, L CHIN, R A MCLAUGHLIN et al. Quantitative micro-elastography: imaging of tissue elasticity using compression optical coherence elastography. Scientific Reports, 5, 15538(2015).
[7] E V GUBARKOVA, A A SOVETSKY, L A MATVEEV et al. Nonlinear elasticity assessment with optical coherence elastography for high-selectivity differentiation of breast cancer tissues. Materials, 15, 3308(2022).
[8] P B HUANG, Y K LIN, R F YOU et al. Simultaneously measurement of strain field and Poisson’s ratio by using an off-axis phase-sensitive optical coherence elastography. Measurement Science and Technology, 33(2022).
[9] Y M ALEXANDROVSKAYA, E M KASIANENKO, A A SOVETSKY et al. Spatio-temporal dynamics of diffusion-associated deformations of biological tissues and polyacrylamide gels observed with optical coherence elastography. Materials, 16, 2036(2023).
[10] D C GORNIG, R MALETZ, P OTTL et al. Influence of artificial aging: mechanical and physicochemical properties of dental composites under static and dynamic compression. Clinical Oral Investigations, 26, 1491-1504(2022).
[11] K V LARIN, D D SAMPSON. Optical coherence elastography - OCT at work in tissue biomechanics. Biomedical Optics Express, 8, 1172-1202(2017).
[12] V Y ZAITSEV, A L MATVEYEV, L A MATVEEV et al. Deformation-induced speckle-pattern evolution and feasibility of correlational speckle tracking in optical coherence elastography. Journal of Biomedical Optics, 20, 75006(2015).
[13] S M MOTAGHIANNEZAM, D KOOS, S E FRASER. Differential phase-contrast, swept-source optical coherence tomography at 1060 nm for
[14] W DREXLER. Ultrahigh-resolution optical coherence tomography. Journal of Biomedical Optics, 9, 47-74(2004).
[15] WIT J DE, K ANGELOPOULOS, J KALKMAN et al. Fast and accurate spectral-estimation axial super-resolution optical coherence tomography. Optics Express, 29, 39946-39966(2021).
[16] K M RATHEESH, L K SEAH, V M MURUKESHAN. Spectral phase-based automatic calibration scheme for swept source-based optical coherence tomography systems. Physics in Medicine and Biology, 61, 7652-7663(2016).
[17] W PU. SAE-net: a deep neural network for SAR autofocus. IEEE Transactions on Geoscience and Remote Sensing, 60, 5220714(2022).
[18] Y C WU, Y RIVENSON, Y B ZHANG et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704(2018).
[19] S CHAUDHARY, S MOON, H LU. Fast, efficient, and accurate neuro-imaging denoising via supervised deep learning. Nature Communications, 13, 5165(2022).
[20] J X ZHAO, L LIU, T H WANG et al. VDE-Net: a two-stage deep learning method for phase unwrapping. Optics Express, 30, 39794-39815(2022).
[21] G W SUN, B Y LI, Z LI et al. Phase unwrapping based on channel transformer U-Net for single-shot fringe projection profilometry. Journal of Optics, 1(2023).
[22] Y ZHOU, K YU, M WANG et al. Speckle noise reduction for OCT images based on image style transfer and conditional GAN. IEEE Journal of Biomedical and Health Informatics, 26, 139-150(2022).
[23] Z ZHOU, M M R SIDDIQUEE, N TAJBAKHSH et al. UNet++: a nested U-net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support, 11045, 3-11(2018).
[24] Y X WU, K M HE. Group normalization, 3-19(2018).
[25] B DONG, N X HUANG, Y L BAI et al. Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography. Optics Letters, 46, 5914-5917(2021).
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Zhanhua ZHANG, Xin CAO, Weihao ZHAN, Bo DONG, Shengli XIE, Yulei BAI. Super-resolution strain measurement in phase-contrast optical coherence elastography[J]. Optics and Precision Engineering, 2024, 32(12): 1812
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Received: Jan. 5, 2024
Accepted: --
Published Online: Aug. 28, 2024
The Author Email: BAI Yulei (ylbai@gdut.edu.cn)