Super-resolution strain measurement in phase-contrast optical coherence elastography

ZHANG Zhanhua; CAO Xin; ZHAN Weihao; DONG Bo; XIE Shengli; BAI Yulei

doi:10.37188/OPE.20243212.1812

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Super-resolution strain measurement in phase-contrast optical coherence elastography

Modern Applied Optics | 更新时间：2024-07-26

- Super-resolution strain measurement in phase-contrast optical coherence elastography
- Optics and Precision Engineering Vol. 32, Issue 12, Pages: 1812-1823(2024)
- 作者机构：
  
  广东工业大学自动化学院，广东广州 510006
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20243212.1812
  CLC： TP394.1;TH691.9
- Published：25 June 2024，
  
  Received：05 January 2024，
  
  Revised：07 March 2024，
- 稿件说明：
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张展华,曹鑫,詹伟浩等.相衬光学相干弹性成像的超分辨应变测量[J].光学精密工程,2024,32(12):1812-1823.

ZHANG Zhanhua,CAO Xin,ZHAN Weihao,et al.Super-resolution strain measurement in phase-contrast optical coherence elastography[J].Optics and Precision Engineering,2024,32(12):1812-1823.
张展华,曹鑫,詹伟浩等.相衬光学相干弹性成像的超分辨应变测量[J].光学精密工程,2024,32(12):1812-1823. DOI： 10.37188/OPE.20243212.1812.

ZHANG Zhanhua,CAO Xin,ZHAN Weihao,et al.Super-resolution strain measurement in phase-contrast optical coherence elastography[J].Optics and Precision Engineering,2024,32(12):1812-1823. DOI： 10.37188/OPE.20243212.1812.

摘要

相衬光学相干弹性成像的相位分辨率受限于系统的光源带宽，导致层析应变成像质量低。这严重制约了相衬光学相干弹性成像的实际应用与发展。为此，本文提出一种基于数据驱动的超分辨应变测量方法，以解决相位分辨率受限下的应变重构困难问题。首先，根据相衬光学相干弹性成像原理，搭建了层析应变测量模型，用于获取网络训练所需的数据集，解决实际测量过程中超分辨应变标签难以获取的问题。其次，构建深度卷积神经网络，通过带有层析分辨率受限特点的数据驱动方式，让网络学习低分辨相位与高分辨应变之间的映射关系，从而实现相衬光学相干弹性成像的超分辨应变测量。最后，采用数值验证和压缩变形加载实验对本文方法的性能进行验证。实验结果表明本文方法在窄带宽输出下能重构出宽带宽的应变测量效果，并且其信噪比相比于矢量方法和传统应变计算深度神经网络，分别提高了18.4 dB和1.45 dB。本文方法可以突破系统光源的带宽限制，在低分辨相位输入条件下实现超分辨应变测量，从而加快相衬光学相干弹性成像在力学性能表征、内部早期损伤探测等方面的应用前景。

Abstract

The resolution of phase-contrast optical coherence elastography （PC-OCE） is constrained by the bandwidth of the system's light source， leading to poor quality in tomographic strain imaging. This limitation significantly hinders the practical implementation and advancement of PC-OCE. This study introduced a data-driven super-resolution strain measurement approach to tackle the challenge of strain reconstruction under restricted phase resolution. Firstly， according to the principle of PC-OCE， a simulation measurement model was built to obtain the required data set， which solved the problem that was is difficult to obtain the ground truth in the real measurement process. Secondly， a deep neural network was used to learn the mapping relationship between low-resolution phase and high-resolution strain through a data-driven manner， realizing the super-resolution measurement of strain. Finally， numerical validation and compression deformation loading experiments were employed to validate the efficacy of the method introduced in this study. The experimental results demonstrate that the approach presented in this study can reconstruct the strain measurement outcomes across a wide bandwidth despite operating under a narrow bandwidth output. Furthermore， the signal-to-noise ratio is enhanced by 18.4 dB and 1.45 dB in comparison to the vector method and conventional deep neural network for strain calculation. The proposed method overcomes the bandwidth limitation of the system light source， enabling super-resolution strain measurement under low-resolution phase input conditions. This advancement enhances the potential applications of phase-contrast optical coherence elastography in characterizing mechanical performance， detecting early internal damage， and other related areas.

关键词

光学弹性成像相衬技术应变测量深度神经网络超分辨重构

Keywords

optical coherence elastographyphase contrast technologystrain measurementdeep neural networksuper-resolution reconstruction

references

WANG J F， XU Y， BOPPART S A. Review of optical coherence tomography in oncology［J］. Journal of Biomedical Optics， 2017， 22（12）： 1-23. doi: 10.1117/1.jbo.22.12.121711http://dx.doi.org/10.1117/1.jbo.22.12.121711

YI J， RADOSEVICH A J， ROGERS J D， et al. Can OCT be sensitive to nanoscale structural alterations in biological tissue？［J］. Optics Express， 2013， 21（7）： 9043-9059. doi: 10.1364/oe.21.009043http://dx.doi.org/10.1364/oe.21.009043

ABDOLAHI F， ZHOU X， ASHIMATEY B S， et al. Optical coherence tomography angiography-derived flux as a measure of physiological changes in retinal capillary blood flow［J］. Translational Vision Science & Technology， 2021， 10（9）： 5. doi: 10.1167/tvst.10.9.5http://dx.doi.org/10.1167/tvst.10.9.5

ZAITSEV V Y， MATVEYEV A L， MATVEEV L A， et al. Strain and elasticity imaging in compression optical coherence elastography： the two-decade perspective and recent advances［J］. Journal of Biophotonics， 2021， 14（2）： e202000257. doi: 10.1002/jbio.202000257http://dx.doi.org/10.1002/jbio.202000257

WANG S， LARIN K V. Optical coherence elastography for tissue characterization： a review［J］. Journal of Biophotonics， 2015， 8（4）： 279-302. doi: 10.1002/jbio.201400108http://dx.doi.org/10.1002/jbio.201400108

KENNEDY K M， CHIN L， MCLAUGHLIN R A， et al. Quantitative micro-elastography： imaging of tissue elasticity using compression optical coherence elastography［J］. Scientific Reports， 2015， 5： 15538. doi: 10.1038/srep15538http://dx.doi.org/10.1038/srep15538

GUBARKOVA E V， SOVETSKY A A， MATVEEV L A， et al. Nonlinear elasticity assessment with optical coherence elastography for high-selectivity differentiation of breast cancer tissues［J］. Materials， 2022， 15（9）： 3308. doi: 10.3390/ma15093308http://dx.doi.org/10.3390/ma15093308

HUANG P B， LIN Y K， YOU R F， et al. Simultaneously measurement of strain field and Poisson’s ratio by using an off-axis phase-sensitive optical coherence elastography［J］. Measurement Science and Technology， 2022， 33（9）： 095406. doi: 10.1088/1361-6501/ac77dahttp://dx.doi.org/10.1088/1361-6501/ac77da

ALEXANDROVSKAYA Y M， KASIANENKO E M， SOVETSKY A A， et al. Spatio-temporal dynamics of diffusion-associated deformations of biological tissues and polyacrylamide gels observed with optical coherence elastography［J］. Materials， 2023， 16（5）： 2036. doi: 10.3390/ma16052036http://dx.doi.org/10.3390/ma16052036

GORNIG D C， MALETZ R， OTTL P， et al. Influence of artificial aging： mechanical and physicochemical properties of dental composites under static and dynamic compression［J］. Clinical Oral Investigations， 2022， 26（2）： 1491-1504. doi: 10.1007/s00784-021-04122-0http://dx.doi.org/10.1007/s00784-021-04122-0

LARIN K V， SAMPSON D D. Optical coherence elastography - OCT at work in tissue biomechanics［J］. Biomedical Optics Express， 2017， 8（2）： 1172-1202. doi: 10.1364/boe.8.001172http://dx.doi.org/10.1364/boe.8.001172

ZAITSEV V Y， MATVEYEV A L， MATVEEV L A， et al. Deformation-induced speckle-pattern evolution and feasibility of correlational speckle tracking in optical coherence elastography［J］. Journal of Biomedical Optics， 2015， 20（7）： 75006. doi: 10.1117/1.jbo.20.7.075006http://dx.doi.org/10.1117/1.jbo.20.7.075006

MOTAGHIANNEZAM S M， KOOS D， FRASER S E. Differential phase-contrast， swept-source optical coherence tomography at 1060 nm for in vivo human retinal and choroidal vasculature visualization［J］. Journal of Biomedical Optics， 2012， 17（2）： 026011. doi: 10.1117/1.jbo.17.2.026011http://dx.doi.org/10.1117/1.jbo.17.2.026011

DREXLER W. Ultrahigh-resolution optical coherence tomography［J］. Journal of Biomedical Optics， 2004， 9（1）： 47-74. doi: 10.1117/1.1629679http://dx.doi.org/10.1117/1.1629679

DE WIT J， ANGELOPOULOS K， KALKMAN J， et al. Fast and accurate spectral-estimation axial super-resolution optical coherence tomography［J］. Optics Express， 2021， 29（24）： 39946-39966. doi: 10.1364/oe.439761http://dx.doi.org/10.1364/oe.439761

RATHEESH K M， SEAH L K， MURUKESHAN V M. Spectral phase-based automatic calibration scheme for swept source-based optical coherence tomography systems［J］. Physics in Medicine and Biology， 2016， 61（21）： 7652-7663. doi: 10.1088/0031-9155/61/21/7652http://dx.doi.org/10.1088/0031-9155/61/21/7652

PU W. SAE-net： a deep neural network for SAR autofocus［J］. IEEE Transactions on Geoscience and Remote Sensing， 2022， 60： 5220714. doi: 10.1109/tgrs.2021.3139914http://dx.doi.org/10.1109/tgrs.2021.3139914

WU Y C， RIVENSON Y， ZHANG Y B， et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery［J］. Optica， 2018， 5（6）： 704. doi: 10.1364/optica.5.000704http://dx.doi.org/10.1364/optica.5.000704

CHAUDHARY S， MOON S， LU H. Fast， efficient， and accurate neuro-imaging denoising via supervised deep learning［J］. Nature Communications， 2022， 13： 5165. doi: 10.1038/s41467-022-32886-whttp://dx.doi.org/10.1038/s41467-022-32886-w

ZHAO J X， LIU L， WANG T H， et al. VDE-Net： a two-stage deep learning method for phase unwrapping［J］. Optics Express， 2022， 30（22）： 39794-39815. doi: 10.1364/oe.469312http://dx.doi.org/10.1364/oe.469312

SUN G W， LI B Y， LI Z， et al. Phase unwrapping based on channel transformer U-Net for single-shot fringe projection profilometry［J］. Journal of Optics， 2023： 1. doi: 10.1007/s12596-023-01515-0http://dx.doi.org/10.1007/s12596-023-01515-0

ZHOU Y， YU K， WANG M， et al. Speckle noise reduction for OCT images based on image style transfer and conditional GAN［J］. IEEE Journal of Biomedical and Health Informatics， 2022， 26（1）： 139-150. doi: 10.1109/jbhi.2021.3074852http://dx.doi.org/10.1109/jbhi.2021.3074852

ZHOU Z， SIDDIQUEE M M R， TAJBAKHSH N， et al. UNet++： a nested U-net architecture for medical image segmentation［J］. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support （2018）， 2018， 11045： 3-11. doi: 10.1007/978-3-030-00889-5_1http://dx.doi.org/10.1007/978-3-030-00889-5_1

WU Y X， HE K M. Group normalization［C］. European Conference on Computer Vision. Cham： Springer， 2018： 3-19. doi: 10.1007/978-3-030-01261-8_1http://dx.doi.org/10.1007/978-3-030-01261-8_1

DONG B， HUANG N X， BAI Y L， et al. Deep-learning-based approach for strain estimation in phase-sensitive optical coherence elastography［J］. Optics Letters， 2021， 46（23）： 5914-5917. doi: 10.1364/ol.446403http://dx.doi.org/10.1364/ol.446403

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