Super-resolution strain measurement in phase-contrast optical coherence elastography
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 lim-itation significantly hinders the practical implementation and advancement of PC-OCE. This study intro-duced a data-driven super-resolution strain measurement approach to tackle the challenge of strain recon-struction 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-driv-en manner,realizing the super-resolution measurement of strain. Finally,numerical validation and com-pression 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 recon-struct the strain measurement outcomes across a wide bandwidth despite operating under a narrow band-width 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 measure-ment under low-resolution phase input conditions. This advancement enhances the potential applications of phase-contrast optical coherence elastography in characterizing mechanical performance,detecting early in-ternal damage,and other related areas.
万方数据
维普
