Optical coherence elastography (OCE) can quantify the tissue elasticity by measuring the velocities of elastic wave propagation in the tissue. Due to the high sensitivity and micron-level resolution, OCE is especially suitable for biomechanical property measurements of the ocular tissues. Usually, the external excited elastic wave is visualized by optical coherence tomography (OCT). However, the imaging depth of the OCE system is limited by the OCT system and the excitation depth of external force. In this study, we proposed a method extending the OCE imaging depth with an electrically tunable lens (ETL). The method was validated by detecting the propagation of elastic waves in the corneas and retinas of porcine eyes using an acoustic radiation force-based OCE system. Firstly, an acoustic simulation was taken for the ring ultrasound transducer. Secondly, a mathematical model of the ETL was established for dynamic control of the imaging depth. Thirdly, the optical simulation of the sample arm was performed to analyze the critical optical parameters and evaluating the imaging quality of the system. Also, the optimal working depth of the OCT system was discussed. Lastly, an OCE system with a ring ultrasound transducer and an ETL was built. The experimental results on ex vivo porcine eyes showed the imaging depth of the system was 22 mm. This method can extend the depth of elasticity detection and, thus, provides a powerful tool for non-invasive, high-resolution biomechanical analysis of the ocular tissues.
Optical coherence tomography angiography (OCTA) has been widely used for neuroimaging with non-invasive and high-resolution advantages. However, the signals from the skull and the noise from the deep imaging areas reduce the microvascular clarity in the OCTA projections. Here we proposed a U-Net deep learning method to segment the superficial cortical area from the skull and other tissues for improving the quality of the OCTA projections. The peak signal-to-noise ratio (pSNR) and the average contrast-to-noise ratio (aCNR) were analyzed to evaluate the OCTA projection images. The results showed that the pSNR and aCNR values increased significantly and, thus, the image quality of the microvascular projections was improved after the cortical segmentation.
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