Accurate measurements of the algal cell concentration are essential in microalgae culturing and ecological monitoring. Here, we propose a method based on optical coherence tomography (OCT) to assess green algae concentrations quantitatively and measure the depth distribution of green algae cells. The amplitude information of the complex OCT sequence was extracted to calculate the scattering coefficient. The results show that the scattering coefficient can be used to evaluate different concentrations of green algae suspensions by frequency statistics under non-contact conditions, and the scattering coefficient images can provide the depth distribution of green algae cells. This method provides an in situ, accurate and non-invasive tool for monitoring the growth of green algae and can effectively reflect the health status of water bodies.
Viscosity measurement is critical in the fields of biomedicine and industry. Here, we propose a method based on optical coherence tomography (OCT) to quantitatively assess the Doppler viscosity of the liquid in microfluidic devices. The velocity of the liquid in a silicone tube was obtained by Doppler optical coherence tomography (DOCT), by analysing the phase change between sequential B-Scan. Two manometers were used to measure the real-time pressure difference between the inlet and outlet of the silicone tube. Finally, the viscosity of the flow sample was calculated according to Poiseuille’s law. Different viscous liquids and blood samples were tested, and the results were consistent with data reported in the literature. Experimental results indicated that the proposed method can be a new tool for non-contact and fast liquid viscosity measurement and even be engineered in the future for daily monitoring of blood viscosity with only a small amount of blood.
Forces generated by cells are critical regulators of cell adhesion and function. Polymeric micropillar arrays have been widely used for measuring cellular traction forces, however, the sub-micron scale deflections of micropillars are typically measured with a high power microscope, leading to a limited field-of-view and reducing the measurement efficiency. Herein, we reports a typical 4F system used to monitor cell contraction force based on optical Fourier transform (OFT). Compared to conventional microscopy, with a field-of-view on the scale of millimeters and instant acquisition of the force map, this method makes high-throughput and real-time cell contraction monitoring possible. The cells growing on micropillar arrays were placed on the object plane of a typical 4F system, illuminated by a monochromatic plane wave. OFT was performed on the light field as it passed through the first Fourier lens. The spatial spectrum that consists of discrete light spots was projected on the back focal plane of the first Fourier lens and was then filtered by an optical stop, eliminating the extraneous frequency components while allowing the frequency spots corresponding to the period of the micropillar arrays to pass through the aperture. Inverse OFT was conducted as the filtered light passed through the second Fourier lens and an image was reconstructed on the image plane of the 4F system, which was recorded by a CCD camera with a 20X objective. The light intensity of the image directly represents the degree of periodicity of the micropillar arrays, in which low-intensity areas indicated that the micropillars in this area were deflected while high-intensity areas indicate the presence of uniform micropillar arrays. Using this method, cell contraction forces could be directly visualized based on the intensity distribution at the image plane with no need for further image post-processing, enabling direct visualization of cell contraction in a larger field-of-view.
Optical coherence tomography (OCT) enables non-invasive imaging of biological tissue and has become one of the most effective tools for monitoring the retinal structures and detecting retinal diseases. However, the existence of speckle noise severely degrades the OCT image quality and makes it difficult to identify the retinal disorders accurately. In this work, a deep generative model, named as despeckling generative adversarial network (DSGAN), is proposed for retinal OCT image despeckling. The proposed DSGAN is composed of two components, i.e., a despeckling generator and a discriminator. The despeckling generator employs the residual-in-residual dense block-based U-shape network to learn how to map the noisy image to the clean image. The discriminator learns to accurately discriminate whether the real clean images are relatively more realistic than the image generated by the generator. To improve the structure preservation ability during speckle noise reduction, the structural similarity index measure (SSIM) loss is introduced into the objective function of DSGAN to achieve more structural constraints. The proposed DSGAN was evaluated and analyzed on two public OCT datasets. The qualitative and quantitative comparison results show that the proposed DSGAN can achieve higher image quality, and is more effective in both speckle noise reduction and structural information preservation than previous despeckling methods.
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.
Speckle noise in optical coherence tomography (OCT) images seriously degrades the image quality and impairs the subsequent diagnosis of various ocular diseases. Most of the existing deep learning-based denoising models pay little attention to edge preservation, and rely on the large number of reference clean images which are hard to acquire in clinical OCT practice. In this work, an unsupervised retinal OCT image denoising model, named as edge-enhanced generative adversarial network (EEGAN), is proposed to free the dependence on reference clean images and enhance the edge information. Specifically, considering the noisy OCT image can be roughly divided into noisy retinal foreground and noise-only background regions, the generator of EEGAN is designed to denoise the noisy foreground samples based on the residual dense blocks, while the discriminator of EEGAN is employed to distinguish the real background noise samples from the fake noise samples, i.e., the difference images between the noisy foreground samples and its generated counterparts. As retinal edge details are the most vital information for disease diagnosis, an edge enhancement layer based on Sobel operators is integrated into the generator of EEGAN to strengthen the edge preservation ability of the model. Experimental results on clinical retinal OCT datasets show that our model has a better performance than the compared models in suppressing noise and preserving details, demonstrating the effectiveness of the proposed EEGAN.
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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