KEYWORDS: Cornea, Transplantation, Deep learning, Optical coherence tomography, 3D modeling, Ultrasonography, Time metrology, Medical devices, Image segmentation, Eye
For several decades after corneal transplantation was performed for the first time, studies to predict the success of corneal transplantation have been conducted. To obtain a successful corneal transplantation, various factors other than biocompatibility between the donor cornea and the transplant recipient's eye must be satisfied. Therefore, various studies are being conducted to develop an artificial cornea that does not require a donor. One of the important indicators contributing to the success of corneal transplantation is measurement of corneal thickness (CT) after corneal transplantation. In previous studies, to measure the CT and transplanted cornea, partial CT measurement using an algorithm was mainly performed in optical coherence tomography (OCT) images. However, a single algorithm eventually has limitations in determining the suitability of the entire transplanted cornea. In this study, we automatically segmented the region of the artificial cornea implanted in the rabbit cornea through U-Net based models, and based on this, we measured and analyzed the three-dimensional total thickness of the conventional cornea and the artificial cornea. Our results suggest that the thickness of the transplanted and existing corneas can be automatically measured over time to provide information as an indicator for determining the success of corneal transplants.
Dental cervical abrasion is wear on the neck of the tooth where it meets the gums. To treat cervical abrasion, a therapeutic resin is used to fill the worn area. It is difficult to check whether the resin has structural defects such as internal voids. If the resin is not properly cured or has internal voids and bubbles, it can easily fall off when subjected to an external impact. OCT can be used to non-destructively inspect the internal structure after treatment. By acquiring cross-sectional images of the treated area, OCT can be used to check if bubbles have formed inside the resin. In addition, to check the volume of resin used in the treatment, an algorithm is used to extract the resin portion from each tomographic image.
One common limitation of spectral-domain optical coherence tomography (SD-OCT) is the mismatch between line-scan camera pixels and the wavelength of the source spectrum, causing image thickening in deeper regions and compromising imaging quality. Various studies have addressed this issue by attempting to improve the alignment between camera pixels and wavelength, with a focus on mitigating the nonlinearity of wavenumbers in SD-OCT systems. To enhance signal quality in deeper imaging regions, several wavenumber linearization (k-linearization) methods have been explored. In our research, we have introduced a novel k-linearization approach based on the diffraction grating equation. The specifications of the light source in our SD-OCT system were utilized for algorithm simulation. Our method concentrated on the difference in diffraction angles at the diffraction grating within the spectrometer to determine the incident wavenumber per pixel. By applying the acquired k-index to our system, we observed an improvement in intensity roll-off and a reduction in the thickening of images in the high-frequency region of the sample. One notable advantage of our proposed method is its effectiveness in obtaining a suitable k-index for systems with simple specifications. Additionally, it can be easily tailored to meet the specific requirements of different systems. This ensures that our approach is not only innovative but also adaptable to diverse SD-OCT setups.
KEYWORDS: 3D image reconstruction, Image restoration, Deep learning, 3D image processing, Data modeling, Photoacoustic microscopy, Biological imaging, 3D modeling, Spatial resolution, Polygon scanners
Photoacoustic microscopy (PAM) is a non-invasive, label-free functional imaging technique that provides high absorption contrast with high spatial resolution. Spatial sampling density and data size are key determinants of PAM imaging speed. Therefore, under sampling methods that reduce the number of scan points are usually employed to improve the imaging speed of PAM by increasing the scan step size. Because under sampling techniques sacrifice spatial sampling density, deep learning-based reconstruction techniques have been explored as alternatives. However, these methods have been applied to reconstruct two-dimensional PAM images related to spatial sampling density. Therefore, by considering the number of data points, the data size, and the characteristics of PAM to provide three-dimensional (3D) volume data, this study proposes a deep-learning-based complete reconstruction of under sampled 3D PAM data. newly reported to Obtained from real experiments (i.e. not manually generated). Quantitative analysis results show that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various under sampling ratios, resulting in 80x faster imaging speed and 800x smaller data. Improves PAM system performance with size. Furthermore, the applicability of this method is experimentally verified by enlarging a sparsely sampled test dataset. His proposed deep learning-based PAM data reconstruction has been demonstrated to be the closest model available under experimental conditions, significantly reducing the data size for processing and effectively reducing the imaging time.
Residual adhesive on tooth surface after bracket removal has to be identified at an advanced stage to avoid further effects on orthodontic and dental procedures, which has to be identified at an advanced stage. Since conventional visual inspection has a major limitation in identifying residuals, non-invasive identification with multi-dimensional assessments using swept-source optical coherence tomography (SS-OCT) is proposed for the precise identification of residual adhesive on the tooth surface during dental bracket replacements. The feasibility was examined using ex-vivo bovine teeth specimens after the removal of orthodontic implant from the tooth surface. Multi-dimensional assessments, such as residual adhesive boundary, color-scaled enface, adhesive area and thickness information were obtained using OCT to confirm the feasibility of the method. The detection algorithm finds the boundary which is between the dental surface and residual adhesive. The residual adhesive is separated based on the boundary. The area of residual adhesive is measured by an optical microscope and the detection algorithm. The difference between the optical microscope and detection algorithm measured area is lower than 10%. The results revealed that the performed OCT assessments can be beneficial for real-time application during orthodontic procedures as a primary inspection tool. Multi-dimensional assessment method used OCT and confirmed feasibility study shows that OCT can be used the detailed novel diagnosis system and effective tools for dental.
Doppler optical coherence tomography (DOCT) is a non-destructive imaging technique designed to measure the movement of a sample by applying the Doppler effect to optical coherence tomography (OCT) signal data. It was designed to acquire a tomography image of the tympanic membrane (TM) and a calculated Doppler signal in real time with OCT using the CUDA parallel processing algorithm while inducing vibration of the TM with an audio signal. Afterwards, the thickness of the TM inside the ROI was measured using software, and the degree of response was analyzed according to the thickness. To measure the tomographic thickness of the TM responding to sound waves, image processing was used to acquire the upper and lower boundaries of the TM. To reduce the error in thickness measurement according to the angle of the TM, the shortest distance between the upper and lower boundaries at each pixel was used to reduce the error in the thickness measurement. In addition, by mapping the thickness information to a two-dimensional array, the movement of the TM in response to sound waves was finally analyzed through a histogram according to the thickness of the TM. Finally, we were able to obtain the tendency of the response according to the thickness of the TM, and quantitatively analyze the change in the reactivity according to the area of the TM.
Photoacoustic microscopy (PAM) is a non-invasive, label-free functional imaging technique that provides high absorption contrast with high spatial resolution. Spatial sampling density and data size are important determinants of the imaging speed of PAM. Therefore, undersampling methods that reduce the number of scanning points are typically adopted to enhance the imaging speed of PAM by increasing the scanning step size. For the reason that undersampling methods sacrifice spatial sampling density, deep learning-based reconstruction methods have been considered as an alternative; however, these methods have been applied to reconstruct the two-dimensional PAM images, which is related to the spatial sampling density. Therefore, by considering the number of data points, data size, and the characteristics of PAM that provides three-dimensional (3D) volume data, in this study, we newly reported deep learning-based fully reconstructing the undersampled 3D PAM data, which is obtained at the actual experiment (i.e., not manually generated). The results of quantitative analyses demonstrate that the proposed method exhibits robustness and outperforms interpolation-based reconstruction methods at various undersampling ratios, enhancing the PAM system performance with 80-times faster-imaging speed and 800-times lower data size. Moreover, the applicability of this method is experimentally verified by upscaling the sparsely sampled test dataset. The proposed deep learning-based PAM data reconstructing is demonstrated to be the closest model that can be used under experimental conditions, effectively shortening the imaging time with significantly reduced data size for processing.
Optical coherence tomography (OCT) is a high-resolution and non-invasive internal structural imaging technique. Since the first introduction of OCT, it has been widely studied to enhance the scanning speed of the system to enhance the applicability. Spectral-domain OCT (SD-OCT) is one of the representative types of Fourier-domain OCT, which consisted with lower prices than swept-source OCT and offers higher axial resolution, but there are limited hardware performance to improve the scanning speed. In this paper, we introduced the space-time division multiplexing (STDM) method-based superfast SD-OCT with 1 MHz A-scan rate. In terms of the time-division method, dual-cameras were implemented in a single spectrometer to reduce the alignment error between each camera and fully utilize the operating time of camera by remove the dead time. In addition, the path length difference of the two-sample arm is accurately controlled to utilize the space-division method. By concurrently integrating the time- and space-division methods in STDM with GPU parallel computing, 32 volume/sec was acquired. The quantitative evaluation of the performance of STDM-OCT was analyzed with sensitivity roll-off and image quality comparison measured at different depth. The proposed STDM-OCT is able to enlarge the application of OCT including biomedical research areas, which require a high-speed scanning system.
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