Blood vessel imaging is essential for diagnosing and treating ear-related diseases like tinnitus caused by altered blood flow. However, existing methods using cadaver samples have limitations due to vessel collapse or loss. This study utilizes optical coherence tomography angiography (OCTA), a non-invasive, label-free imaging of ear blood vessels. Customized imaging tips and a complex differential variance algorithm enhance OCTA image quality. Vascular distribution in 10 volunteers (5 male, 5 female) is successfully imaged. Internal factors, such as skin condition and body fat percentage, affect image quality by influencing light penetration, signal-to-noise ratio, and system robustness. Quantitative values for 20 parameters are obtained, enabling comparative analysis of blood vessel images. The findings demonstrate OCTA's potential in diagnosing and treating ear-related diseases and provide insights into blood vessel dynamics.
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.
The ischemic stroke animal model has gained increasing popularity to elucidate the pathophysiology and evaluate the efficacy of reperfusion and neuroprotective strategies for ischemic injuries. Various conventional methods to induce the ischemic models have been reported, however, it is difficult to control specific neurological deficits, mortality rates, and the extent of the infarction since the size of the affected region is precisely controlled, which limits the closeness of animal model to human stroke. In this study, we report a novel creation method of the target ischemic stroke model by simultaneous vessel monitoring and photothrombosis induction using localization photoacoustic microscopy (L-PAM), which minimizes infarct size at a precise location with high reproducibility. By utilizing the proposed L-PAM system, we resolve the occurred position error of the scanner for high-speed imaging caused by external resistance, which enables the precise localization up to a single micro-vasculature. The reproducibility and validity of the suggested target ischemic stroke model-inducing method have been successfully proven through repeated experiments and histological analyses. These results demonstrate that the proposed method is able to induce the closest ischemic stroke model to the clinical pathology for brain ischemia research from inducement dynamics, occurrence mechanisms to the recovery process.
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.
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.
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.
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