Optical coherence tomography (OCT) is a non-invasive imaging method that provide high-resolution tomographic images. Attempts to incorporate OCT in dental practice have been ongoing, but the relatively bulky systems have limited their clinical utility. In this study, we utilized a microelectromechanical system (MEMS) to optimize the size of these OCT scanners to be similar to commercial intra-oral scanner (IOS) products. The optical axis of the internal scanner is designed in a Z shape to maximize the beam size reflected by the MEMS mirrors. To prove its usefulness in practical dentistry, we imaged the teeth in the oral cavity by position. Imaged teeth by position in the oral cavity demonstrated that the developed system can image deep into the oral cavity without difficulty. As a next step, we imaged teeth with cervical abrasion in three dimensions (3D) and high resolution. We classified the teeth into two types based on how the cervix was worn, and the degree of wear was quantitatively analyzed by performing A-scan profiling. This study demonstrates that the developed dental OCT system is effective in actual dental clinical practice and can be utilized for a variety of dental conditions.
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.
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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