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The primary optimization of the imaging speed of optical coherence tomography (OCT) has been keenly studied. In order to overcome the major speed limitation of spectral-domain OCT (SD-OCT), we developed an ultrahigh speed SD-OCT system, with an A-scan rate of up to 1 MHz, using the method of spacetime division multiplexing (STDM). Multi-cameras comprising a single spectrometer was implemented in the developed ultrahighspeed STDM method to eliminate the dead time of operation, whereas space-time division multiplexing was simultaneously employed to enable wide-range scanning measurements at a high speed. By successfully integrating the developed STDM method with GPU parallel processing, 8 vol/s for an image range of 250 × 250 × 2048 pixels (9 × 4.5 × 5 mm) was achieved, with an adjustable volume rate according to the required scanning speed and range. The examined STDM-OCT results of the customized optical thin film confirmed its feasibility for various fields that require rapid and wide-field scanning.

Photoacoustic imaging (PAI) is a hybrid non-invasive imaging technique used to merge high optical contrast and high acoustic resolution in deep tissue. PAI has been extensively developed by utilizing its advantages that include deep imaging depth, high resolution, and label-free imaging. As a representative implementation of PAI, photoacoustic microscopy (PAM) has been used in preclinical and clinical studies for its micron-scale spatial resolution capability with high optical absorption contrast. Several handheld and portable PAM systems have been developed that improve its applicability to several fields, making it versatile. In this study, we developed a laboratory-customized, two-axis, waterproof, galvanometer scanner-based handheld PAM (WP-GVS-HH-PAM), which provides an extended field of view (14.5 × 9 mm2) for wide-range imaging. The fully waterproof handheld probe enables free movement for imaging regardless of sample shape, and volume rate and scanning region are adjustable per experimental conditions. Results of WP-GVS-HH-PAM-based phantom and in vivo imaging of mouse tissues (ear, iris, and brain) confirm the feasibility and applicability of our system as an imaging modality for various biomedical applications.

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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, undersampling 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 undersampling 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 undersampled 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 undersampling 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.