Session Index

Glycated hemoglobin (HbA1c) is a pivotal metric for diabetes diagnosis. Conventional measurements tend to be invasive and time-consuming. Here, we report a method utilizing ultraviolet-visible spectrum measurement to determine HbA1c levels. Comprehensive spectral feature analyses, wavelength selection, validations based on idealized samples, and tests employing real blood samples were conducted. We also incorporated a novel correction technique, substantially bolstering the predictive accuracy of our real blood model, with the R-squared value rising from 0.55 to 0.86. This study enriches the theoretical exploration of optical detection for HbA1c, presenting a more exhaustive argument and promising extensive applications.

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Optical coherence tomography (OCT) has been widely used in many clinical apartments. The trade-off between field-of-view (FOV) and transverse resolution has always become critical. High-resolution (HR) image reconstruction of OCT can increase the resolution without reducing the FOV. But, it is hard to find one-to-one paired HR for reference. The HR methods are used to improve the cross-sectional OCT image only. Therefore, we build a wide FOV, multi-scale OCT that can produce excellent high- and low-resolution one-to-one mapping volumetric data set. With the HR methods, we can reduce the acquisition of synthetic high-resolution volumetric images by up to four times.

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Utilizing time-stretching of carrier pulses in nonlinear light microscopy, we've developed a new method to boost signal strength without degrading image quality and signal to noise ratio. In the reports, 80% signal enhancement in second-harmonic generation collagen fiber imaging was achieved with an improved signal to noise ratio. This also increased the image depth from 200 to 220 microns. Offering reduced photo-damage risk and faster imaging, our method is compatible with other enhancement approaches, making it promising for both research and industrial applications with nonlinear laser scanning microscopes.



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Esophageal cancer is a leading cause of death, especially in East Asia. Taiwan's 2022 mortality statistics revealed it accounted for 25% of all deaths. This study collaborated with Chung-Ho Memorial Hospital, using 2067 early-stage esophageal cancer images. Hyperspectral technology and YOLOv8 models were employed, achieving improved detection. The RGB-WLI model scored 0.85 mAP, 0.86 Sensitivity, 0.88 F1-score, and 0.85 Overall Accuracy. The HSI-NBI model scored 0.87 mAP, 0.88 Sensitivity, 0.89 F1-score, and 0.88 Overall Accuracy. Hyperspectral imaging enhances esophageal cancer detection capabilities.

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Fluorescence lifetime imaging microscopy (FLIM) technology is a non-destructive detection method known for its numerous advantages, such as providing quantitative measurements and high sensitivity. In recent years, scientists have developed various advanced algorithms for analyzing fluorescence lifetime data, driven by the need to handle larger datasets and reduce analysis times. These algorithms include phasor analysis (PA), maximum likelihood estimate (MLE), and others. This study's primary objective is to comprehensively explain the computational methods employed in phasor analysis and the traditional curve fitting method. Furthermore, it seeks to highlight the differences and distinctions between these two algorithms.

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This study explores and implements the use of L-curve and U-curve method to determine the regularization parameter to reconstruct diffuse optical images and use some simulated measurement data to observe the image reconstruction. Some synthesized case examples and experimental ones as well are conducted to verify the performance. uses some simulated measurement data to observe the image reconstruction.

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Revealing the 3D structure neural network play an important role in the studies of neuroscience. However, image contrast and image quality in deep brain degrade due to the scattering of both excitation beam and the emission light. Here, we report the development of a high-resolution 3D imaging platform for intact whole mouse brain. An ultrahigh 0.475μm lateral resolution even at the bottom of a whole intact mouse brain was achieved, corresponding to a 7.3 mm penetration depth in brain tissues.

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Analysis of wood is generally based on observing its external characteristics to determine the type and quality of the wood. However, it cannot accurately identify the chemical composition and structural characteristics, thus failing to meet the requirements of modern industry for wood quality control. This study utilizes two-photon hyperspectral microscopy as a tool to analyze the two-photon fluorescence spectra of three types of softwoods and nine types of hardwoods. It combines techniques such as linear unmixing, principal component analysis (PCA), and KNN to establish models and explore the possibility of differentiating between softwood and hardwood using two-photon fluorescence spectra.

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