With the rapid development of terahertz (THz) technology comes the need to further explore the prospects for various applications of THz systems. Due to the strong need, components and equipment involving the exploration are indispensable. In order to find the most suitable material for THz technology, we selected three common materials for different 3D printing techniques—polyamide (PA), polylactic acid (PLA), and light-curable resin. After mixing each material with a quartz powder of a different weight percentage, we observed the change in absorption coefficients and refractive indices of the mixtures by THz time-domain spectroscopy (THz-TDS). The higher the ratio of a quartz powder to a mixture was, the smaller the absorption coefficient of the mixture would be. The optimum rate of change in the absorption coefficient was attained when the weight percentage of a quartz powder in a mixture was 50 wt%. At 1 THz of the measurement of THz-TDS, the average reduction in the absorption coefficients of the three different materials mixed respectively with a 50 wt% quartz powder was 39.17%. Besides reduced absorption coefficients, the mixtures’ refractive indices also changed as the weight percentage of a quartz powder in the mixtures varied. The PLA-based sample mixed with a 50 wt% quartz powder had the highest increase in the refractive index. Mixing quartz powders with materials, therefore, is an effective method to increase refractive indices and decrease absorption coefficients. The method can be applied in 3D printing techniques in the future to enhance the efficiency of THz components manufactured with 3D printing techniques.
Today, radiologists observe a mammogram to determine whether breast tissue is normal. However, calcifications on the mammogram are so small that sometimes radiologists cannot locate them without a magnified observation to make a judgment. If clusters formed by malignant calcifications are found, the patient should undergo a needle localization surgical biopsy to determine whether the calcification cluster is benign or malignant. However, a needle localization surgical biopsy is an invasive examination. This invasive examination leaves scars, causes pain, and makes the patient feel uncomfortable and unwilling to receive an immediate biopsy, resulting in a delay in treatment time. The researcher cooperated with a medical radiologist to analyze calcification clusters and lesions, employing a mammogram using a multi-architecture deep learning algorithm to solve these problems. The features of the location of the cluster and its benign or malignant status are collected from the needle localization surgical biopsy images and medical order and are used as the target training data in this study. This study adopts the steps of a radiologist examination. First, VGG16 is used to locate calcification clusters on the mammogram, and then the Mask R-CNN model is used to find micro-calcifications in the cluster to remove background interference. Finally, an Inception V3 model is used to analyze whether the calcification cluster is benign or malignant. The prediction precision rates of VGG16, Mask R-CNN, and Inception V3 in this study are 93.63%, 99.76%, and 88.89%, respectively, proving that they can effectively assist radiologists and help patients avoid undergoing a needle localization surgical biopsy.
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