• Journal of Life Science
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• v.20 no.11
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• pp.1725-1728
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• 2010

Miniature pigs are widely used in experiments related to pulmonary disease because of their similarities with humans. However, there are not enough data about normal lung function in miniature pigs. Thus, in this study, we investigated normal lung function in miniature pigs with lung ventilation/perfusion scintigraphy and evaluated the availability of this method. Three male miniature pigs weighing 30-35 kg were used. After general anesthesia, ventilation scintigraphy was performed with 100 MBq of $^{99m}Tc$-pertechnetate (${O_4}^-$), after which perfusion scintigraphy was performed with intravenous injection of $^{99m}Tc$-macro aggregated albumin (MAA). The functional contribution of the right lung was about 55%, and left lung was about 45%, similar to humans. Lung ventilation/perfusion scintigraphy was very useful in evaluating the normal lung function of miniature pigs because it was a non-invasive procedure (no tissue damage was involved), took a short time and was easy to perform. In conclusion, miniature pigs are similar to humans in functional contributions of the lung, and this method will be helpful in future pulmonary disease studies involving miniature pigs.

• Journal of radiological science and technology
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• v.45 no.1
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• pp.41-47
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• 2022

The purpose of this study is to apply a deep learning model that can distinguish lung perfusion and lung ventilation images in nuclear medicine, and to evaluate the image classification ability. Image data pre-processing was performed in the following order: image matrix size adjustment, min-max normalization, image center position adjustment, train/validation/test data set classification, and data augmentation. The convolutional neural network(CNN) structures of VGG-16, ResNet-18, Inception-ResNet-v2, and SE-ResNeXt-101 were used. For classification model evaluation, performance evaluation index of classification model, class activation map(CAM), and statistical image evaluation method were applied. As for the performance evaluation index of the classification model, SE-ResNeXt-101 and Inception-ResNet-v2 showed the highest performance with the same results. As a result of CAM, cardiac and right lung regions were highly activated in lung perfusion, and upper lung and neck regions were highly activated in lung ventilation. Statistical image evaluation showed a meaningful difference between SE-ResNeXt-101 and Inception-ResNet-v2. As a result of the study, the applicability of the CNN model for lung scintigraphy classification was confirmed. In the future, it is expected that it will be used as basic data for research on new artificial intelligence models and will help stable image management in clinical practice.