• Progress in Medical Physics
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• v.24 no.1
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• pp.76-83
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• 2013

Previous studies about effect of respiratory motion on diagnostic imaging and radiation therapy have been performed by monitoring external motions but these can not reflect internal organ motion well. The aim of this study was to develope the artificial pulmonary nodule able to perform non-invasive implantation to dogs in the thorax and to evaluate applicability of the model to respiratory motion studies on PET image acquisition and radiation delivery by phantom studies. Artificial pulmonary nodule was developed on the basis of 8 Fr disposable gastric feeding tube. Four anesthetized dogs underwent implantation of the models via trachea and implanted locations of the models were confirmed by fluoroscopic images. Artificial pulmonary nodule models for PET injected $^{18}F$-FDG and mounted on the respiratory motion phantom. PET images of those acquired under static, 10-rpm- and 15-rpm-longitudinal round motion status. Artificial pulmonary nodule models for radiation delivery inserted glass dosemeter and mounted on the respiratory motion phantom. Radiation delivery was performed at 1 Gy under static, 10-rpm- and 15-rpm-longitudinal round motion status. Fluoroscpic images showed that all models implanted in the proximal caudal bronchiole and location of models changed as respiratory cycle. Artificial pulmonary nodule model showed motion artifact as respiratory motion on PET images. SNR of respiratory gated images was 7.21. which was decreased when compared with that of reference images 10.15. However, counts of respiratory images on profiles showed similar pattern with those of reference images when compared with those of static images, and it is assured that reconstruction of images using by respiratory gating improved image quality. Delivery dose to glass dosemeter inserted in the models were same under static and 10-rpm-longitudinal motion status with 0.91 Gy, but dose delivered under 15-rpm-longitudinal motion status was decreased with 0.90 Gy. Mild decrease of delivered radiation dose confirmed by electrometer. The model implanted in the proximal caudal bronchiole with high feasibility and reflected pulmonary internal motion on fluoroscopic images. Motion artifact could show on PET images and respiratory motion resulted in mild blurring during radiation delivery. So, the artificial pulmonary nodule model will be useful tools for study about evaluation of motion on diagnostic imaging and radiation therapy using laboratory animals.

• Journal of Korea Multimedia Society
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• v.23 no.3
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• pp.468-475
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• 2020

In this paper, we proposed a three-dimensional visualization system for medical images in augmented reality based on deep learning. In the proposed system, the artificial neural network model performed fully automatic segmentation of the region of lung and pulmonary nodule from chest CT images. After applying the three-dimensional volume rendering method to the segmented images, it was visualized in augmented reality devices. As a result of the experiment, when nodules were present in the region of lung, it could be easily distinguished with the naked eye. Also, the location and shape of the lesions were intuitively confirmed. The evaluation was accomplished by comparing automated segmentation results of the test dataset to the manual segmented image. Through the evaluation of the segmentation model, we obtained the region of lung DSC (Dice Similarity Coefficient) of 98.77%, precision of 98.45%, recall of 99.10%. And the region of pulmonary nodule DSC of 91.88%, precision of 93.05%, recall of 90.94%. If this proposed system will be applied in medical fields such as medical practice and medical education, it is expected that it can contribute to custom organ modeling, lesion analysis, and surgical education and training of patients.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.