• Journal of the Korean Society of Radiology
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• v.14 no.2
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• pp.129-138
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• 2020

Due to the nature of the Rosenberg Method, the patient needs to maintain posture for a certain period of time, and the joint space is observed in various forms depending on the angle of knee flexion, which causes difficulties in examination. In order to solve these problems, Image quality was evaluated in order to evaluate the usefulness of the assistive device by making the assistive device itself. SNR and CNR analysis about the presence or absence of an assistive device using the extremity phantom and the angle of the assistive device itself were not statistically significant(p < 0.05). As a result of measuring the distance between the right and left edges of the medial condyle based on the presence or absence of an assist device, and the absence of assist device (96.00±40.6 mm) and presence of an assist device (134.86±17.68 mm) were statistically significant (p <0.05). To find the aLDFA relationship about the femur and tibia, we measured the right and left aLDFA based on the presence or absence of assist device. As a result, the absence of the right-side aLDFA assist device (74.63°±4.87) and the presence of assist device (79.64°±3.65) were statistically significant (p <0.05). The absence of the left-side aLDFA assist device (76.39°±4.62) and the presence of assist device (79.64°±3.65) were statistically significant (p < 0.05). but, As a result of measuring the distance of the overlapping parts of the right and left proximal tibiofibular joint and the lateral condyle, There were no statistically significant differences between the right and left sides. In conclusion, we confirmed that we can obtain Diagnostically valuable images with a constant knee-to-knee spacing using an assist device we self-created. In addition, we could learn through aLDFA relationship between femur and tibial that the smaller the angle, the more medial condyle overlaps with JSW, We also confirmed the significance by deriving similar values on the normal range values of aLDFA using assist devices. However, it is considered necessary to pay attention to internal and external rotations in order to obtain good quality images by evaluating the distance of overlapping parts between proximal tibiofibular joint and lateral condyle.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.127-133
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• 2014

Purpose: Low dose of PET/CT is important because of Patient's X-ray exposure. The aim of this study was to evaluate the effectiveness of low-dose PET/ CT image through the CTAC and QAC of patient study and phantom study. Materials and Methods: We used the discovery 710 PET/CT (GE). We used the NEMA IEC body phantom for evaluating the PET data corrected by ultra-low dose CT attenuation correction method and NU2-94 phantom for uniformity. After injection of 70.78 MBq and 22.2 MBq of 18 F-FDG were done to each of phantom, PET/CT scans were obtained. PET data were reconstructed by using of CTAC of which dose was for the diagnosis CT and Q. AC of which was only for attenuation correction. Quantitative analysis was performed by use of horizontal profile and vertical profile. Reference data which were corrected by CTAC were compared to PET data which was corrected by the ultra-low dose. The relative error was assessed. Patients with over weighted and normal weight also underwent a PET/CT scans according to low dose protocol and standard dose protocol. Relative error and signal to noise ratio of SUV were analyzed. Results: In the results of phantom test, phantom PET data were corrected by CTAC and Q.AC and they were compared each other. The relative error of Q.AC profile was been calculated, and it was shown in graph. In patient studies, PET data for overweight patient and normal weight patient were reconstructed by CTAC and Q.AC under routine dose and ultra-low dose. When routine dose was used, the relative error was small. When high dose was used, the result of overweight patient was effectively corrected by Q.AC. Conclusion: In phantom study, CTAC method with 80 kVp and 10 mA was resulted in bead hardening artifact. PET data corrected by ultra- low dose CTAC was not quantified, but those by the same dose were quantified properly. In patients' cases, PET data of over weighted patient could be quantified by Q.AC method. Its relative difference was not significant. Q.AC method was proper attenuation correction method when ultra-low dose was used. As a result, it is expected that Q.AC is a good method in order to reduce patient's exposure dose.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.195-211
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• 2010

An exhibition is defined as market events for specific duration to present exhibitors' main product range to either business or private visitors, and it also plays a key role as effective marketing channels. Especially, as the effect of the opinions of the visitors after the exhibition impacts directly on sales or the image of companies, exhibition organizers must consider various needs of visitors. To meet needs of visitors, ubiquitous technologies have been applied in some exhibitions. However, despite of the development of the ubiquitous technologies, their services cannot always reflect visitors' preferences as they only generate information when visitors request. As a result, they have reached their limit to meet needs of visitors, which consequently might lead them to loss of marketing opportunity. Recommendation systems can be the right type to overcome these limitations. They can recommend the booths to coincide with visitors' preferences, so that they help visitors who are in difficulty for choices in exhibition environment. One of the most successful and widely used technologies for building recommender systems is called Collaborative Filtering. Traditional recommender systems, however, only use neighbors' evaluations or behaviors for a personalized prediction. Therefore, they can not reflect visitors' dynamic preference, and also lack of accuracy in exhibition environment. Although there is much useful information to infer visitors' preference in ubiquitous environment (e.g., visitors' current location, booth visit path, and so on), they use only limited information for recommendation. In this study, we propose a booth recommendation methodology using Sequential Association Rule which considers the sequence of visiting. Recent studies of Sequential Association Rule use the constraints to improve the performance. However, since traditional Sequential Association Rule considers the whole rules to recommendation, they have a scalability problem when they are adapted to a large exhibition scale. To solve this problem, our methodology composes the confidence database before recommendation process. To compose the confidence database, we first search preceding rules which have the frequency above threshold. Next, we compute the confidences of each preceding rules to each booth which is not contained in preceding rules. Therefore, the confidence database has two kinds of information which are preceding rules and their confidence to each booth. In recommendation process, we just generate preceding rules of the target visitors based on the records of the visits, and recommend booths according to the confidence database. Throughout these steps, we expect reduction of time spent on recommendation process. To evaluate proposed methodology, we use real booth visit records which are collected by RFID technology in IT exhibition. Booth visit records also contain the visit sequence of each visitor. We compare the performance of proposed methodology with traditional Collaborative Filtering system. As a result, our proposed methodology generally shows higher performance than traditional Collaborative Filtering. We can also see some features of it in experimental results. First, it shows the highest performance at one booth recommendation. It detects preceding rules with some portions of visitors. Therefore, if there is a visitor who moved with very a different pattern compared to the whole visitors, it cannot give a correct recommendation for him/her even though we increase the number of recommendation. Trained by the whole visitors, it cannot correctly give recommendation to visitors who have a unique path. Second, the performance of general recommendation systems increase as time expands. However, our methodology shows higher performance with limited information like one or two time periods. Therefore, not only can it recommend even if there is not much information of the target visitors' booth visit records, but also it uses only small amount of information in recommendation process. We expect that it can give real?time recommendations in exhibition environment. Overall, our methodology shows higher performance ability than traditional Collaborative Filtering systems, we expect it could be applied in booth recommendation system to satisfy visitors in exhibition environment.

• The Journal of the Korea Contents Association
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• v.9 no.12
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• pp.742-749
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• Proceedings of the Korea Contents Association Conference
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• 2009.05a
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• pp.1159-1166
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• 2009

The use of cone-beam computed tomography(CBCT) has been proposed for guiding the delivery of radiation therapy. A kilovoltage imaging system capable of radiography, fluoroscopy, and cone-beam computed tomography(CT) has been integrated with a medical linear accelerator. A standard clinical linear accelerator, operating in arc therapy mode, and an amorphous-silicon (a-Si) with an on-board electronic portal imager can be used to treat palliative patient and verify the patient's position prior to treatment. On-board CBCT images are used to generate patient geometric models to assist patient setup. The image data can also, potentially, be used for dose reconstruction in combination with the fluence maps from treatment plan. In this study, the accuracy of Hounsfield Units of CBCT images as well as the accuracy of dose calculations based on CBCT images of a phantom and compared the results with those of using CT simulator images. Phantom and patient studies were carried out to evaluate the achievable accuracy in using CBCT and CT stimulator for dose calculation. Relative electron density as a function of HU was obtained for both planning CT stimulator and CBCT using a Catphan-600 (The Phantom Laboratory, USA) calibration phantom. A clinical treatment planning system was employed for CT stimulator and CBCT based dose calculations and subsequent comparisons. The dosimetric consequence as the result of HU variation in CBCT was evaluated by comparing MU/cCy. The differences were about 2.7% (3-4MU/100cGy) in phantom and 2.5% (1-3MU/100cGy) in patients. The difference in HU values in Catphan was small. However, the magnitude of scatter and artifacts in CBCT images are affected by limitation of detector's FOV and patient's involuntary motions. CBCT images included scatters and artifacts due to In addition to guide the patient setup process, CBCT data acquired prior to the treatment be used to recalculate or verify the treatment plan based on the patient anatomy of the treatment area. And the CBCT has potential to become a very useful tool for on-line ART.)

• KIPS Transactions on Software and Data Engineering
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• v.10 no.7
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• pp.271-278
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• 2021

As the current cyber attacks become more intelligent, the existing Intrusion Detection System is difficult for detecting intelligent attacks that deviate from the existing stored patterns. In an attempt to solve this, a model of a deep learning-based intrusion detection system that analyzes the pattern of intelligent attacks through data learning has emerged. Intrusion detection systems are divided into host-based and network-based depending on the installation location. Unlike network-based intrusion detection systems, host-based intrusion detection systems have the disadvantage of having to observe the inside and outside of the system as a whole. However, it has the advantage of being able to detect intrusions that cannot be detected by a network-based intrusion detection system. Therefore, in this study, we conducted a study on a host-based intrusion detection system. In order to evaluate and improve the performance of the host-based intrusion detection system model, we used the host-based Leipzig Intrusion Detection-Data Set (LID-DS) published in 2018. In the performance evaluation of the model using that data set, in order to confirm the similarity of each data and reconstructed to identify whether it is normal data or abnormal data, 1D vector data is converted to 3D image data. Also, the deep learning model has the drawback of having to re-learn every time a new cyber attack method is seen. In other words, it is not efficient because it takes a long time to learn a large amount of data. To solve this problem, this paper proposes the Siamese Convolutional Neural Network (Siamese-CNN) to use the Few-Shot Learning method that shows excellent performance by learning the little amount of data. Siamese-CNN determines whether the attacks are of the same type by the similarity score of each sample of cyber attacks converted into images. The accuracy was calculated using Few-Shot Learning technique, and the performance of Vanilla Convolutional Neural Network (Vanilla-CNN) and Siamese-CNN was compared to confirm the performance of Siamese-CNN. As a result of measuring Accuracy, Precision, Recall and F1-Score index, it was confirmed that the recall of the Siamese-CNN model proposed in this study was increased by about 6% from the Vanilla-CNN model.

• Journal of the Korean Society of Radiology
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• v.84 no.2
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• pp.441-453
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• 2023

Purpose To evaluate the development, location, and volume of a VX2 carcinoma using four inoculation methods in a rabbit brain. Materials and Methods Inoculation of a VX2 cell suspension was performed 1) on the appointed day, 2) seven days after storing a VX2 carcinoma in a freezer or 3) seven days after storing a VX2 carcinoma in a deep freezer after sacrificing the donor rabbits. 4) Without sacrificing the rabbits, the VX2 cell suspension was obtained using a gun biopsy, inoculation was performed on the appointed day. MR imaging was performed 10 days after inoculation. Brain tissues were obtained the day after. The development, location, and volume of the tumor were evaluated. Results Seventeen of the 18 rabbits inoculated on the appointed day developed tumors (average tumor volume, 106.32 mm³). One of five inoculated seven days after storing the VX2 tumor in the freezer, and three of five inoculated seven days after storing the VX2 tumor in the deep freezer developed tumors. Inoculation with a VX2 cell suspension obtained with a gun biopsy from five rabbits revealed development of tumors in only two rabbits. The tumors mostly developed in the superficial cortex. Conclusion TVX2 rabbit brain tumor model is easy to develop and revealed variable reproducibility. This model can be applicable in radiologic imaging, treatment planning, interventional treatment and drug delivery research. VX2 cell can be successfully innoculated into the brain using variable methods under researcher's variable conditions.

• The Journal of Korean Society for Radiation Therapy
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• v.25 no.1
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• pp.77-85
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• 2013

Purpose: The accurate movement of gantry rotation, collimator and correct application of dose rate are very important to approach the successful performance of Volumetric Modulated Arc Therapy (VMAT), because it is tightly interlocked with a complex treatment plan. The interruption and restart of dose delivery, however, are able to occur on treatment by various factors of a treatment machine and treatment plan. If unexpected problems of a treat machine or a patient interrupt the VMAT, the movement of treatment machine for delivering the remaining dose will be restarted at the start point. In this investigation, We would like to know the effect of interruptions and restart regarding dose delivery at VMAT. Materials and Methods: Treatment plans of 10 patients who had been treated at our center were used to measure and compare the dose distribution of each VMAT after converting to a form of digital image and communications in Medicine (DICOM) with treatment planning system (Eclipse V 10.0, Varian, USA). We selected the 6 MV photon energy of Trilogy (Varian, USA) and used OmniPro I'mRT system (V 1.7b, IBA dosimetry, Germany) to analyze the data that were acquired through this measurement with two types of interruptions four times for each case. The door interlock and the beam-off were used to stop and then to restart the dose delivery of VMAT. The gamma index in OmniPro I'mRT system and T-test in Microsoft Excel 2007 were used to evaluate the result of this investigation. Results: The deviations of average gamma index in cases with door interlock, beam-off and without interruption on VMAT are 0.141, 0.128 and 0.1. The standard deviations of acquired gamma values are 0.099, 0.091, 0.071 and The maximum gamma value in each case is 0.413, 0.379, 0.286, respectively. This analysis has a 95-percent confidence level and the P-value of T-test is under 0.05. Gamma pass rate (3%, 3 mm) is acceptable in all of measurements. Conclusion: As a result, We could make sure that the interruption of this investgation are not enough to seriously affect dose delivery of VMAT by analyzing the measured data. But this investigation did not reflect all cases about interruptions and errors regarding the movement of a gantry rotation, collimator and patient So, We should continuously maintain a treatment machine and program to deliver the accurate dose when we perform the VMAT for the many kinds of cancer patients.

• Radiation Oncology Journal
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• v.26 no.4
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• pp.263-270
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• 2008

Purpose: This study aimed to quantitatively measure the movement of tumors in real-time and evaluate the treatment accuracy, during the treatment of a liver tumor patient, who underwent radiosurgery with a Synchrony Respiratory motion tracking system of a robot CyberKnife. Materials and Methods: The study subjects included 24 liver tumor patients who underwent CyberKnife treatment, which included 64 times of treatment with the Synchrony Respiratory motion tracking system ($Synchrony^{TM}$). The treatment involved inserting 4 to 6 acupuncture needles into the vicinity of the liver tumor in all the patients using ultrasonography as a guide. A treatment plan was set up using the CT images for treatment planning uses. The position of the acupuncture needle was identified for every treatment time by Digitally Reconstructed Radiography (DRR) prepared at the time of treatment planning and X-ray images photographed in real-time. Subsequent results were stored through a Motion Tracking System (MTS) using the Mtsmain.log treatment file. In this way, movement of the tumor was measured. Besides, the accuracy of radiosurgery using CyberKnife was evaluated by the correlation errors between the real-time positions of the acupuncture needles and the predicted coordinates. Results: The maximum and the average translational movement of the liver tumor were measured 23.5 mm and $13.9{\pm}5.5\;mm$, respectively from the superior to the inferior direction, 3.9 mm and $1.9{\pm}0.9mm$, respectively from left to right, and 8.3 mm and $4.9{\pm}1.9\;mm$, respectively from the anterior to the posterior direction. The maximum and the average rotational movement of the liver tumor were measured to be $3.3^{\circ}$ and $2.6{\pm}1.3^{\circ}$, respectively for X (Left-Right) axis rotation, $4.8^{\circ}$ and $2.3{\pm}1.0^{\circ}$, respectively for Y (Crania-Caudal) axis rotation, $3.9^{\circ}$ and $2.8{\pm}1.1^{\circ}$, respectively for Z (Anterior-Posterior) axis rotation. In addition, the average correlation error, which represents the treatment's accuracy was $1.1{\pm}0.7\;mm$. Conclusion: In this study real-time movement of a liver tumor during the radiosurgery could be verified quantitatively and the accuracy of the radiosurgery with the Synchrony Respiratory motion tracking system of robot could be evaluated. On this basis, the decision of treatment volume in radiosurgery or conventional radiotherapy and useful information on the movement of liver tumor are supposed to be provided.