• Progress in Medical Physics
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• v.9 no.4
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• pp.207-215
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• 1998

There have been many radiation measurement methods so far among which film dosimetry, TLD, and ion chamber are the most frequently used methods. But this study describes a new radiation measurement method which uses polymer gel and magnetic resonance imaging(MRI). The objective of this study is to fabricate a polymer gel sensitive to radiation and to generate a dose to MRI contrast relationship, and to apply this results to the radiation measurement for the brachytherapy. To do this, 12 cm diameter cylindrical gel phantom was made, and the phantom was irradiated using the 30 mm diameter circular collimator which was used for radiosurgery. And this irradiated phantom was scanned with MRI. To find out the relationship between the radiation dose and the transversal relaxation time, an image processing software(IDL) was used. From this study it is found out that the radiation dose showed linear relationship to the transversal relaxation time of the gel up to 17 Gy($R^2$=0.993) and they had a different relationship above 17 Gy. The dose distributions were calculated using these results for the Ir-192 sources, one for the HDR afterloading system and the other for a 2 mCi seed source. And these calculated dose distributions were compared to the ones from the treatment planning computers. From this study the dose to the irradiated gel's transversal relaxation time relationship was examined, and this result was tried for the measurement of the brachytherapy.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Progress in Medical Physics
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• v.23 no.3
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• pp.177-187
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• 2012

Magnetic resonance angiography (MRA) techniques are widely used in diagnosis of vascular disorders such as hemadostenosis and aneurism. Especially, phase contrast (PC) MRA technique, which is a typical non contrast-enhanced MRA technique, provides not only the anatomy of blood vessels but also flow velocity. In this study, we developed the 2- and 3-dimensional PC MRA pulse sequences for a low magnetic field MRI system. Vessel images were acquired using 2D and 3D PC MRA and the velocities of the blood flow were measured in the superior sagittal sinus, straight sinus and the confluence of the two. The 2D PC MRA provided the good quality of vascular images for large vessels but the poor quality for small ones. Although 3D PC MRA gave more improved visualization of small vessels than 2D PC MRA, the image quality was not enough to be used for diagnosis of the small vessels due to the low SNR and field homogeneity of the low field MRI system. The measured blood velocities were $25.46{\pm}0.73cm/sec$, $24.02{\pm}0.34cm/sec$ and $26.15{\pm}1.50cm/sec$ in the superior sagittal sinus, straight sinus and the confluence of the two, respectively, which showed good agreement with the previous experimental values. Thus, the developed PC MRA technique for low field MRI system is expected to provide the useful velocity information to diagnose the large brain vessels.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.1-19
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• 2019

Artificial intelligence (AI) is one of the main driving forces leading the Fourth Industrial Revolution. The technologies associated with AI have already shown superior abilities that are equal to or better than people in many fields including image and speech recognition. Particularly, many efforts have been actively given to identify the current technology trends and analyze development directions of it, because AI technologies can be utilized in a wide range of fields including medical, financial, manufacturing, service, and education fields. Major platforms that can develop complex AI algorithms for learning, reasoning, and recognition have been open to the public as open source projects. As a result, technologies and services that utilize them have increased rapidly. It has been confirmed as one of the major reasons for the fast development of AI technologies. Additionally, the spread of the technology is greatly in debt to open source software, developed by major global companies, supporting natural language recognition, speech recognition, and image recognition. Therefore, this study aimed to identify the practical trend of AI technology development by analyzing OSS projects associated with AI, which have been developed by the online collaboration of many parties. This study searched and collected a list of major projects related to AI, which were generated from 2000 to July 2018 on Github. This study confirmed the development trends of major technologies in detail by applying text mining technique targeting topic information, which indicates the characteristics of the collected projects and technical fields. The results of the analysis showed that the number of software development projects by year was less than 100 projects per year until 2013. However, it increased to 229 projects in 2014 and 597 projects in 2015. Particularly, the number of open source projects related to AI increased rapidly in 2016 (2,559 OSS projects). It was confirmed that the number of projects initiated in 2017 was 14,213, which is almost four-folds of the number of total projects generated from 2009 to 2016 (3,555 projects). The number of projects initiated from Jan to Jul 2018 was 8,737. The development trend of AI-related technologies was evaluated by dividing the study period into three phases. The appearance frequency of topics indicate the technology trends of AI-related OSS projects. The results showed that the natural language processing technology has continued to be at the top in all years. It implied that OSS had been developed continuously. Until 2015, Python, C ++, and Java, programming languages, were listed as the top ten frequently appeared topics. However, after 2016, programming languages other than Python disappeared from the top ten topics. Instead of them, platforms supporting the development of AI algorithms, such as TensorFlow and Keras, are showing high appearance frequency. Additionally, reinforcement learning algorithms and convolutional neural networks, which have been used in various fields, were frequently appeared topics. The results of topic network analysis showed that the most important topics of degree centrality were similar to those of appearance frequency. The main difference was that visualization and medical imaging topics were found at the top of the list, although they were not in the top of the list from 2009 to 2012. The results indicated that OSS was developed in the medical field in order to utilize the AI technology. Moreover, although the computer vision was in the top 10 of the appearance frequency list from 2013 to 2015, they were not in the top 10 of the degree centrality. The topics at the top of the degree centrality list were similar to those at the top of the appearance frequency list. It was found that the ranks of the composite neural network and reinforcement learning were changed slightly. The trend of technology development was examined using the appearance frequency of topics and degree centrality. The results showed that machine learning revealed the highest frequency and the highest degree centrality in all years. Moreover, it is noteworthy that, although the deep learning topic showed a low frequency and a low degree centrality between 2009 and 2012, their ranks abruptly increased between 2013 and 2015. It was confirmed that in recent years both technologies had high appearance frequency and degree centrality. TensorFlow first appeared during the phase of 2013-2015, and the appearance frequency and degree centrality of it soared between 2016 and 2018 to be at the top of the lists after deep learning, python. Computer vision and reinforcement learning did not show an abrupt increase or decrease, and they had relatively low appearance frequency and degree centrality compared with the above-mentioned topics. Based on these analysis results, it is possible to identify the fields in which AI technologies are actively developed. The results of this study can be used as a baseline dataset for more empirical analysis on future technology trends that can be converged.