• Progress in Medical Physics
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• v.3 no.2
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• pp.1-12
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• 1992

For intraoperative radiation therapy using electron beams, a cone system to deliver a large dose to the tumor during surgical operation and to save the surrounding normal tissue should be developed and dosimetry for the cone system is necessary to find proper X-ray collimator setting as well as to get useful data for clinical use. We developed a docking type of a cone system consisting of two parts made of aluminum: holder and cone. The cones which range from 4cm to 9cm with 1cm step at 100cm SSD of photon beam are 28cm long circular tubular cylinders. The system has two 26cm long holders: one for the cones larger than or equal to 7cm diamter and another for the smaller ones than 7cm. On the side of the holder is an aperture for insertion of a lamp and mirror to observe treatment field. Depth dose curve. dose profile and output factor at dept of dose maximum. and dose distribution in water for each cone size were measured with a p-type silicone detector controlled by a linear scanner for several extra opening of X-ray collimators. For a combination of electron energy and cone size, the opening of the X-ray collimator was caused to the surface dose, depths of dose maximum and 80%, dose profile and output factor. The variation of the output factor was the most remarkable. The output factors of 9MeV electron, as an example, range from 0.637 to 1.549. The opening of X-ray collimators would cause the quantity of scattered electrons coming to the IORT cone system. which in turn would change the dose distribution as well as the output factor. Dosimetry for an IORT cone system is inevitable to minimize uncertainty in the clinical use.

• Progress in Medical Physics
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• v.15 no.4
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• pp.202-209
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• 2004

Flat panel based digital radiography (DR) systems have recently become useful and important in the field of diagnostic radiology. For DRs with amorphous silicon photosensors, CsI(TI) is normally used as the scintillator, which produces visible light corresponding to the absorbed radiation energy. The visible light photons are converted into electric signal in the amorphous silicon photodiodes which constitute a two dimensional array. In order to produce good quality images, detailed behaviors of DR detectors to radiation must be studied. The relationship between air exposure and the DR outputs has been investigated in many studies. But this relationship was investigated under the condition of the fixed tube voltage. In this study, we investigated the relationship between the DR outputs and X-ray in terms of the absorbed energy in the detector rather than the air exposure using SPEC-l8, an X-ray energy spectrum model. Measured exposure was compared with calculated exposure for obtaining the inherent filtration that is a important input variable of SPEC-l8. The absorbed energy in the detector was calculated using algorithm of calculating the absorbed energy in the material and pixel values of real images under various conditions was obtained. The characteristic curve was obtained using the relationship of two parameter and the results were verified using phantoms made of water and aluminum. The pixel values of the phantom image were estimated and compared with the characteristic curve under various conditions. It was found that the relationship between the DR outputs and the absorbed energy in the detector was almost linear. In a experiment using the phantoms, the estimated pixel values agreed with the characteristic curve, although the effect of scattered photons introduced some errors. However, effect of a scattered X-ray must be studied because it was not included in the calculation algorithm. The result of this study can provide useful information about a pre-processing of digital radiography.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Progress in Medical Physics
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• v.27 no.1
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• pp.31-36
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• 2016

The paper discusses radiation dose of dual energy CT on which copper modulation layer, is mounted in order to improve diagnostic performance of the dual energy CT. The radiation dose is estimated using MCNPX and its results are compared with that of the conventional dual energy CT system. CT X-ray spectra of 80 and 120 kVp, which are usually used for thorax, abdominal, head, and neck CT scans, were generated by the SPEC78 code and were used for the source specification 'SDEF' card for MCNPX dose modeling. The copper modulation layer was located 20 cm away from a source covering half of the X-ray window. The radiation dose was measured as changing its thickness from 0.5 to 2.0 mm at intervals of 0.5 mm. Since the MCNPX tally provides only normalized values to a single particle, the dose conversion coefficients of F6 tally for the modulation layer-based dual energy CBCT should be calculated for matching the modeling results into the actual dose. The dose conversion coefficient is $7.2*10^4cGy/output$ that is obtained from dose calibration curve between F6 tally and experimental results in which GAFCHORMIC EBT3 films were exposed by an already known source. Consequently, the dose of the modulation layer-based dual energy cone beam CT is 33~40% less than that of the single energy CT system. On the basis of the results, it is considered that scattered dose produced by the copper modulation layer is very small. It shows that the modulation layer-based dual energy CBCT system can effectively reduce radiation dose, which is the major disadvantage of established dual energy CT.

• Progress in Medical Physics
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• v.27 no.1
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• pp.14-24
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• 2016

Since the head and neck region is densely located with organs at risk (OAR), OAR-sparing is an important issue in the treatment of head and neck cancers. This study-in which different treatment plans were performed varying the head tilt angle on brain tumor patients-investigates the optimal head elevation angle for sparing normal organs (e.g. the hippocampus) and further compares the dosimetric characteristics of different types of radiation equipment. we performed 3D conformal radiation therapy (3D-CRT), intensity-modulated radiation therapy (IMRT), and tomotherapy on 10 patients with brain tumors in the frontal lobe while varying the head tilt angle of patients to analyze the dosimetric characteristics of different therapy methods. In each treatment plan, 95% of the tumor volume was irradiated with a dose of 40 Gy in 10 fractions. The step and shoot technique with nine beams was used for IMRT, and the same prescription dose was delivered to the tumor volume for the 3D-CRT and tomotherapy plans. The homogeneity index, conformity index, and normal tissue complication probability (NTCP) were calculated. At a head elevation angle of $30^{\circ}$, conformity of the isodose curve to the target increased on average by 53%, 8%, and 5.4%. In 3D-CRT, the maximum dose received by the brain stem decreased at $15^{\circ}$, $30^{\circ}$, and $40^{\circ}$, compared to that observed at $0^{\circ}$. The NTCP value of the hippocampus observed in each modality was the highest at a head and neck angle of $0^{\circ}$ and the lowest at $30^{\circ}$. This study demonstrates that the elevation of the patients' head tilt angle in radiation therapy improves the target region's homogeneity of dose distribution by increasing the tumor control rate and conformity of the isodose curve to the target. Moreover, the study shows that the elevation of the head tilt angle lowers the NTCP by separating the tumor volume from the normal tissues, which helps spare OARs and reduce the delivered dose to the hippocampus.

• Progress in Medical Physics
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• v.18 no.3
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• pp.179-185
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• 2007

The purpose of this study is to investigate the spectra of a magnetic resonance spectroscopy (MRS) in accordance with the variance of TE and the volumes of metabolites in a localized voxel for the quality assurance using a designed single voxel spectroscopy QA phantom. Because a cone-shade phantom is designed as the volume of metabolite in a localized voxel is changeable, we try to analyze the peaks of each metabolite (NAA, Cr, Cho, Lac, etc.) in accordance with metabolite volume in a localized voxel as well as echo time (TE). All data were obtained using a 3T MRI/MRS machine and analyzed using $jMRUI^{(R)}$. The results of this study show that TE is in inverse proportion to the noise of MRS and the longer TE and the less metabolite volume in the localized voxel, the peak intensities of each metabolite decrease. In case of the lactate, its peak was observed on the all TE only if the greatest metabolite is included in the localized voxel. Then, the intensity of a metabolite is more sensitive to the metabolite volume in the localized voxel than the TE. These obtained in vitro MRS data is provide the guideline that is important for in vivo metabolite quantification. But, in the edge of cone-shape vial air bubbles were observed and spectrum could not obtained. Therefore our cone-shape MRS phantom needs to be modified in order to solve these problems.

• Progress in Medical Physics
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• v.20 no.4
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• pp.269-276
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• 2009

Radiation treatment techniques using photon beam such as three-dimensional conformal radiation therapy (3D-CRT) as well as intensity modulated radiotherapy treatment (IMRT) demand accurate dose calculation in order to increase target coverage and spare healthy tissue. Both jaw collimator and multi-leaf collimators (MLCs) for photon beams have been used to achieve such goals. In the Pinnacle3 treatment planning system (TPS), which we are using in our clinics, a set of model parameters like jaw collimator transmission factor (JTF) and MLC transmission factor (MLCTF) are determined from the measured data because it is using a model-based photon dose algorithm. However, model parameters obtained by this auto-modeling process can be different from those by direct measurement, which can have a dosimetric effect on the dose distribution. In this paper we estimated JTF and MLCTF obtained by the auto-modeling process in the Pinnacle3 TPS. At first, we obtained JTF and MLCTF by direct measurement, which were the ratio of the output at the reference depth under the closed jaw collimator (MLCs for MLCTF) to that at the same depth with the field size $10{\times}10\;cm^2$ in the water phantom. And then JTF and MLCTF were also obtained by auto-modeling process. And we evaluated the dose difference through phantom and patient study in the 3D-CRT plan. For direct measurement, JTF was 0.001966 for 6 MV and 0.002971 for 10 MV, and MLCTF was 0.01657 for 6 MV and 0.01925 for 10 MV. On the other hand, for auto-modeling process, JTF was 0.001983 for 6 MV and 0.010431 for 10 MV, and MLCTF was 0.00188 for 6 MV and 0.00453 for 10 MV. JTF and MLCTF by direct measurement were very different from those by auto-modeling process and even more reasonable considering each beam quality of 6 MV and 10 MV. These different parameters affect the dose in the low-dose region. Since the wrong estimation of JTF and MLCTF can lead some dosimetric error, comparison of direct measurement and auto-modeling of JTF and MLCTF would be helpful during the beam commissioning.

• Progress in Medical Physics
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• v.6 no.2
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• pp.71-81
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• 1995

Consecutive brain 〔Tc-99m〕ECD SPECT studies before and after acetazolamide (Diamox) administration have been performed with patients for the evaluation of cerebrovascular hemodynamic reserve. However, the quantitaitve potential of SPECT Diamox imaging is limited as a result of degrading fractors such as finite detector resolution, attenuation, scatter, poor counting statistics, and methods of data analysis. Making physical measurements in phantoms filled with known amounts of radioactivity can help characterize and potentially quantify the sensitivities. However, it is often very difficult to make a realistic phantom simulating patients in clinical situations. By computer simulation, we studied the sensitivities of ECD SPECT before and after Diamox administration. The sensitivity is defined as ($\Delta$N/N)/($\Delta$S/S)$\times$100％, where $\Delta$N denotes the differences in mean counts between post-and pre-Diamox in the measured data, N denotes the mean counts before Diamox in the measure data, $\Delta$S denotes the differences in mean counts between post-and pre-Diamox in the model, and S denotes the mean counts before Diamox in the model. In clinical Diamox studies, the percentage changes of radioactivity could be determined to measure changes in radioactivity concentration by Diamox after subtracting pre-from post-Diamox data. However, the optimal amount of subtraction for 100％ sensitivity is not known since this requires a thorough sensitivity analysis by computer simulation. For consecutive brain SPECT imaging model before and after Diamox, when 30％ increased radioactivity concentrations were assingned for Diamox effect in model, the sensitivities were measured as 51.03, 73.4, 94.00, 130.74％ for 0, 100, 150, 200％ subtraction, respectively. Sensitivity analysis indicated that the partial voluming effects due to finite detector resolution and statistical noise result in a significant underestimation of radioactivity measurements and the amount of underestimation depends on the. ％ increase of radioactivity concentration and ％ subtraction of pre-from post-Diamox data. The 150％ subtraction appears to be optimal in clinical situations where we expect approximately 30％ changes in radioactivity concentration. The computer simulation may be a powerful technique to study sensitivities of ECD SPECT before and after Diamox administration.

• Progress in Medical Physics
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• v.23 no.3
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• pp.154-161
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• 2012

Digital Radiography (DR) has rapidly developed in megavoltage X-ray imaging (MVI). Thus, a very simple and general quality assurance (QA) method is required. The purpose of this study was to evaluate the modulation transfer function (MTF), the noise power spectrum (NPS) and the detective quantum efficiency (DQE) for MVI using general QA method and computed radiography (CR) device. We used tungsten edge block with $19{\times}10{\times}1cm^3$ thickness and 6MV energy. For detector, CR-IP (image plate), CR-IP-lead, the CR-IP-back (lanex TM fast back screen), CR-IP-front (lanex TM fast front screen) were used and pre-sampling MTF was calculated. The MTF of CR-IP-front showed the highest value with 1.10 lp/mm although the CR-IP showed the only 0.70 lp/mm. The best NPS was observed in CR-IP front screen. According to the increase in spatial frequency, our results showed that DQE was approximately 1.0 cycles/mm. The present study demonstrates that the QA method with our home-made edge block can be used to evaluate MTF, NPS and DQE for MVI.

• Progress in Medical Physics
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• v.21 no.1
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• pp.35-41
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• 2010

The purpose of this study is to quantitate regional neurochemical profile of regional normal adult mice brain and assess regional metabolic differences by using ex vivo $^1H$ high-resolution magic angle spinning nuclear magnetic resonance spectroscopy ($^1H$ HR-MAS NMRS). The animals were matched in sex and age. The collected brain tissue included frontal cortex, temporal cortex, thalamus, and hippocampus. Quantitative 1D spectra were acquired on 40 samples with the CPMG pulse sequence (8 kHz spectral window, TR/TE = 5500/2.2 ms, NEX = 128, scan time: 17 min 20 sec). The mass of brain tissue and $D_2O$+TSP solvent were 8~14 mg and 7~13 mg. A total of 16 metabolites were quantified as follow: Acet, NAA, NAAG, tCr, Cr, tCho, Cho, GPC + PC, mIns, Lac, GABA, Glu, Gln, Tau and Ala. As a results, Acet, Cho, NAA, NAAG and mIns were showed significantly different aspects on frontal cortex, hippocampus, temporal cortex and thalamus respectively. The present study demonstrated that absolute metabolite concentrations were significantly different among four brain regions of adult mice. Our finding might be helpful to investigate brain metabolism of neuro-disease in animal model.