• The Korean Journal of Nuclear Medicine Technology
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• 제20권2호
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• pp.27-31
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• 2016

Purpose $^{99m}Tc-DMSA$ renal scan is a test for the comparison of the function by imaging the parenchyma of the kidneys by the cortex of a kidney and by computing the intake ratio of radiation by the left and right kidney. Since the distance between the kidneys and the bladder is not far given the bodily structure of an infant, the bladder is included in the examination domain. Research was carried out with the presumption that counts of bladder would impart an influence on the kidneys at the time of this renal scan. In consideration of the special feature that only a trace amount of a RI is injected in a pediatric examination, research on the method of injection was also carried out concurrently. Materials and Methods With 34 infants aged between 1 month to 12 months for whom a $^{99m}Tc-DMSA$ renal scan was implemented on the subjects, a Post IMAGE was acquired in accordance with the test time after having injected the same quantity of DMSA of 0.5mCi. Then, after having acquired an additional image by shielding the bladder by using a circular lead plate for comparison purposes, a comparison was made by illustrating the percentile of (Lt. Kidney counts + Rt. Kidney counts)/ Total counts, by drawing the same sized ROI (length of 55.2mm X width of 70.0mm). In addition, in the format of a 3-way stopcock, a Heparin cap and direct injection into the patient were performed in accordance with RI injection methods. The differences in the count changes in accordance with each of the methods were compared by injecting an additional 2cc of saline into the 3-way stopcock and Heparin cap. Results The image prior to shielding of the bladder displayed a kidney intake rate with a deviation of $70.9{\pm}3.18%$ while the image after the shielding of the bladder displayed a kidney intake rate with a deviation of $79.4{\pm}5.19%$, thereby showing approximately 6.5~8.5% of difference. In terms of the injection method, the method that used the 3-way form, a deviation of $68.9{\pm}2.80%$ prior to the shielding and a deviation of $78.1{\pm}5.14%$ after the shielding were displayed. In the method of using a Heparin cap, a deviation of $71.3{\pm}5.14%$ prior to the shielding and a deviation of $79.8{\pm}3.26%$ after the shielding were displayed. Lastly, in the method of direct injection into the patient, a deviation of $75.1{\pm}4.30%$ prior to the shielding and a deviation of $82.1{\pm}2.35%$ after the shielding were displayed, thereby illustrating differences in the kidney intake rates in the order of direct injection, a Heparin cap and the 3-way methods. Conclusion Since a substantially minute quantity of radiopharmaceuticals is injected for infants in comparison to adults, the cases of having shielded the bladder by removing radiation of the bladder displayed kidney intake rates that are improved from those of the cases of not having shielded the bladder. Although there are difficulties in securing blood vessels, it is deemed that the method of direct injection would be more helpful in acquisition of better images since it displays improved kidney intake rate in comparison to other methods.

• The Korean Journal of Nuclear Medicine Technology
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• 제22권1호
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• pp.90-97
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• 2018

Purpose The lung segment ratio which is obtained through quantitative analyses of lung perfusion scan images is calculated to evaluate the lung function pre and post surgery. In this Study, the planar image production methods by using Q-Metrix (GE Healthcare, USA) program capable of not only quantitative analysis but also computation of the segment ratio after having performed SPECT/CT are comparatively evaluated. Materials and Methods Lung perfusion scan and SPECT/CT were performed on 50 lung cancer patients prior to surgery who visited our hospital from May 1, 2015 to September 13, 2016 by using Discovery 670(GE Healthcare, USA) equipment. AP(Anterior Posterior)method that uses planar image divided the frontal and rear images into three rectangular portions by means of ROI tool while PO(Posterior Oblique)method computed the segment ratio by dividing the right lobe into three parts and the left lobe into two parts on the oblique image. Segment ratio was computed by setting the ROI and VOI in the CT image by using Q-Metrix program and statistically analysis was performed with SPSS Ver. 23. Results Regarding the correlation concordance rate of Q-Metrix and AP methods, RUL(Right upper lobe), RML(Right middle lobe) and RLL(Right lower lobe) were 0.224, 0.035 and 0.447. LUL(Left upper lobe) and LLL(Left lower lobe) were found to be 0.643 and 0.456, respectively. In the PO method, the right lobe were 0.663, 0.623 and 0.702, respectively, while the left lobe were 0.754 and 0.823. When comparison was made by using the Paired sample T-test, Right lobe were $11.6{\pm}4.5$, $26.9{\pm}6.2$ and $17.8{\pm}4.2$, respectively in the AP method. Left lobe were $28.4{\pm}4.8$ and $15.4{\pm}5.6$. The right lobe of PO had values of $17.4{\pm}5.0$, $10.5{\pm}3.6$ and $27.3{\pm}6.0$, while the left lobe had values of $21.6{\pm}4.8$ and $23.1{\pm}6.6$, thereby having statistically significant difference in comparison to the Q-Metrix method for each of the lobes (P<0.05). However, there was no statistically significant difference in Right middle lobe (P>0.05). Conclusion The AP method showed low concordance rate in correlation with the Q-Metrix method. However, PO method displayed high concordance rate overall. although AP method had significant differences in all lobes, there was no significant difference in Right middle lobe of PO method. Therefore, at the time of production of lung perfusion scan results, utilization of Q-Metrix method of SPECT/CT would be useful in computation of accurate resultant values. Moreover, it is deemed possible to expect obtain more practical sectional computation result values by using PO method at the time of planar image acquisition.

• Journal of Intelligence and Information Systems
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• 제23권4호
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.