• Journal of radiological science and technology
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• v.40 no.3
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• pp.443-451
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• 2017

Mammography is commonly used for screening early breast cancer. However, mammographic images, which depend on the physical properties of breast components, are limited to provide information about whether a lesion is malignant or benign. Although a dual-energy subtraction technique decomposes a certain material from a mixture, it increases radiation dose and degrades the accuracy of material decomposition. In this study, we simulated a breast phantom using attenuation characteristics, and we proposed a technique to enable the accurate material decomposition by applying weighting factors for the dual-energy mammography based on a photon-counting detector using a Monte Carlo simulation tool. We also evaluated the contrast and noise of simulated breast images for validating the proposed technique. As a result, the contrast for a malignant tumor in the dual-energy weighted subtraction technique was 0.98 and 1.06 times similar than those in the general mammography and dual-energy subtraction techniques, respectively. However the contrast between malignant and benign tumors dramatically increased 13.54 times due to the low contrast of a benign tumor. Therefore, the proposed technique can increase the material decomposition accuracy for malignant tumor and improve the diagnostic accuracy of mammography.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• Journal of the Korean Society of Radiology
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• v.8 no.6
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• pp.333-338
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• 2014

The fact is that in addition to an increase in social needs that must be managed systematically unnecessary exposure in the field of medical Recent important that the shield has emerged. However, products that are now in practical use, are not subdivided as compared to various medical radiology. Therefore, in the present study, we tried to present with the help of Monte Carlo simulation the structure of the shielding material that has been optimized. Simulated estimation result, the energy of the mammography for (30 kVp) spectrum, check the shielding rate of 90% or more $30{\mu}mPb$, at 2 mmAl case of shielding material of a single, at design time of 1 mmAl and 0.03 mmPb a double shield structure it is determined that more efficient. Also, check the blocking rate of 90% or more $340{\mu}mPb$, at 30 mmAl energy captured general in (80 kVp) spectra, it is considered that a double shield structure, design 1 mmAl and 0.3 mmPb is useful. These results, be used as basic material for the development of commercialization customized products for dose reduction is expected.