• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1091-1092
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• 2008

In this paper, it is described the performance of the breast cancer detection system that is composed of sensing, RF signal and image reconstruction part. Especially in the reconstruction algorithm, the amplitude and the phase of electric fields are used as compare value. So we improved to get the stable values of measured amplitude and phase of electric fields. Through compare images of reconstruction, we confirmed the performance of improved system.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.3
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• pp.201-208
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• 2007

Purpose: We investigated whether the diagnostic performance of SPECT scintimammography (SMM) can be improved by adding computer-aided diagnosis (CAD) of ultrasonography (US). Materials and methods: We reviewed breast SPECT SMM images and corresponding US images from 40 patients with breast masses (21 malignant and 19 benign tumors). The quantitative data of SPECT SMM were obtained as the uptake ratio of lesion to contralateral normal breast. The morphologic features of the breast lesions on US were extracted and quantitated using the automated CAD software program. The diagnostic performance of SPECT SMM and CAD of US alone was determined using receiver operating characteristic (ROC) curve analysis. The best discriminating parameter (D-value) combining SPECT SMM and the CAD of US was created. The sensitivity, specificity and accuracy of combined two diagnostic modalities were compared to those of a single one. Results: Both SPECT SMM and CAD of US showed a relatively good diagnostic performance (area under curve = 0.846 and 0.831, respectively). Combining the results of SPECT SMM and CAD of US resulted in improved diagnostic performance (area under curve =0.860), but there was no statistical differerence in sensitivity, specificity and accuracy between the combined method and a single modality. Conclusion: It seems that combining the results of SPECT SMM and CAD of breast US do not significantly improve the diagnostic performance for diagnosis of breast cancer, compared with that of SPECT SMM alone. However, SPECT SMM and CAD of US may complement each other in differential diagnosis of breast cancer.

• Progress in Medical Physics
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• v.17 no.1
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• pp.24-31
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• 2006

Automated analysis software was developed to measure the magnitude of the intrafractional and interfractional errors during breast radiation treatments. Error analysis results are important for determining suitable planning target volumes (PTV) prior to Implementing breast-conserving 3-D conformal radiation treatment (CRT). The electrical portal imaging device (EPID) used for this study was a Portal Vision LC250 liquid-filled ionization detector (fast frame-averaging mode, 1.4 frames per second, 256X256 pixels). Twelve patients were imaged for a minimum of 7 treatment days. During each treatment day, an average of 8 to 9 images per field were acquired (dose rate of 400 MU/minute). We developed automated image analysis software to quantitatively analyze 2,931 images (encompassing 720 measurements). Standard deviations ($\sigma$) of intrafractional (breathing motion) and intefractional (setup uncertainty) errors were calculated. The PTV margin to include the clinical target volume (CTV) with 95% confidence level was calculated as $2\;(1.96\;{\sigma})$. To compensate for intra-fractional error (mainly due to breathing motion) the required PTV margin ranged from 2 mm to 4 mm. However, PTV margins compensating for intefractional error ranged from 7 mm to 31 mm. The total average error observed for 12 patients was 17 mm. The intefractional setup error ranged from 2 to 15 times larger than intrafractional errors associated with breathing motion. Prior to 3-D conformal radiation treatment or IMRT breast treatment, the magnitude of setup errors must be measured and properly incorporated into the PTV. To reduce large PTVs for breast IMRT or 3-D CRT, an image-guided system would be extremely valuable, if not required. EPID systems should incorporate automated analysis software as described in this report to process and take advantage of the large numbers of EPID images available for error analysis which will help Individual clinics arrive at an appropriate PTV for their practice. Such systems can also provide valuable patient monitoring information with minimal effort.

• Journal of the Korean Chemical Society
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• v.60 no.2
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• pp.118-124
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• 2016

Parabens were commonly used for preventing the growth of microorganisms as preservatives in the pharmaceutical, cosmetic and food industry. Also, parabens are known endocrine disruptors because of their estrogenic effects on human. Parabens affect the endocrine system and show adverse effect such as, genital malformations, precocious puberty and testicular cancer in young children, infants and fetuses. In this study, we developed analytical method for four parabens (methyl paraben, ethyl paraben, propyl paraben, butyl paraben) in human breast milk which frequently consumed by newborn baby. The analytes were extracted using liquid-liquid extraction (LLE) after enzyme hydrolysis with protease and lipase, then quantitative analysis was performed by liquid chromatography tandem mass spectrometry (LC-MS/MS). The method validation results were as follows; the linearity of calibration curves were excellent with coefficient of determinations (r²) higher than 0.999, the limit of detections (LODs) were 0.019~0.044 ng/mL, the accuracies were 85.3~105.9% and the precisions were lower than 10%. The average concentration ± standard deviation of parabens in ten human breast milk sample were MP 0.660 ± 0.519 ng/mL, EP 1.631 ± 2.081 ng/mL and PP 0.326 ± 0.320 ng/mL, and BP was not detected.

• Archives of Plastic Surgery
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• v.47 no.6
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• pp.583-589
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• 2020

Background Reduction mammoplasty or mastopexy is performed as an additional balancing procedure in patients with large or ptotic breasts who undergo breast-conserving surgery (BCS). Radiation therapy on breasts that have undergone surgery may result in changes in the volume. This study presents a comparative analysis of patients who received post-BCS balancing procedures to determine whether volume changes were larger in breasts that received radiation therapy than on the contralateral side. Methods Thirty-six participants were selected among patients who received BCS using the inverted-T scar technique between September 2012 and July 2017, were followed up for 2 or more years, and had pre-radiation therapy computed tomography images and post-radiation therapy images taken between 12 and 18 months after completion. The average age of the participants was 53.5 years, their average body mass index was 26.62 kg/㎡. Results The pre- and post-radiation therapy volumes of the breasts receiving BCS were 666.08±147.48 mL and 649.33±130.35 mL, respectively. In the contralateral breasts, the volume before radiation therapy was 637.69±145.72 mL, which decreased to 628.14±166.41 mL after therapy. The volume ratio of the affected to the contralateral breasts was 1.05±0.10 before radiation therapy and 1.06±0.12 after radiation therapy. Conclusions The ratio of the volume between the two breasts immediately after surgery and at roughly 18 months postoperatively was not significantly different (P=0.98). For these reasons, we recommend a simultaneous single-stage balancing procedure as a reasonable option for patients who require radiation therapy after BCS without concerns regarding volume change.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.2
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• pp.133-137
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• 2010

Purpose: Breast cancer is known to be more vulnerable to bone metastasis and lymph node metastasis than other types of cancer, and nuclear examinations whole body bone scan and lymphoscintigraphy are performed commonly before and after breast cancer operation. In case whole body bone scan is performed on the day before lymphoscintigraphy, the radiopharmaceutical taken into and remaining in the bones provides anatomical information for tracking and locating sentinel lymph nodes. Thus, this study purposed to examine how much bone density affects in locating sentinel lymph nodes. Materials and Methods: The subjects of this study were 22 patients (average age $52{\pm}7.2$) who had whole body bone scan and lymphoscintigraphy over two days in our hospital during the period from January to December, 2009. In the blind test, 22 patients (average age $57{\pm}6.5$) who had lymphoscintigraphy using $^{57}Co$ flood phantom were used as a control group. In quantitative analysis, the relative ratio of the background to sentinel lymph nodes was measured by drawing ROIs on sentinel lymph nodes and the background, and in gross examination, each of a nuclear physician and a radiological technologist with five years' or longer field experience examined images through blind test in a five-point scale. Results: In the results of quantitative analysis, the relative ratio of the background to sentinel lymph nodes was 14.2:1 maximum and 8.5:1 ($SD{\pm}3.48$) on the average on the front, and 14.7:1 maximum and 8.5:1 ($SD{\pm}3.42$) on the average on the side. In the results of gross examination, when $^{57}Co$ flood phantom images were compared with images containing bones, the score was relative high as 3.86 ($SD{\pm}0.35$) point for $^{57}Co$ flood phantom images and 4.09 ($SD{\pm}0.42$) for bone images. Conclusion: When whole body bone scan was performed on the day before lymphoscintigraphy, the ratio of the background to sentinel lymph nodes was over 10:1, so there was no problem in locating lymph nodes. In addition, we expect to reduce examination procedures and improve the quality of images by indicating the location of sentinel lymph nodes using bone images as body contour without the use of a source.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• Journal of Radiation Protection and Research
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• v.43 no.1
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• pp.1-9
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• 2018

Background: In this study, we investigate the image quality of virtual monochromatic images synthesized from dual-energy computed tomography (DECT) at voltages of 80/140 kV and 100/140 kV. Materials and Methods: Virtual monochromatic images of a phantom are synthesized from DECT scans from 40 to 70 keV in steps of 1 keV under the two combinations of tube voltages. The dose allocation of dual-energy (DE) scan is 50% for both low- and high-energy tubes. The virtual monochromatic images are compared to single-energy (SE) images at the same radiation dose. In the DE images, noise is reduced using the 100/140 kV scan at the optimal monochromatic energy. Virtual monochromatic images are reconstructed from 40 to 70 keV in 1-keV increments and analyzed using two quality indexes: noise and contrast-to-noise ratio (CNR). Results and Discussion: The DE scan mode with the 100/140 kV protocol achieved a better maximum CNR compared to the 80/140 kV protocol for various materials, except for adipose and brain. Image noise is reduced with the 100/140 kV protocol. The CNR values of DE with the 100/140 kV protocol is similar to or higher than that of SE at 120 kV at the same radiation dose. Furthermore, the maximum CNR with the 100/140 kV protocol is similar to or higher than that of the SE scan at 120 kV. Conclusion: It was found that the CNR achieved with the 100/140 kV protocol was better than that with the 80/140 kV protocol at optimal monochromatic energies. Virtual monochromatic imaging using the 100/140 kV protocol could be considered for application in breast, brain, lung, liver, and bone CT in accordance with the CNR results.

• Journal of radiological science and technology
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• v.38 no.3
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• pp.229-235
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• 2015

Mammoplasty is currently increasing not only for cosmetic surgery, but as well as for the recovery after breast cancer surgery. The prostheses inserted into the breasts of women who have undergone mammoplasty, hide the breast substances and it is becoming increasingly difficult to diagnose breast disease, and fear is growing by the concern of the prostheses bursting by the strap. So we want to develop a strap applicable to women with prostheses inserted, to determine the usefulness, and we also want to compare the utility by comparing the total area of the Inner and Outer parts of the breast and Posterior Nipple Line (PNL), after obtaining video by applying the existing strap and the developed strap to phantom of the prostheses inserted patient shape. When the pressure by the developed pressure, the total area increased by 10.09% from CC view to $9,813.797mm^2$, 3.88% from CC-ID view to $7,621.531mm^2$, PNL increased by 3.41% from CC view to $90.916mm^2$, 1.64% from CC-ID view to$75.357mm^2$. And the breast tissue of the thorax side increased 3.53% from CC view to $177.725mm^2$, and 6.57% from CC-ID view to $152.510mm^2$, and we could verify that the prostheses were completely eliminated in the CC-ID images of developed strap, compared with the existing strap.