• Journal of Magnetics
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• v.17 no.2
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• pp.158-162
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• 2012

Although 3.0T magnetic resonance imaging (MRI) has the advantages of a higher signal to noise ratio (SNR) and contrast than 1.5T MRI, there are limitations on the contrast between white and grey matter because of the long T1 recovery time when T1 images are obtained using the Spin Echo Technique. To overcome this, T1 weighted images are obtained occasionally using the inversion recovery (IR) technique, which employs a relatively long TR. The aim of this study was to determine the optimal TI in a brain examination when a T1 weighted image is obtained using the IR technique. Eight participants (male: 7, female: 1, average age: $34{\pm}14.11$) with a normal diagnosis were targeted from February 18, 2012 to February 27, 2012, and the contrast between white and grey matter as well as the contrast to noise ratio (CNRs) in each participant were measured. The CNRs of white matter and grey matter were highest at TI = 600, 650, 750, 900, 1050 and 1100 ms when the TR was 1100, 1400, 1700, 2000, 2300 and 2600 ms, respectively. Therefore, as the TIs were $44.425{\pm}0.877%$ of the TRs in the TR range of 1400-2300 ms, the optimal T1 weighted images that describe the contrast between white and grey matter can be obtained if the TIs are compensated for with $44.425{\pm}0.877%$ of the TRs in the time of setting TIs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3427-3433
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• 2009

This paper attempted to suggest the standards of light transmittance based on the analysis of the changes in contrast sensitivity of photopic condition according to the light transmittance of tinted lenses. The subjects of the experiment were 24 male and 13 female adults with normal ocular functions. They were asked to wear four tinted lenses (gray, brown, red, and green) in turn. The light transmittance of the lenses were 80~90%, 60~80%, 43~60%, and 30~43%, respectively. Contrast sensitivities were measured by using F.A.C.T. chart. The results showed that contrast sensitivities were decreased in proportion to the decrease of the light transmittance, and visual sensitivity of brown lenses were the highest and that of red lenses were the lowest. The results of the experiments suggested that the light transmittance should be over 38% for brown, gray and green lenses, and over 52% for red lenses. Accordingly the appropriate light transmittance should be considered when tinted lenses are selected for the purpose of eye protection in everyday life.

• Journal of Magnetics
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• v.21 no.1
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• pp.115-124
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• 2016

The present study analyzes T1 TSE and T1 slice sel. IR (dark_fluid) signal strength according to the degree of gadolinium contrast agent dilution and analyzes the turbo factors with regard to changes in the maximum and overall signal strength to study correlations between changes and signal-to-noise ratios (SNRs) and compare peak-to-peak SNR (PSNR) enhancement in order to improve the quality of T1-weighted images. Enhancement TR (600 msec) evaluated to determine the T1 TSE turbo factor and obtain the maximum signal strength, T1WI were used sequentially to experiment with turbo factors_1-4. T1 slice sel. IR (dark-fluid) was used to sequentially test turbo factors_2-5 but not turbo factor_1 at a TR (1500 msec) and compare data at an increase in T1 of 900 msec. The T1 TSE was reduced according to the contrast agent concentration. Phantom signal strength increased, whereas turbo factors_1-4 exhibited maximum signal strength at a concentration of 3 mmol, followed by a gradual decrease. In the turbo factors_2-5, the signal strength increased sharply to maximum signal strength at 0.7 mmol, followed by a reduction. T1 TSE had a greater maximum signal strength than did T1 slice sel. IR (dark_fluid). A comparison of SNR found that T1 TSE imaging was superior (33.3 dB) in turbo factor_1 and T1 slice sel. IR (dark_fluid) was highest (33.9 dB) at turbo factor_5. A PSNR comparison analysis was not sufficient to distinguish between the images obtained with both techniques at 30 dB or higher under all experimental conditions.

• Journal of radiological science and technology
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• v.44 no.1
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• pp.1-8
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• 2021

The purpose of this study was to analyze the effect of automatic exposure control (AEC) control factors in digital radiography systems based on the fine contrast images using coin phantoms. The AEC control factors were targeted at the range of dominent zone, sensitivity, and density. The dominent zone was divided into cases where a single coin was used to cover the field configuration, and cases where seven coins were used to cover the field configuration. The sensitivity was classified into three stages (200, 400, 800) and the density was classified into three stages (2.5, 0, 2.5). Image quality was evaluated by signal to noise ratio (SNR) and contrast to noise ratio (CNR). Then, the automatically exposed tube current was measured. As a result, the X-ray image of seven coins obtained a result value of about 1.2 times higher for SNR and 1.9 times higher for CNR than the X-ray image for one coin. The tube current was also about 1.6 times higher. In conclusion, In AEC, the higher the field configuration and dominent zone are matched and the higher the density, the lower the sensitivity, which increases the tube current and CNR, which increases the image quality. Therefore, it is judged that the appropriate setting of the range of dominent zone, sensitivity, and density of the control, which is the AEC control factor, could improve the fine contrast of images.

• Investigative Magnetic Resonance Imaging
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• v.4 no.1
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• pp.42-51
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• 2000

Purpose: Projection-type Fast Spin Echo (PFSE) imaging is robust to patient motion or flow related artifact compared to conventional Fast Spin Echo (FSE) imaging, however, it has difficulty in controlling $T_2$ contrast. In this paper, Tz contrast in the PFSE method is analyzed and compared with those of the FSE method with various effective echo times by computer simulation. The contrasts in the FSE and PFSE methods are also compared by experiments with volunteers. From the analysis and simulation, it is shown that ${T_2}-weighted$ images can well be obtained by the PFSE method proposed. Materials and methods: Pulse sequence for the PFSE method is implemented at a 1.0 Tesla whole body MRI system and $T_2$ contrasts in the PFSE and FSE methods are analyzed by computer simulation and experiment with volunteers. For the simulation, a mathematical phantom composed of various $T_2$ values is devised and $T_2$ contrast in the reconstructed image by the PFSE is compared to those by the FSE method with various effective echo times. Multi-slice ${T_2}-weighted$ head images of the volunteers obtained by the PFSE method are also shown in comparison with those by the FSE method at a 1.0 Tesla whole body MRI system. Results: From the analysis, $T_2$ contrast by the PFSE method appears similar to those by the FSE method with the effective echo time in a range of SO-lOOms. Using a mathematical phantom, contrast in the PFSE image appears close to that by the FSE method with the effective echo time of 96ms. From experiment with volunteers, multi-slice $T_2-weighted$ images are obtained by the PFSE method having contrast similar to that of the FSE method with the effective echo time of 96ms. Reconstructed images by the PFSE method show less motion related artifact compared to those by the FSE method. Conclusion: The projection-type FSE imaging acquires multiple radial lines with different angles in polar coordinate in k space using multiple spin echoes. The PFSE method is robust to patient motion or flow, however, it has difficulty in controlling $T_2$ contrast compared to the FSE method. In this paper, it is shown that the PFSE method provides good $T_2$ contrast (${T_2}-weighted$ images) similar to the FSE method by both computer simulation and experiments with volunteers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.3
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• pp.1670-1683
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• 2017

Mitotic event recognition is a crucial and challenging task in biomedical applications. In this paper, we introduce the slow feature analysis and propose a fully-automated mitotic event recognition method for cell populations imaged with time-lapse phase contrast microscopy. The method includes three steps. First, a candidate sequence extraction method is utilized to exclude most of the sequences not containing mitosis. Next, slow feature is learned from the candidate sequences using slow feature analysis. Finally, a hidden conditional random field (HCRF) model is applied for the classification of the sequences. We use a supervised SFA learning strategy to learn the slow feature function because the strategy brings image content and discriminative information together to get a better encoding. Besides, the HCRF model is more suitable to describe the temporal structure of image sequences than nonsequential SVM approaches. In our experiment, the proposed recognition method achieved 0.93 area under curve (AUC) and 91% accuracy on a very challenging phase contrast microscopy dataset named C2C12.

• Progress in Medical Physics
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• v.33 no.4
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• pp.158-163
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• 2022

Purpose: We subjected scanning electron microscopic (SEM) images of the active layer of EBT3 film to texture analysis to determine the dose-response curve. Methods: Uncoated Gafchromic EBT3 films were prepared for direct surface SEM scanning. Absorbed doses of 0-20 Gy were delivered to the film's surface using a 6 MV TrueBeam STx photon beam. The film's surface was scanned using a SEM under 100× and 3,000× magnification. Four textural features (Homogeneity, Correlation, Contrast, and Energy) were calculated based on the gray level co-occurrence matrix (GLCM) using the SEM images corresponding to each dose. We used R-square to evaluate the linear relationship between delivered doses and textural features of the film's surface. Results: Correlation resulted in higher linearity and dose-response curve sensitivity than Homogeneity, Contrast, or Energy. The R-square value was 0.964 for correlation using 3,000× magnified SEM images with 9-pixel offsets. Dose verification was used to determine the difference between the prescribed and measured doses for 0, 5, 10, 15, and 20 Gy as 0.09, 1.96, -2.29, 0.17, and 0.08 Gy, respectively. Conclusions: Texture analysis can be used to accurately convert microscopic structural changes to the EBT3 film's surface into absorbed doses. Our proposed method is feasible and may improve the accuracy of film dosimetry used to protect patients from excess radiation exposure.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1339-1346
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• 2019

In the characteristic of the brain malignant, the blood vessels and tumors have the same color and shape, and the boundary distinction is not clear, Therefore, it is difficult to observe the naked eye. Because of the high invasiveness, the risk of recurrence is high. Therefore, complete resection of the tumor is essential. The method for distinguishing the boundary between blood vessels and tumors is a fluorescence contrast method using indocyanine green (ICG), a fluorescence contrast agent. In ICG, the concentration range analysis is very important because the fluorescence expression state varies depending on the concentration. However, since the analysis result of the fluorescence expression condition is insufficient according to the current concentration, this paper proposes by analyzing the initial protocol of the concentration range. 780 nm infrared light was irradiated to the ICG sample to observe the fluorescence expression through a near infrared (NIR) camera. The wavelength is measured by using a spectrum instrument (ocean view) to observe the fluorescence expression wavelength of 811nm. As a result of analyzing the mol concentration according to each sample, the fluorescence expression range was found to be 16.15-0.16M and the optimum fluorescence concentration on the brightest part was found to be 3.23-0.81M.

• Korean Journal of Radiology
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• v.23 no.4
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.2
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• pp.39-47
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• 2014

Purpose As breast cancer patients continue to increase every year, cases of BSGI are on the rise with a heavier reliance on it. However, BSGI protocol in hospitals was not studied enough despite it was covered by hospital's condition and recommendation of manufacturers. The objective of the study was an examination of methods to be applicable to BSGI protocols, putting the self-development phantom to use in quality assessment of the images. Materials and Methods Dilon 6800 (Dilon Technologies Inc, Newport News, USA) was used in the study and five different sizes of sphere were distinctively produced in the phantom. The study used $^{99m}TcO_4$. The cases were classified in to three categories that background radioactivity to region of interest as ratio of 2: 4: 8, They were acquired images for 5, 7, 10mins. The acquired image was set region of interest according to the size of sphere, and We analyzed quantitative and qualitative analysis. The acquired data statistically analyzed with SPSS ver.18.0. Results As the result of quantitative and qualitative analysis, count rate of each sphere in accordance with difference of injection dose showed that higher count rate as injection dose and sphere size increased (P<0.005). Count rate of each sphere in accordance with difference of acquisition time showed that higher count rate as acquisition time and sphere size increased (P<0.005). Contrast noise ratio of each sphere in accordance with difference of injection dose showed that higher contrast noise ratio as injection dose increased. Particularly, Contrast noise ratio of eight times ratio images was the highest among. Contrast noise ratio of each sphere in accordance with difference of acquisition time showed that higher contrast noise ratio as acquisition time increased. And, Contrast noise ratio of seven minute image was the highest among (P<0.005). Conclusion There was significant change of Contrast noise ratio through quantitative and qualitative analysis. Moreover, We found usefulness of phantom. If Institutions identified image through the phantom study and they made BSGI protocol, We expected to help the improvement of diagnostic value of the images.