• Korean Journal of Radiology
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• v.24 no.3
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• pp.190-203
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• 2023

Objective: We aimed to assess and validate the radiologic and clinical factors that were associated with recurrence and survival after curative surgery for heterogeneous targetoid primary liver malignancies in patients with chronic liver disease and to develop scoring systems for risk stratification. Materials and Methods: This multicenter retrospective study included 197 consecutive patients with chronic liver disease who had a single targetoid primary liver malignancy (142 hepatocellular carcinomas, 37 cholangiocarcinomas, 17 combined hepatocellular carcinoma-cholangiocarcinomas, and one neuroendocrine carcinoma) identified on preoperative gadoxetic acid-enhanced MRI and subsequently surgically removed between 2010 and 2017. Of these, 120 patients constituted the development cohort, and 77 patients from separate institution served as an external validation cohort. Factors associated with recurrence-free survival (RFS) and overall survival (OS) were identified using a Cox proportional hazards analysis, and risk scores were developed. The discriminatory power of the risk scores in the external validation cohort was evaluated using the Harrell C-index. The Kaplan-Meier curves were used to estimate RFS and OS for the different risk-score groups. Results: In RFS model 1, which eliminated features exclusively accessible on the hepatobiliary phase (HBP), tumor size of 2-5 cm or > 5 cm, and thin-rim arterial phase hyperenhancement (APHE) were included. In RFS model 2, tumors with a size of > 5 cm, tumor in vein (TIV), and HBP hypointense nodules without APHE were included. The OS model included a tumor size of > 5 cm, thin-rim APHE, TIV, and tumor vascular involvement other than TIV. The risk scores of the models showed good discriminatory performance in the external validation set (C-index, 0.62-0.76). The scoring system categorized the patients into three risk groups: favorable, intermediate, and poor, each with a distinct survival outcome (all log-rank p < 0.05). Conclusion: Risk scores based on rim arterial enhancement pattern, tumor size, HBP findings, and radiologic vascular invasion status may help predict postoperative RFS and OS in patients with targetoid primary liver malignancies.

• Korean Journal of Radiology
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• v.24 no.10
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• pp.1028-1037
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• 2023

Objective: To evaluate the computed tomography (CT) features for diagnosing metastatic cervical lymph nodes (LNs) in patients with differentiated thyroid cancer (DTC) and validate the CT-based risk stratification system suggested by the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) guidelines. Materials and Methods: A total of 463 LNs from 399 patients with DTC who underwent preoperative CT staging and ultrasound-guided fine-needle aspiration were included. The following CT features for each LN were evaluated: absence of hilum, cystic changes, calcification, strong enhancement, and heterogeneous enhancement. Multivariable logistic regression analysis was performed to identify independent CT features associated with metastatic LNs, and their diagnostic performances were evaluated. LNs were classified into probably benign, indeterminate, and suspicious categories according to the K-TIRADS and the modified LN classification proposed in our study. The diagnostic performance of both classification systems was compared using the exact McNemar and Kosinski tests. Results: The absence of hilum (odds ratio [OR], 4.859; 95% confidence interval [CI], 1.593-14.823; P = 0.005), strong enhancement (OR, 28.755; 95% CI, 12.719-65.007; P < 0.001), and cystic changes (OR, 46.157; 95% CI, 5.07-420.234; P = 0.001) were independently associated with metastatic LNs. All LNs showing calcification were diagnosed as metastases. Heterogeneous enhancement did not show a significant independent association with metastatic LNs. Strong enhancement, calcification, and cystic changes showed moderate to high specificity (70.1%-100%) and positive predictive value (PPV) (91.8%-100%). The absence of the hilum showed high sensitivity (97.8%) but low specificity (34.0%). The modified LN classification, which excluded heterogeneous enhancement from the K-TIRADS, demonstrated higher specificity (70.1% vs. 62.9%, P = 0.016) and PPV (92.5% vs. 90.9%, P = 0.011) than the K-TIRADS. Conclusion: Excluding heterogeneous enhancement as a suspicious feature resulted in a higher specificity and PPV for diagnosing metastatic LNs than the K-TIRADS. Our research results may provide a basis for revising the LN classification in future guidelines.

• Korean Journal of Radiology
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• v.23 no.11
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• pp.1102-1111
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• 2022

Objective: To evaluate the ultrasonography (US) features for diagnosing metastasis in cervical lymph nodes (LNs) in patients with thyroid cancer and compare the US classification of risk of LN metastasis between European and Korean guidelines. Materials and Methods: From January 2014 to December 2018, US-guided fine-needle aspiration was performed on 836 LNs from 714 patients for the preoperative nodal staging of thyroid cancer. The US features of LNs were retrospectively reviewed for the following features: size, presence of hilum, margin, orientation, cystic change, punctate echogenic foci (PEF), large echogenic foci, eccentric cortical thickening, abnormal vascularity, and cortical hyperechogenicity. A multiple logistic regression analysis was performed to identify the independent US features for the diagnosis of metastatic LNs. The diagnostic performance of independent US features was subsequently evaluated. LNs were categorized according to the Korean Thyroid Imaging Reporting and Data System (K-TIRADS) and European Thyroid Association (ETA) guidelines, and the correlation between the two sets of classifications was assessed. Results: Absence of the hilum, presence of cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features of metastatic LNs. Cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity showed high specificity (86.8%-99.6%). The absence of the hilum had the highest sensitivity yet low specificity (66.4%). When LNs were classified according to the ETA guidelines and K-TIRADS, they yielded similar categorizations of malignancy risks and were strongly correlated (Spearman coefficient, 0.9766 [95% confidence interval, 0.973-0.979]). According to the ETA guidelines, 9.8% (82/836) of LNs were classified as "not specified." Conclusion: Absence of hilum, cystic changes, PEF, abnormal vascularity, and cortical hyperechogenicity were independent US features suggestive of metastatic LNs in thyroid cancer. Both K-TIRADS and the ETA guidelines provided similar risk stratification for metastatic LNs with a high correlation; however, the ETA guidelines failed to classify 9.8% of LNs into a specific risk stratum. These results may provide a basis for revising LN classification in future guidelines.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.37-44
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• 2024

Even though MR can reveal excellent soft-tissue contrast and functional information, CT is also required for electron density information for accurate dose calculation in Radiotherapy. For the fusion of MRI and CT images in RT treatment planning workflow, patients are normally scanned on both MRI and CT imaging modalities. Recently deep-learning-based generations of CT images from MR images became possible owing to machine learning technology. This eliminated CT scanning work. This study implemented a CycleGan deep-learning-based CT image generation from MR images. Three CT generators whose learning is based on T1- , T2- , or T1-&T2-weighted MR images were created, respectively. We found that the T1-weighted MR image-based generator can generate better than other CT generators when T1-weighted MR images are input. In contrast, a T2-weighted MR image-based generator can generate better than other CT generators do when T2-weighted MR images are input. The results say that the CT generator from MR images is just outside the practical clinics and the specific weight MR image-based machine-learning generator can generate better CT images than other sequence MR image-based generators do.

• Journal of Korean Society of Rural Planning
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• v.30 no.1
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• pp.57-66
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• 2024

This study aimed to develop a precise vegetation cover classification model for small streams using the combination of drone remote sensing and support vector machine (SVM) techniques. The chosen study area was the Idong stream, nestled within Geosan-gun, Chunbuk, South Korea. The initial stage involved image acquisition through a fixed-wing drone named ebee. This drone carried two sensors: the S.O.D.A visible camera for capturing detailed visuals and the Sequoia+ multispectral sensor for gathering rich spectral data. The survey meticulously captured the stream's features on August 18, 2023. Leveraging the multispectral images, a range of vegetation indices were calculated. These included the widely used normalized difference vegetation index (NDVI), the soil-adjusted vegetation index (SAVI) that factors in soil background, and the normalized difference water index (NDWI) for identifying water bodies. The third stage saw the development of an SVM model based on the calculated vegetation indices. The RBF kernel was chosen as the SVM algorithm, and optimal values for the cost (C) and gamma hyperparameters were determined. The results are as follows: (a) High-Resolution Imaging: The drone-based image acquisition delivered results, providing high-resolution images (1 cm/pixel) of the Idong stream. These detailed visuals effectively captured the stream's morphology, including its width, variations in the streambed, and the intricate vegetation cover patterns adorning the stream banks and bed. (b) Vegetation Insights through Indices: The calculated vegetation indices revealed distinct spatial patterns in vegetation cover and moisture content. NDVI emerged as the strongest indicator of vegetation cover, while SAVI and NDWI provided insights into moisture variations. (c) Accurate Classification with SVM: The SVM model, fueled by the combination of NDVI, SAVI, and NDWI, achieved an outstanding accuracy of 0.903, which was calculated based on the confusion matrix. This performance translated to precise classification of vegetation, soil, and water within the stream area. The study's findings demonstrate the effectiveness of drone remote sensing and SVM techniques in developing accurate vegetation cover classification models for small streams. These models hold immense potential for various applications, including stream monitoring, informed management practices, and effective stream restoration efforts. By incorporating images and additional details about the specific drone and sensors technology, we can gain a deeper understanding of small streams and develop effective strategies for stream protection and management.

• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.213-222
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• 2024

This study analyzed the volumes generated by deep learning and atlas-based automatic segmentation methods, as well as the Dice similarity coefficient and 95% Hausdorff distance, according to the conditions of conduction voltage and conduction current in computed tomography for lung radiotherapy. The first result, the volumes generated by the atlas-based smart segmentation method showed the smallest volume change as a function of the change in tube voltage and tube current, while Aview RT ACS and OncoStudio using deep learning showed smaller volumes at tube currents lower than 100 mA. The second result, the Dice similarity coefficient, showed that Aview RT ACS was 2% higher than OncoStuido, and the 95% Hausdorff distance results also showed that Aview RT ACS analyzed an average of 0.2-0.5% higher than OncoStudio. However, the standard deviation of the respective results for tube current and tube voltage is lower for OncoStudio, which suggests that the results are consistent across volume variations. Therefore, caution should be exercised when using deep learning-based automatic segmentation programs at low perfusion voltages and low perfusion currents in CT imaging conditions for lung radiotherapy, and similar results were obtained with conventional atlas-based automatic segmentation programs at certain perfusion voltages and perfusion currents.

• Journal of Korean Ophthalmic Optics Society
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• v.16 no.4
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• pp.409-415
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• 2011

Purpose: To design applied anamorphic lens that focal length ratio is 3:1 optical system to improve detecting distance. Methods: We defined a boundary condition as $50^{\circ}{\sim}60^{\circ}$ for viewing angle, horizontal direction 36mm, vertical direction 12 mm for focal length, f-number 4, $15{\mu}m{\times}15{\mu}m$ for pixel size and limit resolution 25% in 33l p/mm. Si, ZnS and ZnSe as a materials were used and 4.8 ${\mu}m$, 4.2 ${\mu}m$, 3.7 ${\mu}m$ as a wavelength were set. optical performance with detection distance, narcissus and athermalization in designed camera were analyzed. Results: F-number 4, y direction 12 mm and x direction 36 mm for focal length of the thermal optical system were satisfied. Total length of the system was 76 mm so that an overall volume of the system was reduced. Astigmatism and spherical aberration was within ${\pm}$0.10 which was less than 2 pixel size. Distortion was within 10% so there was no matter to use as a thermal optical camera. MTF performance for the system was over 25% from 33l p/mm to full field so it was satisfied with the boundary condition. Designed optical system was able to detect up to 2.9 km and reduce a diffused image by decreasing a narcissus value from all surfaces except the 4th surface. From sensitivity analysis, MTF resolution was increased on changing temperature with the 5th lens which was assumed as compensation. Conclusions: Designed optical system which used anamorphic lens was satisfied with boundary condition. an increasing resolution with temperature, longer detecting distance and decreasing of narcissus were verified.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.3
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• pp.229-234
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• 2008

Purpose: A urea breath test (UBT) using C-14 or C-13 has been developed for identifying Helicobacter (H) pylori infection on the basis of urease production with release of labeled $CO_2$. We investigated if the C-14 and C-13 UBT have the difference to reflect the presence and degree of H. pylori infection detected by gastro-duodenoscopic biopsies (CBx) in the same patients. Materials and methods: Thirty eight patients (M:F = 28:10, age $53.4{\pm}13.0$ yrs) with upper gastrointestinal symptoms such as indigestion, gastric fullness or pain consecutively underwent C-14 UBT, GBx and C-13 UBT within one week before medications. For the C-14 UBT, a single breath sample was collected at 10 minutes after ingestion of C-14 urea (37 KBq) capsule and counting was done in a liquid scintillation counter for 1 minute, and the results were classified as positive (${\ge}$ 200 dpm), intermediate (50-199 dpm) or negative (50 dpm). For the C-13 UBT, the results were classified as positive (${\ge}2.5\%_{\circ}$) or negative ($<2.5\%_{\circ}$). The results of GBx with Giemsa stain were graded 0 (normal) to 4 (diffuse) according to the distribution of H. pylori by the Wyatt method. We compared C-14 UBT and C-13 UBT results with GBx grade as a gold standard. Results: The prevalence of H. pylori infection by GBx with Giemsa stain was 25/38 (65.8%). In the assessment of the presence of H. pylori infection, the C-14 UBT global performance yielded sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of 92.0%, 92.3%, 95.8%, 91.7% and 92.1%, respectively. However, the C-13 UBT had sensitivity, specificity, PPV, NPV and accuracy of 96.0%, 84.6%, 92.3%, 91.7% and 92.1%, respectively. The more significant correlation in C-14 than C-13 UBT (r=0.948 vs r=0.819, p <0.001) was found between the value of UBT and the grade of distribution of H. pylori infection. Conclusion: We conclude that the diagnostic performance between C-14 and C-13 UBT to detect H. pylori infection is not significantly different, but the value of C-14 UBT more significantly reflects the degree of bacterial distribution.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.90-90
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• 2014

The Immersion Grating Infrared Spectrometer (IGRINS) is the first astronomical spectrograph that uses a silicon immersion grating as its dispersive element. IGRINS fully covers the H and K band atmospheric transmission windows in a single exposure. It is a compact high-resolution cross-dispersion spectrometer whose resolving power R is 40,000. An individual volume phase holographic grating serves as a secondary dispersing element for each of the H and K spectrograph arms. On the 2.7m Harlan J. Smith telescope at the McDonald Observatory, the slit size is $1^{{\prime}{\prime}}{\times}15^{{\prime}{\prime}}$. IGRINS has a plate scale of 0.27" pixel-1 on a $2048{\times}2048$ pixel Teledyne Scientific & Imaging HAWAII-2RG detector with a SIDECAR ASIC cryogenic controller. The instrument includes four subsystems; a calibration unit, an input relay optics module, a slit-viewing camera, and nearly identical H and K spectrograph modules. The use of a silicon immersion grating and a compact white pupil design allows the spectrograph collimated beam size to be 25mm, which permits the entire cryogenic system to be contained in a moderately sized ($0.96m{\times}0.6m{\times}0.38m$) rectangular Dewar. The fabrication and assembly of the optical and mechanical components were completed in 2013. From January to July of this year, we completed the system optical alignment and carried out commissioning observations on three runs to improve the efficiency of the instrument software and hardware. We describe the major design characteristics of the instrument including the system requirements and the technical strategy to meet them. We also present the instrumental performance test results derived from the commissioning runs at the McDonald Observatory.

• Korean Journal of Cognitive Science
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• v.27 no.3
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• pp.441-467
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• 2016

This fMRI study is aim to investigate effects of competitive environment in cognitive empathic process in human brain. Empathy is known as a crucial factor for human's adaptive behavior in aspects of social cognition and it is almost automatic process, on the other hand competitive situation is psychologically devastated environment to win someone for getting rewards. We hypnotized that reading and understanding of other person's mind are a specific characteristic related to survival evolutionarily, however competition would have an effect on the empathic cognitive process because of mechanisms of competition. To manipulate the competitive atmosphere, one researcher took a role of competitor against participants and they were instructed to get monetary rewards when their performance was better than a competitor. 21 participants(9 males and 12 females) performed to judge the emotional valence of the empathic task consisted of illustrated images with various situation could be experienced in real world as on $1^{st}$ person perspective in both competitive and non-competitive condition, and did same performance with objects stimulus in control condition. In order to examine the competition effects on empathic process,, hemodynamic response were obtained during fMRI session and the imaging data were analyzed to identify brain regions where responses to each condition across the two consecutive runs. Participants' reaction time in competitive condition was faster statistically significant than non-competitive one. Activation for competitive condition increased in the following areas: ACC, mPFC, SMG, thalamus extended caudate and Nacc, parahippocampal gyrus, and for non-competitive condition increased paracingulate gyrus, temporal pole, vmPFC, superior occipital gyrus. As a result of regression analysis using empathic scores as covariance, the rSMG, IFG, fusiform gyrus, thalamus, putamen were correlated with higher empathic levels, and TPJ were correlated with lower empathic scores. We suggest that these observations could mean competitive environment have an effect on neural base of cognitive empathic process.