• Korean Journal of Heritage: History & Science
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• v.45 no.3
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• pp.174-193
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• 2012

This study focused on distribution ratio of stone properties based on material characteristic analysis, provenance presumption and transportation route interpretation of the Sungnyemun stone block foundation. The stone block foundation is composed of pinkish granite (56.0%), reddish granite (4.5%) and leucocratic granite (26.2%) of original stones and pinkish granite of new stones(13.3%). The rock-forming minerals for granites are consisted mainly of quartz, alkali-feldspar, plagioclase and biotite, and are similar geochemical evolution trend of major, rare earth, compatible and incompatible elements. Therefore, it is clear that the rocks are genetically same origin. As a result of magnetic susceptibility measurement, the pinkish and reddish granite of original stones and pinkish granite of new stones showed normal distribution around about 4.00(${\times}10^{-3}SI\;unit$). But the leucocratic granite of original stones were confirmed ilmenite series under about 1.00(${\times}10^{-3}SI\;unit$). As a result of provenance interpretation and transportation route analysis based on the petrological results, the provenance of pinkish granite and reddish granite of original stones are presumed the north slope in Namsan mountain and Naksan mountain. Also, the leucocratic granite of original stones and the pinkish granite of new stones are strongly possible furnished from the south and north slope in Namsan mountain and Naksan mountain, respectively.

• Journal of the Korean Society of Radiology
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• v.79 no.6
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• pp.340-347
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• 2018

Purpose: To evaluate value of image subtraction for identifying hepatocellular carcinoma (HCC) capsule on gadoxetic acid-enhanced MR images. Materials and Methods: This study involved 108 patients at risk of HCC preoperatively examined using gadoxetic acid-enhanced MRI with hepatic resection between May 2015 and February 2017. We evaluated qualities of subtraction images and presence of capsular appearance on portal venous or transitional phases conventional and subtraction images. We assessed effect of capsular appearance on subtraction images on HCC. Results: After excluding 1 patient who had treated by transarterial chemoembolization prior to surgery and 33 patients with unsatisfactory subtraction image qualities, 82 focal hepatic lesions (73 HCC, 5 non-HCC malignancies, and 4 benign) from 74 patients were analyzed. Regarding detection of capsules, sensitivity, accuracy, and area under the receiver operating characteristic curve (AUC) on subtraction images were significantly higher than those on conventional images (95.4%, 89.0%, and 0.80, respectively; p < 0.001), though specificities were same (64.7%). For diagnosis of HCC, sensitivity, accuracy, and AUC on subtraction images were significantly higher than on conventional images (82.2%, 79.3%, and 0.69, respectively; p = 0.011), though specificities were identical (55.6%). Conclusion: Portal venous or transitional phase gadoxetic acid-enhanced MRI subtraction images could improve detection of HCC capsule.

• Journal of the Korea Computer Graphics Society
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• v.28 no.3
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• pp.31-43
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• 2022

Recently, metaverse is rapidly expanding its area as a platform that can be applied to various fields. In particular, the function that allows many users to interact in a three-dimensional space allows VR/AR-based educational content to be used as a more advanced concept. Due to the nature of engineering education, it is often based on three-dimensional objects. In the case of a three-dimensional object, it is difficult to explain through two-dimensional videos or documents, and it becomes more difficult to express when the process of changing the object is included. The three-dimensional space of the metaverse can improve this difficulty based on real-time rendering. Another characteristic of engineering education is that there are many invisible elements. Although it is involved in the movement of objects due to electromagnetic fields, magnetic fields, and forces, it is the main reason for increasing learning difficulty because it is invisible. These problems can also help learning because they can be visually represented in the metaverse space. In this paper, the results of the establishment of the metaverse platform for engineering education and the real-time lecture contents produced based on it are described, and the applied results and lecture evaluation are discussed. Lectures using a total of 9 metaverse contents were conducted, and 90% of the positive lecture evaluation results were obtained.

• Korean Journal of Radiology
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• v.21 no.4
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• pp.402-412
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• 2020

Objective: To evaluate the performance of predicting early recurrence using preoperative factors only in comparison with using both pre-/postoperative factors. Materials and Methods: We retrospectively reviewed 549 patients who had undergone curative resection for single hepatcellular carcinoma (HCC) within Milan criteria. Multivariable analysis was performed to identify pre-/postoperative high-risk factors of early recurrence after hepatic resection for HCC. Two prediction models for early HCC recurrence determined by stepwise variable selection methods based on Akaike information criterion were built, either based on preoperative factors alone or both pre-/postoperative factors. Area under the curve (AUC) for each receiver operating characteristic curve of the two models was calculated, and the two curves were compared for non-inferiority testing. The predictive models of early HCC recurrence were internally validated by bootstrap resampling method. Results: Multivariable analysis on preoperative factors alone identified aspartate aminotransferase/platelet ratio index (OR, 1.632; 95% CI, 1.056-2.522; p = 0.027), tumor size (OR, 1.025; 95% CI, 0.002-1.049; p = 0.031), arterial rim enhancement of the tumor (OR, 2.350; 95% CI, 1.297-4.260; p = 0.005), and presence of nonhypervascular hepatobiliary hypointense nodules (OR, 1.983; 95% CI, 1.049-3.750; p = 0.035) on gadoxetic acid-enhanced magnetic resonance imaging as significant factors. After adding postoperative histopathologic factors, presence of microvascular invasion (OR, 1.868; 95% CI, 1.155-3.022; p = 0.011) became an additional significant factor, while tumor size became insignificant (p = 0.119). Comparison of the AUCs of the two models showed that the prediction model built on preoperative factors alone was not inferior to that including both pre-/postoperative factors {AUC for preoperative factors only, 0.673 (95% confidence interval [CI], 0.623-0.723) vs. AUC after adding postoperative factors, 0.691 (95% CI, 0.639-0.744); p = 0.0013}. Bootstrap resampling method showed that both the models were valid. Conclusion: Risk stratification solely based on preoperative imaging and laboratory factors was not inferior to that based on postoperative histopathologic risk factors in predicting early recurrence after curative resection in within Milan criteria single HCC patients.

• Korean Journal of Radiology
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• v.22 no.2
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• pp.213-224
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• 2021

Objective: Clinical outcomes of patients who undergo transarterial chemoembolization (TACE) for single small hepatocellular carcinoma (HCC) are not consistent, and may differ based on certain imaging findings. This retrospective study was aimed at determining the efficacy of pre-TACE CT or MR imaging findings in predicting survival outcomes in patients with small HCC upon being treated with TACE. Besides, the study proposed to build a risk prediction model for these patients. Materials and Methods: Altogether, 750 patients with functionally good hepatic reserve who received TACE as the first-line treatment for single small HCC between 2004 and 2014 were included in the study. These patients were randomly assigned into training (n = 525) and validation (n = 225) sets. Results: According to the results of a multivariable Cox analysis, three pre-TACE imaging findings (tumor margin, tumor location, enhancement pattern) and two clinical factors (age, serum albumin level) were selected and scored to create predictive models for overall, local tumor progression (LTP)-free, and progression-free survival in the training set. The median overall survival time in the validation set were 137.5 months, 76.1 months, and 44.0 months for low-, intermediate-, and high-risk groups, respectively (p < 0.001). Time-dependent receiver operating characteristic curves of the predictive models for overall, LTP-free, and progression-free survival applied to the validation cohort showed acceptable areas under the curve values (0.734, 0.802, and 0.775 for overall survival; 0.738, 0.789, and 0.791 for LTP-free survival; and 0.671, 0.733, and 0.694 for progression-free survival at 3, 5, and 10 years, respectively). Conclusion: Pre-TACE CT or MR imaging findings could predict survival outcomes in patients with small HCC upon treatment with TACE. Our predictive models including three imaging predictors could be helpful in prognostication, identification, and selection of suitable candidates for TACE in patients with single small HCC.

• Journal of the Korean Society of Radiology
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• v.83 no.3
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• pp.620-631
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• 2022

Purpose This study aimed to investigate the diagnostic performance of features suggestive of nodal metastasis on preoperative MRI in patients with invasive breast cancer. Materials and Methods We retrospectively reviewed the preoperative breast MRI of 192 consecutive patients with surgically proven invasive breast cancer. We analyzed MRI findings of axillary lymph nodes with regard to the size, long/short ratio, cortical thickness, shape and margin of the cortex, loss of hilum, asymmetry, signal intensity (SI) on T2-weighted images (T2WI), degree of enhancement in the early phase, and enhancement kinetics. Receiver operating characteristic (ROC) analysis, chi-square test, t test, and McNemar's test were used for statistical analysis. Results Increased shorter diameter, uneven cortical shape, increased cortical thickness, loss of hilum, asymmetry, irregular cortical margin, and low SI on T2WI were significantly suggestive of metastasis. ROC analysis revealed the cutoff value for the shorter diameter and cortical thickness as 8.05 mm and 2.75 mm, respectively. Increased cortical thickness (> 2.75 mm) and uneven cortical shape showed significantly higher sensitivity than other findings in McNemar's test. Irregular cortical margins showed the highest specificity (100%). Conclusion Cortical thickness > 2.75 mm and uneven cortical shape are more sensitive parameters than other findings, and an irregular cortical margin is the most specific parameter for predicting axillary metastasis in patients with invasive breast cancer.

• Journal of the Korean Society of Radiology
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• v.82 no.3
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• pp.613-623
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• 2021

Purpose To evaluate the MRI findings and clinical factors that are characteristic of patients who ultimately undergo surgery for medial epicondylitis. Materials and Methods Fifty-two consecutive patients who were diagnosed with medial epicondylitis and underwent an elbow MRI between March 2010 and December 2018 were included in this retrospective study. The patients' demographic information, clinical data, and MRI findings were evaluated. All variables were compared between the conservative treatment and surgical treatment groups. Logistic regression analyses were conducted to identify which factors were associated with surgical treatment. Results Common flexor tear (CFT) tear size showed a statistically significant difference in both the transverse and longitudinal planes (p < 0.001, p = 0.013). The CFT abnormality grade significantly differed in both the transverse and longitudinal planes (p = 0.022, p = 0.003). A significant difference was also found in the medial collateral ligament abnormality (p = 0.025). Logistic regression analyses showed that only the transverse diameter of the CFT tear size (odds ratio: 1.864; 95% confidence interval: 1.264-2.750) was correlated with surgical treatment. Conclusion Of patients diagnosed with medial epicondylitis, patients with a larger transverse CFT tear size tend to undergo surgical treatment ultimately.

• Korean Journal of Radiology
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• v.25 no.4
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• pp.363-373
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• 2024

Objective: To develop and evaluate a deep learning model for automated segmentation and detection of bone metastasis on spinal MRI. Materials and Methods: We included whole spine MRI scans of adult patients with bone metastasis: 662 MRI series from 302 patients (63.5 ± 11.5 years; male:female, 151:151) from three study centers obtained between January 2015 and August 2021 for training and internal testing (random split into 536 and 126 series, respectively) and 49 MRI series from 20 patients (65.9 ± 11.5 years; male:female, 11:9) from another center obtained between January 2018 and August 2020 for external testing. Three sagittal MRI sequences, including non-contrast T1-weighted image (T1), contrast-enhanced T1-weighted Dixon fat-only image (FO), and contrast-enhanced fat-suppressed T1-weighted image (CE), were used. Seven models trained using the 2D and 3D U-Nets were developed with different combinations (T1, FO, CE, T1 + FO, T1 + CE, FO + CE, and T1 + FO + CE). The segmentation performance was evaluated using Dice coefficient, pixel-wise recall, and pixel-wise precision. The detection performance was analyzed using per-lesion sensitivity and a free-response receiver operating characteristic curve. The performance of the model was compared with that of five radiologists using the external test set. Results: The 2D U-Net T1 + CE model exhibited superior segmentation performance in the external test compared to the other models, with a Dice coefficient of 0.699 and pixel-wise recall of 0.653. The T1 + CE model achieved per-lesion sensitivities of 0.828 (497/600) and 0.857 (150/175) for metastases in the internal and external tests, respectively. The radiologists demonstrated a mean per-lesion sensitivity of 0.746 and a mean per-lesion positive predictive value of 0.701 in the external test. Conclusion: The deep learning models proposed for automated segmentation and detection of bone metastases on spinal MRI demonstrated high diagnostic performance.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.379-394
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• 2020

Purpose To design a scoring system to predict malignancy of additional MRI-detected lesions in breast cancer patients. Materials and Methods Eighty-six lesions (64 benign and 22 malignant) detected on preoperative MRI of 68 breast cancer patients were retrospectively included. The clinico-radiologic features were correlated with the histopathologic results using the Student's t-test, Fisher's exact test, and logistic regression analysis. The scoring system was designed based on the significant predictive features of malignancy, and its diagnostic performance was compared with that of the Breast Imaging-Reporting and Data System (BI-RADS) category. Results Lesion size ≥ 8 mm (p < 0.001), location in the same quadrant as the primary cancer (p = 0.005), delayed plateau kinetics (p = 0.010), T2 isointense (p = 0.034) and hypointense (p = 0.024) signals, and irregular mass shape (p = 0.028) were associated with malignancy. In comparison with the BI-RADS category, the scoring system based on these features with suspicious non-mass internal enhancement increased the diagnostic performance (area under the receiver operating characteristic curve: 0.918 vs. 0.727) and detected three false-negative cases. With this scoring system, 22 second-look ultrasound examinations (22/66, 33.3%) could have been avoided. Conclusion The scoring system based on the lesion size, location relative to the primary cancer, delayed kinetic features, T2 signal intensity, mass shape, and non-mass internal enhancement can provide a more accurate approach to evaluate MRI-detected lesions in breast cancer patients.

• Korean Journal of Radiology
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• v.23 no.10
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• pp.986-997
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• 2022

Objective: Whether metabolic redistribution occurs in patients with white matter hyperintensities (WMHs) on magnetic resonance imaging (MRI) is unknown. This study aimed 1) to propose a measure of the brain metabolic network for an individual patient and preliminarily apply it to identify impaired metabolic networks in patients with WMHs, and 2) to explore the clinical and imaging features of metabolic redistribution in patients with WMHs. Materials and Methods: This study included 50 patients with WMHs and 70 healthy controls (HCs) who underwent ¹⁸F-fluorodeoxyglucose-positron emission tomography/MRI. Various global property parameters according to graph theory and an individual parameter of brain metabolic network called "individual contribution index" were obtained. Parameter values were compared between the WMH and HC groups. The performance of the parameters in discriminating between the two groups was assessed using the area under the receiver operating characteristic curve (AUC). The correlation between the individual contribution index and Fazekas score was assessed, and the interaction between age and individual contribution index was determined. A generalized linear model was fitted with the individual contribution index as the dependent variable and the mean standardized uptake value (SUV_mean) of nodes in the whole-brain network or seven classic functional networks as independent variables to determine their association. Results: The means ± standard deviations of the individual contribution index were (0.697 ± 10.9) × 10^-3 and (0.0967 ± 0.0545) × 10^-3 in the WMH and HC groups, respectively (p < 0.001). The AUC of the individual contribution index was 0.864 (95% confidence interval, 0.785-0.943). A positive correlation was identified between the individual contribution index and the Fazekas scores in patients with WMHs (r = 0.57, p < 0.001). Age and individual contribution index demonstrated a significant interaction effect on the Fazekas score. A significant direct association was observed between the individual contribution index and the SUV_mean of the limbic network (p < 0.001). Conclusion: The individual contribution index may demonstrate the redistribution of the brain metabolic network in patients with WMHs.