• Korean Journal of Radiology
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• v.22 no.4
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• pp.535-546
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• 2021

Objective: To evaluate the feasibility of texture analysis on non-contrast-enhanced T1 maps of cardiac magnetic resonance (CMR) imaging for the diagnosis of myocardial injury in acute myocardial infarction (MI). Materials and Methods: This study included 68 patients (57 males and 11 females; mean age, 55.7 ± 10.5 years) with acute ST-segment-elevation MI who had undergone 3T CMR after a percutaneous coronary intervention. Forty patients of them also underwent a 6-month follow-up CMR. The CMR protocol included T2-weighted imaging, T1 mapping, rest first-pass perfusion, and late gadolinium enhancement. Radiomics features were extracted from the T1 maps using open-source software. Radiomics signatures were constructed with the selected strongest features to evaluate the myocardial injury severity and predict the recovery of left ventricular (LV) longitudinal systolic myocardial contractility. Results: A total of 1088 segments of the acute CMR images were analyzed; 103 (9.5%) segments showed microvascular obstruction (MVO), and 557 (51.2%) segments showed MI. A total of 640 segments were included in the 6-month follow-up analysis, of which 160 (25.0%) segments showed favorable recovery of LV longitudinal systolic myocardial contractility. Combined radiomics signature and T1 values resulted in a higher diagnostic performance for MVO compared to T1 values alone (area under the curve [AUC] in the training set; 0.88, 0.72, p = 0.031: AUC in the test set; 0.86, 0.71, p = 0.002). Combined radiomics signature and T1 values also provided a higher predictive value for LV longitudinal systolic myocardial contractility recovery compared to T1 values (AUC in the training set; 0.76, 0.55, p < 0.001: AUC in the test set; 0.77, 0.60, p < 0.001). Conclusion: The combination of radiomics of non-contrast-enhanced T1 mapping and T1 values could provide higher diagnostic accuracy for MVO. Radiomics also provides incremental value in the prediction of LV longitudinal systolic myocardial contractility at six months.

• Dementia and Neurocognitive Disorders
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• v.21 no.4
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• pp.138-146
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• 2022

Background and Purpose: Magnetic resonance imaging (MRI) helps with brain development analysis and disease diagnosis. Brain volumes measured from different ages using MRI provides useful information in clinical evaluation and research. Therefore, we trained machine learning models that predict the brain age gap of healthy subjects in the East Asian population using T1 brain MRI volume images. Methods: In total, 154 T1-weighted MRIs of healthy subjects (55-83 years of age) were collected from an East Asian community. The information of age, gender, and education level was collected for each participant. The MRIs of the participants were preprocessed using FreeSurfer(https://surfer.nmr.mgh.harvard.edu/) to collect the brain volume data. We trained the models using different supervised machine learning regression algorithms from the scikit-learn (https://scikit-learn.org/) library. Results: The trained models comprised 19 features that had been reduced from 55 brain volume labels. The algorithm BayesianRidge (BR) achieved a mean absolute error (MAE) and r squared (R²) of 3 and 0.3 years, respectively, in predicting the age of the new subjects compared to other regression methods. The results of feature importance analysis showed that the right pallidum, white matter hypointensities on T1-MRI scans, and left hippocampus comprise some of the essential features in predicting brain age. Conclusions: The MAE and R² accuracies of the BR model predicting brain age gap in the East Asian population showed that the model could reduce the dimensionality of neuroimaging data to provide a meaningful biomarker for individual brain aging.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12C
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• pp.1173-1183
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• 2006

In this paper, we propose an efficient method for improving visual quality of AR-FGS (Adaptive Reference FGS) which is adopted as a key scheme for SVC (Scalable Video Coding) or H.264 scalable extension. The standard FGS (Fine Granularity Scalability) adopts AR-FGS that introduces temporal prediction into FGS layer by using a high quality reference signal which is constructed by the weighted average between the base layer reconstructed imageand enhancement reference to improve the coding efficiency in the FGS layer. However, when the enhancement stream is truncated at certain bitstream position in transmission, the rest of the data of the FGS layer will not be available at the FGS decoder. Thus the most noticeable problem of using the enhancement layer in prediction is the degraded visual quality caused by drifting because of the mismatch between the reference frame used by the FGS encoder and that by the decoder. To solve this problem, we exploit the principle of cyclical block coding that is used to encode quantized transform coefficients in a cyclical manner in the FGS layer. Encoding block coefficients in a cyclical manner places 'higher-value' bits earlier in the bitstream. The quantized transform coefficients included in the ealry coding cycle of cyclical block coding have higher probability to be correctly received and decoded than the others included in the later cycle of the cyclical block coding. Therefore, we can minimize visual quality degradation caused by bitstream truncation by adjusting weighting factor to control the contribution of the bitstream produced in each coding cycle of cyclical block coding when constructing the enhancement layer reference frame. It is shown by simulations that the improved AR-FGS scheme outperforms the standard AR-FGS by about 1 dB in maximum in the reconstructed visual quality.

• Journal of the Korean Society of Radiology
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• v.84 no.5
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• pp.1094-1109
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• 2023

Purpose To investigate whether CT-based tumor regression grade (ctTRG) can be used to predict the response to neoadjuvant chemotherapy (NAC) in colon cancer. Materials and Methods A total of 53 patients were enrolled. Two radiologists independently assessed the ctTRG using the length, thickness, layer pattern, and luminal and extraluminal appearance of the tumor. Changes in tumor volume were also analyzed using the 3D Slicer software. We evaluated the association between pathologic TRG (pTRG) and ctTRG. Patients with Rödel's TRG of 2, 3, or 4 were classified as responders. In terms of predicting responder and pathologic complete remission (pCR), receiver operating characteristic was compared between ctTRG and tumor volume change. Results There was a moderate correlation between ctTRG and pTRG (ρ = -0.540, p < 0.001), and the interobserver agreement was substantial (weighted κ = 0.672). In the prediction of responder, there was no significant difference between ctTRG and volumetry (Az = 0.749, criterion: ctTRG ≤ 3 for ctTRG, Az = 0.794, criterion: ≤ -27.1% for volume, p = 0.53). Moreover, there was no significant difference between the two methods in predicting pCR (p = 0.447). Conclusion ctTRG might predict the response to NAC in colon cancer. The diagnostic performance of ctTRG was comparable to that of CT volumetry.

• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.112-126
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• 2022

Purpose To determine the value of 3 Tesla (T) MRI texture analysis for predicting tumor margin infiltration in soft tissue sarcomas. Materials and Methods Thirty-one patients who underwent 3T MRI and had a pathologically confirmed diagnosis of soft tissue sarcoma were included in this study. Margin infiltration on pathology was used as the gold standard. Texture analysis of soft tissue sarcomas was performed on axial T1-weighted images (WI) and T2WI, fat-suppressed contrast-enhanced (CE) T1WI, diffusion-weighted images (DWI) with b-value of 800 s/mm², and apparent diffusion coefficient (ADC) was mapped. Quantitative parameters were compared between sarcomas with infiltrative margins and those with circumscribed margins. Results Among the 31 patients with soft tissue sarcomas, 23 showed tumor margin infiltration on pathology. There were significant differences in kurtosis with the spatial scaling factor (SSF) of 0 and 6 on T1WI, kurtosis (SSF, 0) on CE-T1WI, skewness (SSF, 0) on DWI, and skewness (SSF, 2, 4) on ADC between sarcomas with infiltrative margins and those with circumscribed margins (p ≤ 0.046). The area under the receiver operating characteristic curve based on MR texture features for identification of infiltrative tumor margins was 0.951 (p < 0.001). Conclusion MR texture analysis is reliable and accurate for the prediction of infiltrative margins of soft tissue sarcomas.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of the Korean Society for Railway
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• v.17 no.3
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• pp.193-200
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• 2014

In this study, fatigue tests on existing continuous welded rail (CWR) on a concrete track were carried out. Based on the test results, a S-N curve expressing the remaining life of the CWR at a fracture probability of 50% was obtained using weighted probit analysis suitable for small-sample fatigue data sets. As rails had different histories in terms of accumulated passing tonnage, the test data were corrected to average out the accumulated passing tonnage. The remaining service life for the CWR on the concrete track in an urban railway was estimated using the prediction equation for the bending stress of rail developed in the past to estimate rail base bending stress and taking the surface irregularities into consideration. Estimating the remaining service life of the CWR in an urban railway showed that the rail replacement period could be extended over 200MGT. In addition, comparing the concrete track to the ballast track, the fatigue life of rail was analyzed as approximately 300MGT higher than. Therefore, the rail replacement criteria needs to distinguish between the ballast track and the concrete track, and not the criteria needs to be changed as a target for the maintenance, although it is necessary to remove longitudinal rail surface irregularities at welds by grinding.

• Journal of Dairy Science and Biotechnology
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• v.35 no.3
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• pp.202-207
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• 2017

The objective of this study was to analyze the effects of homogenization, storage temperature, and storage period on the creaming of milk fat and changes in fat contents in the upper and lower layers and to predict the conditions for optimal homogenization efficiency using response surface methodology (RSM). The homogenization pressure, storage temperature, and storage period were set as independent variables of RSM, and the dependent variables were creaming, US Public Health Service (USPHS) code, and volume weighted mean diameter ($D_{4,3}$) in the upper and lower layers. Based on the results of RSM and regression analysis, the correlation coefficient ($R^2$) between experimental data and predicted values by RSM for homogenized milk was estimated to be more than 0.8. The RSM analysis indicated that optimal homogenization pressures of 14 MPa or more and 17 MPa or more were required to maintain the creaming layer of 3 mm or less during the storage for 15 days at $10^{\circ}C$ and $20^{\circ}C$, respectively. To keep the USPHS code at less than 10% for 15 days at $10^{\circ}C$ and $20^{\circ}C$, milk should be homogenized with a pressure of 16.8 MPa or more and 17 MPa or more, respectively.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.8
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• pp.640-647
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• 2014

Infrared signature of aircraft exhaust plume is the critical factor for aircraft survivability. To improve the military aircraft survivability, the accurate prediction of infrared signature for the propulsion system is needed. The numerical analysis of thermal fluid field for nozzle inflow, free stream flow, and plume region is conducted by using the in-house code. Weighted Sum of Gray Gases Model based on Narrow Band with regrouping is adopted to calculate the spectral infrared signature emitted from aircraft exhaust plume. The accuracy and reliability of the developed code are validated in the one-dimensional band model. It is found that the infrared radiant intensity is relatively more strong in the plume through the analysis, the results show the different characteristic of the spectral infrared signature along the temperature, the partial pressure, and the species distribution. The continuous spectral radiant intensity is shown near the nozzle exit due to the emission from the nozzle wall.

• The Journal of the Korea Contents Association
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• v.8 no.3
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• pp.252-263
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• 2008

This study was conducted to derive the optimal regionalization of the precipitation data which can be classified on the basis of climatologically and geographically homogeneous regions all over the regions except Cheju and Ulreung islands in Korea. A total of 65 rain gauges were used to regional analysis of precipitation. Annual maximum series for the consecutive durations of 1, 3, 6, 12, 24, 36, 48 and 72hr were used for various statistical analyses. K-means clustering mettled is used to identify homogeneous regions all over the regions. Five homogeneous regions for the precipitation were classified by the K-means clustering. Using the L-moment ratios and Kolmogorov-Smirnov test, the underlying regional probability distribution was identified to be the generalized extreme value (GEV) distribution among applied distributions. The regional and at-site parameters of the generalized extreme value distribution were estimated by the linear combination of the probability weighted moments, L-moment. The regional and at-site analysis for the design rainfall were tested by Monte Carlo simulation. Relative root-mean-square error (RRMSE), relative bias (RBIAS) and relative reduction (RR) in RRMSE were computed and compared with those resulting from at-site Monte Carlo simulation. All show that the regional analysis procedure can substantially reduce the RRMSE, RBIAS and RR in RRMSE in the prediction of design rainfall. Consequently, optimal design rainfalls following the regions and consecutive durations were derived by the regional frequency analysis.