• Journal of Microbiology and Biotechnology
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• v.6 no.4
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• pp.250-254
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• 1996

MR-387A [(2S, 3R)-2-hydroxy-3-amino-4-phenylbutanoyl-L-valyl-L-prolyl-(2, 4-trans)- L-4-hydroxy-proline] reversibly inhibits aminopeptidase N (BC 3.4.11.2) in a process that is remarkable for its unusual degree of time dependence. The time required to inactivate the enzyme by 50$%$ ($t_{1/2}$) for establishing steady-state levels of $EI^*$complex was approximately 5 minutes. This indicates that the inhibition is a slow-binding process. In dissociation experiments of $EI^*$ complex, enzymic activity was regained slowly in a quadratic equation, indicating that the inhibition of aminopeptidase N by MR-387A is tight-binding and reversible. Thus, the binding of MR-387A by aminopeptidase N is slow and tight, with $K_{i}$ (for initial collision complex, EI) and $K_i{^*}$ (for final tightened complex, $EI^*$) of $2.2\times10^{-8}$ M (from Lineweaver-Burk plot) and $4.4\times10^{-10}$ M (from rate constants), respectively. These data indicate that MR-387A and aminopeptidase N are bound approximately 200-fold more tightly in the final $EI^*$complex than in the initial collision EI complex.

• The Korean Journal of Nuclear Medicine
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• v.32 no.4
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• pp.332-343
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• 1998

Purpose: The purpose of this study was to assess the diagnostic accuracy of various quantitation methods using F-18-fluorodeoxyglucose (FDG) in patients with malignant or benign lung lesion. Materials and Methods: 22 patients (13 malignant including 5 bronchoalverolar cell cancer; 9 benign lesions including 1 hamartoma and 8 active inflammation) were studied after overnight fasting. We performed dynamic PET imaging for 56 min after injection of 370 MBq (10 mCi) of FDG. Standardized uptake values normalized to patient's body weight and plasma glucose concentration (SUVglu) were calculated. The uptake rate constant of FDG and glucose metabolic rate were quantified using Patlak graphical analysis (Kpat and MRpat), three compartment-five parameter model (K5p, MR5p), and six parameter model taking into account heterogeneity of tumor tissue (K6p, MR6p). Areas under receiver operating characteristic curves (ROC) were calculated for each method. Results: There was no significant difference of rate constant or glucose metabolic rate measured by various quantitation methods between malignant and benign lesions. The area under ROC curve were 0.73 for SUVglu, 0.66 for Kpat, 0.77 for MRpat, 0.71 for K5p, 0.73 for MR5p, 0.70 for K6p, and 0.78 for MR6p. No significant difference of area under the ROC curve between these methods was observed except the area between Kpat vs. MRpat (p<0.05). Conclusion: Quantitative methods did not improve diagnostic accuracy in comparison with nonkinetic methods. However, the clinical utility of these methods needs to be evaluated further in patients with low pretest likelihood of active inflammation or bronchoalveolar cell carcinoma.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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• pp.48-48
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• 2003

Abstract: 현대 질병의 최종진단은 세포수준의 해부형태학적 연구가 일반적이며 광학현미경이나 전자현미경을 이용한 병리조직학적 소견을 바탕으로 진단되어지고 있다. 이러한 진단에는 In vivo 검사가 거의 불가능하여 주로 생검을 통한 연구만 수행되고 있어 진단함에 있어 단계적인 불편이 초래될 뿐만 아니라 악성 종양의 전이를 유발시킬 수 있고 또한 생리 및 생화학적 분석이 어렵다. 이러한 문제점을 해결하기 위해 고분해능의 RF Surface Coil을 개발하여 In vivo 및 비침습적인 방법인 MR Technology를 이용하고자 한다. Introduction: 피부조직과 같은 미세 인체구조 연구를 위해 고해상도 3T MRI 시스템에 적합한 고분해능의 RF surface coil을 개발하고 있다. In vivo 연구를 위한 여러 parameter를 최적화하여 기능영상에도 부합된다. 비침습적인 In vivo 검사에 의한 세포수준의 극 미세구조의 연구가 가능해짐으로써 과거 시행하던 침습적인 생검없이 각종질환의 진단적 접근이 병리학적 수준으로 향상되어 질병의 정확한 진단이 가능해지게 될 것이다. Method: 고분해능의 RF Surface Coil을 제작하여 3T MR 장비에서 피부 미세구조연구에 보다 적합하도록 In vivo 및 In vitro 실험을 수행하였다. In vitro 실험은 In vivo 연구를 위한 여러 parameter들을 최적화하기 위한 기초 실험을 하였고 다양한 팬톰들을 이용하여 Tl 강조영상, T2 강조영상을 획득하였으며, SNR을 높이기 위한 개선에 대한 연구를 수행하였다. In vivo 실험은 정상피부에서 다양한 부위에 대한 피부영상의 예비 연구를 수행하였다. Result and Discussion: 비침습적인 In vivo 검사에 의한 세포수준의 극 미세구조의 연구가 가능해짐으로써 과거 시행하던 침습적인 생검없이 각종질환의 진단적 접근이 병리학적 수준에서 가능해짐으로써 질병의 정확한 진단이 가능해지게 될 것이다. Acknowledgement: 본 연구는 2002 년도 한국과학재단 목적기초연구사업 (과제번호 : R0l-2002-000-00294-0 (2002)) 지원아래 수행되었다.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• Investigative Magnetic Resonance Imaging
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• v.24 no.3
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• pp.123-131
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• 2020

Purpose: Joint label fusion (JLF) is a popular multi-atlas-based segmentation algorithm, which compensates for dependent errors that may exist between atlases. However, in order to get good segmentation results, it is very important to set the several free parameters of the algorithm to optimal values. In this study, we first investigate the feasibility of a JLF algorithm for prostate segmentation in MR images, and then suggest the optimal set of parameters for the automatic prostate segmentation by validating the results of each parameter combination. Materials and Methods: We acquired T2-weighted prostate MR images from 20 normal heathy volunteers and did a series of cross validations for every set of parameters of JLF. In each case, the atlases were rigidly registered for the target image. Then, we calculated their voting weights for label fusion from each combination of JLF's parameters (r_pxy, r_pz, r_sxy, r_sz, β). We evaluated the segmentation performances by five validation metrics of the Prostate MR Image Segmentation challenge. Results: As the number of voxels participating in the voting weight calculation and the number of referenced atlases is increased, the overall segmentation performance is gradually improved. The JLF algorithm showed the best results for dice similarity coefficient, 0.8495 ± 0.0392; relative volume difference, 15.2353 ± 17.2350; absolute relative volume difference, 18.8710 ± 13.1546; 95% Hausdorff distance, 7.2366 ± 1.8502; and average boundary distance, 2.2107 ± 0.4972; in parameters of r_pxy = 10, r_pz = 1, r_sxy = 3, r_sz = 1, and β = 3. Conclusion: The evaluated results showed the feasibility of the JLF algorithm for automatic segmentation of prostate MRI. This empirical analysis of segmentation results by label fusion allows for the appropriate setting of parameters.

• Progress in Superconductivity and Cryogenics
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• v.15 no.1
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• pp.45-50
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• 2013

Closed-loop J-T (Joule-Thomson) refrigeration cycle is advantageous compared to common open loop $N_2$ decompression system in terms of nitrogen consumption. In this study, two closed-loop pure $N_2$ J-T refrigeration systems with sub-atmospheric device for cooling High Temperature Superconductor (HTS) power cable are investigated. J-T cooling systems include 2-stage compressor, 2-stage precooling cycle, J-T valve and a cold compressor or an auxiliary vacuum pump at the room temperature. The cold compressor and the vacuum pump are installed after the J-T valve to create sub-atmospheric condition. The temperature of 67 K is possible by lowering the pressure up to 24 kPa at the cold part. The optimized hydrocarbon mixed refrigerant (MR) J-T system is applied for precooling stage. The cold head of precooling MR J-T have the temperature from 120 K to 150 K. The various characteristics of cold compressor are invstigated and applied to design parameter of the cold compressor. The Carnot efficiency of cold compressor system is calculated as 16.7% and that of vacuum pump system as 16.4%. The efficiency difference between the cold compressor system and the vacuum pump system is due to difference of enthalpy change at cryogenic temperature, enthalpy change at room temperature and different work load at the pre-cooling cycle. The efficiency of neon-nitrogen MR J-T system is also presented for comparison with the sub-atmospheric devices. These systems have several pros and cons in comparison to typical MR J-T systems such as vacuum line maintainability, system's COP and etc. In this paper, the detailed design of the subcooled $N_2$ J-T systems are examined and some practical issues of the sub-atmospheric devices are discussed.

• Smart Structures and Systems
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• v.23 no.6
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• pp.537-551
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• 2019

In this paper, a method for analyzing the damping performance of stay cables incorporating magnetorheological (MR) dampers in the passive control mode is developed taking into account the cable sag and inclination, the damper coefficient, stiffness and mass, and the stiffness of damper support. Both numerical and asymptotic solutions are obtained from complex modal analysis. With the asymptotic solution, analytical formulas that evaluate the equivalent damping ratio of the sagged cable-damper system in consideration of all the above parameters are derived. The main thrust of the present study is to develop an general design formula and a universal curve for the optimal design of MR dampers for adjustable passive control of sagged cables. Two sag-affecting coefficients are derived to reflect the effects of cable sag on the maximum attainable damping ratio and the optimal damper coefficient. For the cable configurations commonly used in cable-stayed bridges, the sag-affecting coefficients are directly expressed in terms of the sag-extensibility parameter to facilitate the control design. A case study on adjustable passive vibration control of the longest cable (536 m) on Stonecutters Bridge is carried out to demonstrate the influence of the sag for the damper design, and to figure out the necessity of adjustability of damper coefficients for achieving maximum damping ratio for different vibration modes.

• Investigative Magnetic Resonance Imaging
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• v.26 no.2
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• pp.104-116
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• 2022

The purpose of this study is to systematically determine an optimal percentile cut-off in histogram analysis for calculating the mean parameters obtained from a non-Gaussian continuous-time random-walk (CTRW) diffusion model for differentiating individual glioma grades. This retrospective study included 90 patients with histopathologically proven gliomas (42 grade II, 19 grade III, and 29 grade IV). We performed diffusion-weighted imaging using 17 b-values (0-4000 s/mm²) at 3T, and analyzed the images with the CTRW model to produce an anomalous diffusion coefficient (D_m) along with temporal (𝛼) and spatial (𝛽) diffusion heterogeneity parameters. Given the tumor ROIs, we created a histogram of each parameter; computed the P-values (using a Student's t-test) for the statistical differences in the mean D_m, 𝛼, or 𝛽 for differentiating grade II vs. grade III gliomas and grade III vs. grade IV gliomas at different percentiles (1% to 100%); and selected the highest percentile with P < 0.05 as the optimal percentile. We used the mean parameter values calculated from the optimal percentile cut-offs to do a receiver operating characteristic (ROC) analysis based on individual parameters or their combinations. We compared the results with those obtained by averaging data over the entire region of interest (i.e., 100th percentile). We found the optimal percentiles for D_m, 𝛼, and 𝛽 to be 68%, 75%, and 100% for differentiating grade II vs. III and 58%, 19%, and 100% for differentiating grade III vs. IV gliomas, respectively. The optimal percentile cut-offs outperformed the entire-ROI-based analysis in sensitivity (0.761 vs. 0.690), specificity (0.578 vs. 0.526), accuracy (0.704 vs. 0.639), and AUC (0.671 vs. 0.599) for grade II vs. III differentiations and in sensitivity (0.789 vs. 0.578) and AUC (0.637 vs. 0.620) for grade III vs. IV differentiations, respectively. Percentile-based histogram analysis, coupled with the multi-parametric approach enabled by the CTRW diffusion model using high b-values, can improve glioma grading.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.73-73
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• 2003

목적： 피부조직과 같은 미세 인체구조 연구를 위해 고해상도 3T MRI 시스템에 적합한 고분해능의 RF surface coil을 개발하고 있다. In vivo 연구를 위한 여러 parameter를 최적화하여 기능영상에도 부합된다. 비침습적인 In vivo 검사에 의한 세포수준의 극 미세구조의 연구가 가능해짐으로써 과거 시행하던 침습적인 생검없이 각종질환의 진단적 접근이 병리학적 수준으로 향상되어 질병의 정확한 진단이 가능해지게 될 것이다.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.