• Journal of the Korean Society of Radiology
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• v.16 no.7
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• pp.835-842
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• 2022

This study is aimed to evaluate the effects of a synthetic MR technique in reducing metal artifacts. In the experiment, the in-plane and through-plane images were acquired by applying a synthetic MR technique and a high-speed spin echo technique to a phantom manufactured with screw for spinal surgery. The area of the metal artifact was compared. The metal artifacts were measured by dividing the signal-loss and the signal pile-up areas, and the area of the final artifact was calculated through the sum of the two. As a result, the metal artifacts were relatively reduced when the synthetic MR techniques were applied to both in-plane and through-plane. Comparing by sequence, the in-plane T1 images decreased by 23.45%, T2 images by 20.85%, PD images by 19.67%, and FLAIR images by 22.12%. Also, in the case of the through-plane, the T1 image decreased by 62.95%, the T2 image decreased by 73.93%, the PD image decreased by 74.68%, and the FLAIR image decreased by 66.43%. The cause of this result is that when the synthetic MR technique is applied, the distortion is due to the signal pile-up and does not occur and the size of the entire metal artifact is reduced. Therefore, synthetic MR technique can very effectively reduce metal artifacts, which can help to increase the diagnostic value of images.

• Journal of Radiation Protection and Research
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• v.44 no.4
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• pp.149-155
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• 2019

Background: Magnetic resonance (MR) image guided radiation therapy system, enables real time MR guided radiotherapy (RT) without additional radiation exposure to patients during treatment. However, MR image lacks electron density information required for dose calculation. Image fusion algorithm with deformable registration between MR and computed tomography (CT) was developed to solve this issue. However, delivered dose may be different due to volumetric changes during image registration process. In this respect, synthetic CT generated from the MR image would provide more accurate information required for the real time RT. Materials and Methods: We analyzed 1,209 MR images from 16 patients who underwent MR guided RT. Structures were divided into five tissue types, air, lung, fat, soft tissue and bone, according to the Hounsfield unit of deformed CT. Using the deep learning model (U-NET model), synthetic CT images were generated from the MR images acquired during RT. This synthetic CT images were compared to deformed CT generated using the deformable registration. Pixel-to-pixel match was conducted to compare the synthetic and deformed CT images. Results and Discussion: In two test image sets, average pixel match rate per section was more than 70% (67.9 to 80.3% and 60.1 to 79%; synthetic CT pixel/deformed planning CT pixel) and the average pixel match rate in the entire patient image set was 69.8%. Conclusion: The synthetic CT generated from the MR images were comparable to deformed CT, suggesting possible use for real time RT. Deep learning model may further improve match rate of synthetic CT with larger MR imaging data.

• Journal of Magnetics
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• v.7 no.2
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• pp.55-58
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• 2002

MR transfer behaviors of the permanent magnet biased spin valve MR sensors with SyAF (synthetic antiferromagnet) layers were studied by micromagnetics modeling. For narrow track MR heads, various height to width ratios were considered together with strength of permanent magnets which stabilities the free layed As the MR sensor width is reduced to $0.12 \mu{m}$, sensor height less than 0.09 ${\mu}{\textrm}{m}$ is needed to show good linearity and the Mr.t of permanent magnets smaller than 0.2 memu/$cm^2$ is sufficient for the domain stabilization. The conditions for single domain behavior of the free layer were also investigated through optimizing the biasing strength of permanent magneto the shield gap and the aspect ratio of MR sensor.

• Progress in Medical Physics
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• v.32 no.4
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• pp.172-178
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• 2021

Purpose: This study aimed to develop a deep learning architecture combining two task models to generate synthetic computed tomography (sCT) images from low-tesla magnetic resonance (MR) images to improve metallic marker visibility. Methods: Twenty-three patients with cervical cancer treated with intracavitary radiotherapy (ICR) were retrospectively enrolled, and images were acquired using both a computed tomography (CT) scanner and a low-tesla MR machine. The CT images were aligned to the corresponding MR images using a deformable registration, and the metallic dummy source markers were delineated using threshold-based segmentation followed by manual modification. The deformed CT (dCT), MR, and segmentation mask pairs were used for training and testing. The sCT generation model has a cascaded three-dimensional (3D) U-Net-based architecture that converts MR images to CT images and segments the metallic marker. The performance of the model was evaluated with intensity-based comparison metrics. Results: The proposed model with segmentation loss outperformed the 3D U-Net in terms of errors between the sCT and dCT. The structural similarity score difference was not significant. Conclusions: Our study shows the two-task-based deep learning models for generating the sCT images using low-tesla MR images for 3D ICR. This approach will be useful to the MR-only workflow in high-dose-rate brachytherapy.

• Journal of Korean Neurosurgical Society
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• v.63 no.3
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• pp.386-396
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• 2020

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion : This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

• BMB Reports
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• v.28 no.1
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• pp.83-86
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• 1995

In the course of screening for new aminopeptidase M inhibitors which were expected to be analgesic, immunopotentiating, or anti-metastatic agents, the novel synthetic substance MR-387C[(2S,3R)-3-amino-2-hydroxy-4-phenylbutanoyl-L-valyl-L-prolyl-L-leucine] (M.W. 504 daltons) was obtained. It was competitive with the substrate and had an $IC_{50}$ value of $0.04\;{\mu}m/ml$ ($7.9{\times}10^{-8}\;M$) and an inhibition constant ($K_i$) of $3.8{\times}10^{-8}\;M$. This novel MR-387C was compared with various known inhibitors of aminopeptidase M. It inhibited the enzyme more strongly than any other microorganism-originated inhibitor, except probestin.

• Progress in Medical Physics
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• v.33 no.4
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• pp.142-149
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• 2022

Purpose: This study seeks to compare the dosimetric parameters of the bulk electron density (ED) approach and synthetic computed tomography (CT) image in terms of position variation of the air cavity in magnetic resonance-guided radiotherapy (MRgRT) for patients with pancreatic cancer. Methods: This study included nine patients that previously received MRgRT and their simulation CT and magnetic resonance (MR) images were collected. Air cavities were manually delineated on simulation CT and MR images in the treatment planning system for each patient. The synthetic CT images were generated using the deep learning model trained in a prior study. Two more plans with identical beam parameters were recalculated with ED maps that were either manually overridden by the cavities or derived from the synthetic CT. Dose calculation accuracy was explored in terms of dose-volume histogram parameters and gamma analysis. Results: The D_95% averages were 48.80 Gy, 48.50 Gy, and 48.23 Gy for the original, manually assigned, and synthetic CT-based dose distributions, respectively. The greatest deviation was observed for one patient, whose D_95% to synthetic CT was 1.84 Gy higher than the original plan. Conclusions: The variation of the air cavity position in the gastrointestinal area affects the treatment dose calculation. Synthetic CT-based ED modification would be a significant option for shortening the time-consuming process and improving MRgRT treatment accuracy.

• Journal of Magnetics
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• v.6 no.4
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• pp.132-141
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• 2001

The switching characteristics and the magnetization-flop behavior in magnetic tunnel junctions exchange biased by synthetic antiferromagnets (SyAFs) are investigated by using a computer simulations based on a single-domain multilayer model. The bias field acting on the free layer is found to be sensitive to the thickness of neighboring layers, and the thickness dependence of the bias field is greater at smaller cell dimensions due to larger magnetostatic interactions. The resistance to magnetization flop increases with decreasing cell size due to increased shape anisotropy. When the cell dimensions are small and the synthetic antiferromagnet is weakly, or not pinned, the magnetization directions of the two layers sandwiching the insulating layer are aligned antiparallel due to a strong magnetostatic interaction, resulting in an abnormal magneto resistance (MR) change from the high-MR state to zero, irrespective of the direction of the free-layer switching. The threshold field for magnetization-flop is found to increase linearly with increasing antiferromagnetic exchange coupling in the synthetic antiferromagnet. Irrespective of the magnetic parameters and cell sizes, magnetization flop does not exist near zero applied field, indicating that magnetization flop is driven by the Zeeman energy.

• Journal of the Korean Magnetics Society
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• v.10 no.5
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• pp.196-202
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• 2000

Top synthetic spin valves with structure Ta/NiFe/CoFe/Cu/CoFe(P1)/Ru/CoFe(P2)/FeMn/Ta on Si(100) substrate with natural oxide were prepared by dc magnetron sputtering system, and investigated on the magnetoresistance properties and effective exchange bias field. As the thickness of FeMn increased above 150 $\AA$, MR ratio was decreased due to the current shunting effect. As the thickness of free layer decreased below 40$\AA$, MR ratio was reduced rapidly. In case of 40 $\AA$ thick of free layer, spin valve film with a structure Si(100)/Ta(50 $\AA$)/NiFe(27 $\AA$)/CoFe(13 $\AA$)/Cu(26 $\AA$)/CoFe(30 $\AA$)/Ru(7 $\AA$)/CoFe(15 $\AA$)/FeMn(100 $\AA$)/Ta(50 $\AA$) exhibited maximum MR ratio of 7.5 % and an effective exchange bias field of 600 Oe, respectively. Thickness difference dependence in this synthetic spin valve structure on effective exchange field was investigated and interpreted by the analytical method. It should be noted that thickness increase of CoFe(P 1) and decrease of CoFe(P2) in synthetic antiferromagnet leaded to the decrease in effective exchange bias field by experimentally and analytically.

• Journal of the Korea Society for Simulation
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• v.31 no.2
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• pp.21-32
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• 2022

The ROK Navy is seeking to secure an aircraft carrier(CVX) to take responsibility for the maritime security of the Republic of Korea. In order for the CVX to complete the mission given to it, the crew must be able to operate the CVX perfectly, and for this purpose, the operating skills of the CVX crew result from constant training. Therefore, this paper proposes an On-board Training System(OBTS) so that the best training can always be performed even on ship. CVX OBTS should be built in the form of a thorough simulator based on a Synthetic Training Environment(STE) so that it can be optimally applied to ship and provide the best training environment to the crew. In order to satisfy the various training requirements and implementation conditions of the CVX, this paper proposes a plan to consist of Embedded Training System(ETS), VR training system, AR maintenance system, MR training system, MR metaverse training system, and realistic simulator training system.