• 한국의학물리학회지:의학물리
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• 제32권4호
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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.

• 한국의학물리학회지:의학물리
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• 제33권4호
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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 Radiation Protection and Research
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• 제44권4호
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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 Korean Neurosurgical Society
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• 제63권3호
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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.

• KIEE International Transactions on Electrophysics and Applications
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• 제4C권3호
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• pp.123-128
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• 2004

In electrical impedance tomography (EIT), the internal resistivity distribution of the unknown object is computed using the boundary voltage data induced by different current patterns using various reconstruction algorithms. This paper presents a new image reconstruction algorithm based on the genetic algorithm (GA) via a two-step approach for the solution of the EIT inverse problem, in particular for the reconstruction of "static" images. The computer simulation for the 32 channels synthetic data shows that the spatial resolution of reconstructed images in the proposed scheme is improved compared to that of the modified Newton-Raphson algorithm at the expense of an increased computational burden.rden.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.541-545
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• 1997

This paper presents a modified geometric active contour model or edge detection and segmentation of computed tomography(CT) scan images. The method is based on the level setup approach developed by Osher and Sethian and the modeling of propagation fronts with curvature dependent speeds by Malladi. Based on above algorithms, the geometric active contour is obtained through a particular level set of hypersurface lowing along its gradient force and curvature force. This technique retains the attractive feature which is topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. But there are limitations in this algorithm which are being not able to separate the object with weak difference from neighbor object. So we use speed limitation filter to overcome those problems. We apply a 2D model to various synthetic cases and the three cases of real CT scan images in order to segment objects with complicated shapes and topologies. From the results, the presented model confirms that it attracts very naturally and efficiently to the desired feature of CT scan images.

• Archives of Plastic Surgery
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• 제46권5호
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• pp.470-474
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• 2019

Congenital absent sternum is a rare birth defect that requires early intervention for optimal long-term outcomes. Descriptions of the repair of absent sternum are limited to case reports, and no preferred method for management has been described. Herein, we describe the use of porcine acellular dermal matrix to reconstruct the sternum of an infant with sternal infection following attempted repair using synthetic mesh. The patient was a full-term male with trisomy 21, agenesis of corpus callosum, ventricular septal defect, patent ductus arteriosus, right-sided aortic arch, and congenital absence of sternum with no sternal bars. Following removal of the infected synthetic mesh, negative pressure wound therapy with instillation was used to manage the open wound and provide direct antibiotic therapy. When blood C-reactive protein levels declined to ${\leq}2mg/L$, the sternum was reconstructed using porcine acellular dermal matrix. At 21 months postoperative, the patient demonstrated no respiratory issues. Physical examination and computed tomography imaging identified good approximation of the clavicular heads and sternal cleft and forward curvature of the ribs. This case illustrates the benefits of negative pressure wound therapy and acellular dermal matrix for the reconstruction of absent sternum in the context of infected sternal surgical site previously repaired with synthetic mesh.

• Journal of Periodontal and Implant Science
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• 제47권2호
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• pp.86-95
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• 2017

Purpose: The aim of this study was to determine the effect of recombinant human bone morphogenetic protein-2 (rhBMP-2)-loaded synthetic bone substitute on implants that were simultaneously placed with sinus augmentation in rabbits. Methods: In this study, a circular access window was prepared in the maxillary sinus of rabbits (n=5) for a bone graft around an implant (${\varnothing}3{\times}6mm$) that was simultaneously placed anterior to the window. Synthetic bone substitute loaded with rhBMP-2 was placed on one side of the sinus to form the experimental group, and saline-soaked synthetic bone substitute was placed on the other side of the sinus to form the control group. After 4 weeks, sections were obtained for analysis by micro-computed tomography and histology. Results: Volumetric analysis showed that the median amount of newly formed bone was significantly greater in the BMP group than in the control group ($51.6mm^3$ and $46.6mm^3$, respectively; P=0.019). In the histometric analysis, the osseointegration height was also significantly greater in the BMP group at the medial surface of the implant (5.2 mm and 4.3 mm, respectively; P=0.037). Conclusions: In conclusion, an implant simultaneously placed with sinus augmentation using rhBMP-2-loaded synthetic bone substitute can be successfully osseointegrated, even when only a limited bone height is available during the early stage of healing.

• 대한전자공학회논문지
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• 제15권6호
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• pp.8-22
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• 1978

근래에 발전하고 있는 Artificial Intelligence 또는 Synthetic Image 등 넓은 의미에서의 영상처리에 관하여 해석학적인 설명을 시도하였다. 일반적으로 얻어지는 "영상" 또는 "사진"에 반하여 간접적으로 얻어진 Synthetic Image의 대표적인 예로서 3차원 영상 재 구성 (3-Dimensional Image Reconstruction)을 들 수 있으며, 이의 최근 의학 및 생명 과학 분야는 물론 공학 및 물리학 분야의 비파괴 검사(NDT)등 많은 분야에의 응용에 급격한 발전을 보고 있다. 본 논문은 3차원 CT (Computerized Topography)의 기본을 이루는 3차원 영상 재구성 처리에 관한 기본적인 문제를 two-dimensional signal processing의 관점에서 다루었다.

• The Journal of Advanced Prosthodontics
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• 제10권3호
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• pp.167-176
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• 2018

PURPOSE. The purpose of this study was to compare the new bone formation capability of zirconia with those of other synthetic bone grafts. MATERIALS AND METHODS. Twelve rabbits were used and four 6-mm diameter transcortical defects were formed on each calvaria. Each defect was filled with Osteon II (Os), Tigran PTG (Ti), and zirconia (Zi) bone grafts. For the control group, the defects were left unfilled. The rabbits were sacrificed at 2, 4, and 8 weeks. Specimens were analyzed through micro computed tomography (CT) and histomorphometric analysis. RESULTS. The Ti and Zi groups showed significant differences in the amount of newly formed bone between 2 and 4 weeks and between 2 and 8 weeks (P<.05). The measurements of total bone using micro CT showed significant differences between the Os and Ti groups and between the Os and Zi groups at 2 and 8 weeks (P<.05). Comparing by week in each group, the Ti group showed a significant difference between 4 and 8 weeks. Histomorphometric analysis also showed significant differences in new bone formation between the control group and the experimental groups at 2, 4, and 8 weeks (P<.05). In the comparison of newly formed bone, significant differences were observed between 2 and 4 weeks and between 2 and 8 weeks (P<.05) in all groups. CONCLUSION. Zirconia bone graft material showed satisfactory results in new bone formation and zirconia could be used as a new synthetic bone graft material.