• 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.

• 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.

• 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.

• 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.

• 한국결정성장학회지
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• 제30권1호
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• pp.21-26
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• 2020

이 논문은 라이트박스의 주력 상품인 세 가지 색의 합성 다이아몬드의 분광학적 특성에 대해 분석한 결과이다. 드비어스의 브랜드인 라이트박스 주얼리는 2018년 9월부터 CVD 합성 다이아몬드를 판매하기 시작했다. 본 연구에서 검사된 샘플은 0.25 ct, 0.25 ct, 0.26 ct의 무색(H grade), 핑크, 청색의 합성 다이아몬드이며, 각 샘플의 테이블 중앙에는 라이트박스 로고가 레이저 각인되어 있었다. 분광학적 기법에 기초한 분석 결과, 무색 샘플은 결정성장 후 색 향상을 위한 고온고압 처리과정을 거치지 않았다. 핑크 샘플은 H3, 3H, 594 nm, NV, GR1의 광학 결함이 발견되었고, 이로 인해 결정성장 후 색 향상을 위해 조사와 열처리 과정을 거친 것으로 판단했다. 또한 블루 샘플은 열처리 과정 없이 조사만 되었음을 알 수 있었다.

• 한국임상수의학회지
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• 제30권4호
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• pp.268-272
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• 2013

The purpose of this study was to assess the effects of reconstruction kernel, and slice thickness on the accuracy of spiral CT-based volume assessment over a range of object sizes typical of synthetic simulated tumor. Spiral CT scanning was performed at various reconstruction kernels (soft tissue, standard, bone), and slice thickness (1, 2, 3 mm) using a phantom made of gelatin and 10 synthetic simulated tumors of different sizes (diameter 3.0-12.0 mm). Three-dimensional volume assessments were obtained using an automated software tool. Results were compared with the reference volume by calculating the percentage error. Statistical analysis was performed using ANOVA and setting statistical significance at P < 0.05. In general, smaller slice thickness and larger sphere diameters produced more accurate volume assessment than larger slice thickness and smaller sphere diameter. The measured volumes were larger than the actual volumes by a common factor depending on slice thickness; in 100HU simulated tumors that had statistically significant, 1 mm slice thickness produced on average 27.41%, 2 mm slice thickness produced 45.61%, 3 mm slice thickness produced 93.36% overestimates of volume. However, there was no statistically significant difference in volume error for spiral CT scans taken with techniques where only reconstruction kernel was changed. These results supported that synthetic simulated tumor size, slice thickness were significant parameters in determining volume measurement errors. For an accurate volumetric measurement of an object, it is critical to select an appropriate slice thickness and to consider the size of an object.

• 한국결정성장학회지
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• 제27권4호
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• pp.149-153
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• 2017

최근 많은 양의 보석용 무색 합성 멜리사이즈 다이아몬드가 주얼리 시장에 유입되고 있으며, 이로 인해 세계 곳곳에서 다이아몬드와 관련된 속임 건수가 보고되고 있다. 예를 들면, 의도적으로 합성 다이아몬드를 천연 다이아몬드라고 판매하거나 또는 의도적으로 천연 다이아몬드에 합성 다이아몬드를 섞어 판매하는 경우이다. 결론적으로, 천연 멜리사이즈 다이아몬드와 합성 멜리사이즈 다이아몬드의 구별이 그 어느 때보다도 더욱 중요한 상황이다. 현재 중국에서는 합성 다이아몬드를 생산할 수 있는 큐빅 프레스가 10,000기가 넘는다. 이 중 1,000기가 무색의 보석용으로 생산되고 있으며, 1기당 24시간을 기준으로 10캐럿의 합성 멜리 다이아몬드를 생산할 수 있다. 합성 다이아몬드는 때때로 핀포인트나 금속성 플럭스를 함유하는 특징을 가지지만, 감별을 위해서는 전문 감정원의 첨단 장비들이 요구된다. 소비자의 신뢰를 확보하기 위해서는 천연 다이아몬드로부터 모든 합성 다이아몬드는 구분되어야 한다.

• Radiation Oncology Journal
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• 제38권1호
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• pp.52-59
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• 2020

Purpose: To evaluate and compare the feasibilities of magnetic resonance (MR) image-based planning using synthetic computed tomography (sCT) versus CT (pCT)-based planning in helical tomotherapy for prostate cancer. Materials and Methods: A retrospective evaluation was performed in 16 patients with prostate cancer who had been treated with helical tomotherapy. MR images were acquired using a dedicated therapy sequence; sCT images were generated using magnetic resonance for calculating attenuation (MRCAT). The three-dimensional dose distribution according to sCT was recalculated using a previously optimized plan and was compared with the doses calculated using pCT. Results: The mean planning target volume doses calculated by sCT and pCT differed by 0.65% ± 1.11% (p = 0.03). Three-dimensional gamma analysis at a 2%/2 mm dose difference/distance to agreement yielded a pass rate of 0.976 (range, 0.658 to 0.986). Conclusion: The dose distribution results obtained using tomotherapy from MR-only simulations were in good agreement with the dose distribution results from simulation CT, with mean dose differences of less than 1% for target volume and normal organs in patients with prostate cancer.

• Journal of Korean Neurosurgical Society
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• 제61권6호
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• pp.737-746
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• 2018

Objective : The purpose of this study was to compare the cosmetic outcome and complications after cranioplasty (CP) due to three different implant materials, and analyze the mean implant survival and cumulative survival rate based on these results. Methods : We reviewed 108 patients retrospectively who underwent CP between January 2014 and November 2016. Autologous bone (AB; 45 patients) and synthetic materials with porous polyethylene (PP; 32 patients) and custom-made 3-dimensional printed titanium mesh (CT; 31 patients) were used as implants. Results : Regardless of implanted materials, more than 89.8% of the CP patients were satisfied with the cosmetic outcome. No statistically significant difference was observed among the three groups. The overall postoperative complication rates of each group were 31.1% in the AB group, 15.6% in the PP group and 3.2% in the CT group. The CT group showed lower complication rates compared with AB and PP groups (${\chi}^2$-test : AB vs. PP, p=0.34; AB vs. CT, p=0.00; PP vs. CT, p=0.03). The AB and PP groups demonstrated a higher post-CP infection rate (11.1% and 6.3%) than the CT group (3.2%). However, no significant difference in the incidence of post-CP infection was observed among the three groups. The PP and CT groups demonstrated a higher mean implant survival time and cumulative survival rate than the AB group at the last follow-up (p<0.05). Conclusion : In comparison with AB and PP, cranioplasty with CT shows benefits in terms of lower post-CP complication, less intraoperative bleeding loss, shorter operation time and in-hospital stay. The PP and CT groups showed higher implant survival time and cumulative survival rate compared with the AB group.

• 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.