• 대한방사선협회지
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• 제25권1호
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• pp.18-18
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• 1999

We have studied DR of medical science at SNUH since early 90's, founded the basis of Picture Archiving Communication System(PACS) used for actual clinic parts, has acquired images gradually since January H and came to acquire images in all examinations ex

• Korean Journal of Radiology
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• 제20권4호
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• pp.569-579
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• 2019

Objective: To investigate the usefulness of computed tomography (CT) texture analysis (CTTA) in estimating histologic tumor grade and in predicting disease-free survival (DFS) after surgical resection in patients with hepatocellular carcinoma (HCC). Materials and Methods: Eighty-one patients with a single HCC who had undergone quadriphasic liver CT followed by surgical resection were enrolled. Texture analysis of tumors on preoperative CT images was performed using commercially available software. The mean, mean of positive pixels (MPP), entropy, kurtosis, skewness, and standard deviation (SD) of the pixel distribution histogram were derived with and without filtration. The texture features were then compared between groups classified according to histologic grade. Kaplan-Meier and Cox proportional hazards analyses were performed to determine the relationship between texture features and DFS. Results: SD and MPP quantified from fine to coarse textures on arterial-phase CT images showed significant positive associations with the histologic grade of HCC (p < 0.05). Kaplan-Meier analysis identified most CT texture features across the different filters from fine to coarse texture scales as significant univariate markers of DFS. Cox proportional hazards analysis identified skewness on arterial-phase images (fine texture scale, spatial scaling factor [SSF] 2.0, p < 0.001; medium texture scale, SSF 3.0, p < 0.001), tumor size (p = 0.001), microscopic vascular invasion (p = 0.034), rim arterial enhancement (p = 0.024), and peritumoral parenchymal enhancement (p = 0.010) as independent predictors of DFS. Conclusion: CTTA was demonstrated to provide texture features significantly correlated with higher tumor grade as well as predictive markers of DFS after surgical resection of HCCs in addition to other valuable imaging and clinico-pathologic parameters.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.285-286
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• 1998

Virtual Brain-endoscopy is an effective method to detect lesion in brain. Brain is the most part of the human and is not easy part to operate so that reconstructing in 3D may be very helpful to doctors. In this paper, it is suggested that to increase the reliability, method of matching 3D object with the 2D CT slice. 3D Brain-endoscopy is reconstructed with 35 slices of 2D CT images. There is a plate in 3D brain-endoscopy so as to drag upward or downward to match the relevant 2D CT image. Relevant CT image guides the user to recognize the exact part he or she is investigating. VRML Script is used to make the change in images and PlaneSensor node is used to transmit the y coordinate value with the CT image. The result is test on the PC which has the following spec. 400MHz Clock-speed, 512MB ram, and FireGL 3000 3D accelerator is set up. The VRML file size is 3.83MB. There was no delay in controlling the 3D world and no collision in changing the CT images. This brain-endoscopy can be also put to practical use on medical education through internet.

• Korean Journal of Radiology
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• 제22권3호
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• pp.476-488
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• 2021

Objective: We aimed to develop a deep neural network for segmenting lung parenchyma with extensive pathological conditions on non-contrast chest computed tomography (CT) images. Materials and Methods: Thin-section non-contrast chest CT images from 203 patients (115 males, 88 females; age range, 31-89 years) between January 2017 and May 2017 were included in the study, of which 150 cases had extensive lung parenchymal disease involving more than 40% of the parenchymal area. Parenchymal diseases included interstitial lung disease (ILD), emphysema, nontuberculous mycobacterial lung disease, tuberculous destroyed lung, pneumonia, lung cancer, and other diseases. Five experienced radiologists manually drew the margin of the lungs, slice by slice, on CT images. The dataset used to develop the network consisted of 157 cases for training, 20 cases for development, and 26 cases for internal validation. Two-dimensional (2D) U-Net and three-dimensional (3D) U-Net models were used for the task. The network was trained to segment the lung parenchyma as a whole and segment the right and left lung separately. The University Hospitals of Geneva ILD dataset, which contained high-resolution CT images of ILD, was used for external validation. Results: The Dice similarity coefficients for internal validation were 99.6 ± 0.3% (2D U-Net whole lung model), 99.5 ± 0.3% (2D U-Net separate lung model), 99.4 ± 0.5% (3D U-Net whole lung model), and 99.4 ± 0.5% (3D U-Net separate lung model). The Dice similarity coefficients for the external validation dataset were 98.4 ± 1.0% (2D U-Net whole lung model) and 98.4 ± 1.0% (2D U-Net separate lung model). In 31 cases, where the extent of ILD was larger than 75% of the lung parenchymal area, the Dice similarity coefficients were 97.9 ± 1.3% (2D U-Net whole lung model) and 98.0 ± 1.2% (2D U-Net separate lung model). Conclusion: The deep neural network achieved excellent performance in automatically delineating the boundaries of lung parenchyma with extensive pathological conditions on non-contrast chest CT images.

• Korean Journal of Radiology
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• 제24권4호
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• pp.294-304
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• 2023

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.

• 대한방사선협회지
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• 제27권2호
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• pp.95-103
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• 2001

Ⅰ. Purpose : The purpose of this study was to evaluate optimized images of Turbo Spin Echo(TSE) imaging technique in Brain MRI compared with Spin Echo(SE) technique. Ⅱ. Materials and Methods : A retrospective comparison between SE and TSE sequences was pe

• 대한원격탐사학회지
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• 제38권6_4호
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• pp.1901-1910
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• 2022

뉴스페이스(new space) 시대가 도래함에 따라 국내 KOMPSAT-3·3A 위성영상과 해외 위성영상과의 글로벌 융합활용 기술확보가 대두되고 있다. 일반적으로 다중센서 위성영상은 취득 당시의 다양한 외부요소로 인해 영상 간 상대적인 기하오차(relative geometric error)가 발생하며, 이로 인해 위성영상 산출물의 품질이 저하된다. 따라서 본 연구에서는 KOMPSAT-3·3A 위성영상과 해외 위성영상 간 존재하는 상대기하오차를 최소화하기 위한 정밀영상정합(fine-image registration) 방법론을 제안한다. KOMPSAT-3·3A 위성영상과 해외 위성영상 간 중첩영역을 선정한 후 두 영상 간 공간해상도를 통일한다. 이어서, 특징 및 영역 기반 정합기법을 결합한 형태의 하이브리드(hybrid) 정합기법을 이용하여 정합점(tie-point)을 추출한다. 그리고 피라미드(pyramid) 영상 기반의 반복적 정합을 수행하여 정밀영상정합을 수행한다. KOMPSAT-3·3A 위성영상과 Sentinel-2A 및 PlanetScope 영상을 이용하여 제안기법의 정확도 및 성능을 평가하였다. 그 결과, Sentienl-2A 영상 기준 평균 Root Mean Square Error (RMSE) 1.2 pixels, PlanetScope 영상 기준 평균 RMSE 3.59 pixels의 정확도가 도출되었다. 이를 통해 제안기법을 이용하여 효과적으로 정밀영상정합을 수행할 수 있을 것으로 사료된다.

• Journal of Periodontal and Implant Science
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• 제47권1호
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• pp.41-50
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• 2017

Purpose: The aims of the present study were to compare the image quality and visibility of tooth cracks between conventional methods and swept-source optical coherence tomography (SS-OCT) and to develop an automatic detection technique for tooth cracks by SS-OCT imaging. Methods: We evaluated SS-OCT with a near-infrared wavelength centered at 1,310 nm over a spectral bandwidth of 100 nm at a rate of 50 kHz as a new diagnostic tool for the detection of tooth cracks. The reliability of the SS-OCT images was verified by comparing the crack lines with those detected using conventional methods. After performing preprocessing of the obtained SS-OCT images to emphasize cracks, an algorithm was developed and verified to detect tooth cracks automatically. Results: The detection capability of SS-OCT was superior or comparable to that of trans-illumination, which did not discriminate among the cracks according to depth. Other conventional methods for the detection of tooth cracks did not sense initial cracks with a width of less than $100{\mu}m$. However, SS-OCT detected cracks of all sizes, ranging from craze lines to split teeth, and the crack lines were automatically detected in images using the Hough transform. Conclusions: We were able to distinguish structural cracks, craze lines, and split lines in tooth cracks using SS-OCT images, and to automatically detect the position of various cracks in the OCT images. Therefore, the detection capability of SS-OCT images provides a useful diagnostic tool for cracked tooth syndrome.

• 핵의학기술
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• 제21권1호
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• pp.54-59
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• 2017

폐암(Lung cancer) 환자의 경우 PET/CT 검사에서 호흡으로 인하여 영상의 정합오차가 발생하게 되는데 이로 인해 정확한 SUV 와 Tumor volume측정을 방해하는 요인으로 작용된다. $SUV_{max}$를 이용하여 폐암 환자의 수술 후 예측 및 항암화학요법의 효과를 평가하고 있으며, 방사선치료의 예후 예측 및 평가를 위해 현재 Tumor volume과 SUV를 이용한 지표가 사용되고 있다. 그렇기 때문에 정합오차를 줄이기 위해 본원에서는 Respiratory gating method를 적용하여 검사를 시행하고 있다. 본 연구는 Step and Go 방식이 아닌 Flow mode를 적용하여 Non-gating 영상과 첫 번째 Respiratory Gating영상, 그리고 추가로 부분 Respiratory gating 촬영하여 Respiratory gating method의 유용성에대해 알아보았다. 2016년 6월부터 2016년 9월까지 분당서울대학교병원에서 PET/CT 검사를 한 폐암 환자 20명(남:12명, 여:8명)을 대상으로 amplitude rang 15% 미만인 호흡이 안정한 환자군 10명 15%초과한 호흡이 불안정한 환자군 10명으로 나누어 비교분석하였다. 전체 환자에서 Non-gating 영상의 $SUV_{max}$는 $9.43{\pm}3.93$, $SUV_{mean}$은 $1.77{\pm}0.89$, Tumor Volume은 $4.17{\pm}2.41$로 측정되었고 기존 Gating 영상에서 $SUV_{max}$는 $10.08{\pm}4.07$, $SUV_{mean}$은 $1.75{\pm}0.81$, Tumor Volume은 $3.56{\pm}2.11$로 측정되었다. 그리고 추가 Lung gating 영상에서 $SUV_{max}$는 $10.86{\pm}4.36$, $SUV_{mean}$은 $1.77{\pm}0.85$, Tumor volume은 $3.36{\pm}1.98$을 얻었다. Non-gating 영상과 기존 Gating 영상, 그리고 기존 Gating 영상과 추가 Lung gating 영상을 비교했을 때 둘 다 $SUV_{mean}$ 값에서 통계적으로 유의한 차이를 보이지 않았으나(P>0.05) $SUV_{max}$와 Tumor volume에서 유의한 차이를 보였다(P<0.05). 그중 호흡이 안정한 환자군보다 호흡이 불안정한 환자군에서의 증감률이 더 크게 나타났다. Amplitude range 폭은 전체 20명 중 12명(Signal이 안정된 환자 3명 불안정한 환자 9명)이 추가 Lung gating을 했을 때 기존 Gating 영상보다 더 낮게 나타났다. 본 연구에 의하면 Flow mode를 적용하여 Respiration Gating Method로 촬영한 결과 추가적인 CT 촬영 없이 호흡으로 인해 발생하는 병변의 움직임을 보정해 주어 $SUV_{max}$, Tumor volume을 Non-gating 영상보다 더 정확하게 측정할 수 있었다. 그리고 처음 Gating 할 때보다 추가 촬영 시 호흡의 안정에 따른 Amplitude range 폭의 낮아짐을 알 수 있었다. 따라서 Gating 영상이 Non-gating 영상보다 진단에 더 유용한 정보를 제공함을 알 수 있었고, Signal이 불규칙적인 환자에게 시간적 여유가 있다면 추가로 부분 촬영을 하는 것이 도움이 될 것이라고 사료된다.

• Investigative Magnetic Resonance Imaging
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• 제19권2호
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• pp.107-113
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• 2015

Purpose: Susceptibility weighted imaging (SWI) is a new magnetic resonance technique that can exploit the magnetic susceptibility differences of various tissues. Intracranial hemorrhage (ICH) looks a dark blooming on the magnitude images of SWI. However, the pattern of ICH on phase images is not well known. The purpose of this study is to characterize hemorrhagic lesions on the phase images of SWI. Materials and Methods: We retrospectively enrolled patients with ICH, who underwent both SWI and precontrast CT, between 2012 and 2013 (n = 95). An SWI was taken, using the 3-tesla system. A phase map was generated after postprocessing. Cases with an intracranial hemorrhage were reviewed by an experienced neuroradiologist and a trainee radiologist, with 10 years and 3 years of experience, respectively. The types and stages of the hemorrhages were determined in correlation with the precontrast CT, the T1- and T2-weighted images, and the FLAIR images. The size of the hemorrhage was measured by a one- directional axis on a magnitude image of SWI. The phase values of the ICH were qualitatively evaluated: hypo-, iso-, and hyper-intensity. We summarized the imaging features of the intracranial hemorrhage on the phase map of the SWI. Results: Four types of hemorrhage are observed: subdural and epidural; subarachnoid; parenchymal hemorrhage; and microbleed. The stages of the ICH were classified into 4 groups: acute (n = 34); early subacute (n = 11); late subacute (n = 15); chronic (n = 8); stage-unknown microbleeds (n = 27). The acute and early subacute hemorrhage showed heterogeneous mixed hyper-, iso-, and hypo-signal intensity; the late subacute hemorrhage showed homogeneous hyper-intensity, and the chronic hemorrhage showed a shrunken iso-signal intensity with the hyper-signal rim. All acute subarachnoid hemorrhages showed a homogeneous hyper-signal intensity. All parenchymal hemorrhages (> 3 mm) showed a dipole artifact on the phase images; however, microbleeds of less than 3 mm showed no dipole artifact. Larger hematomas showed a heterogeneous mixture of hyper-, iso-, and hypo-signal intensities. Conclusion: The pattern of the phase value of the SWI showed difference, according to the type, stage, and size.