• International Journal of Vascular Biomedical Engineering
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• 제4권1호
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• pp.27-30
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• 2006

Background; Pleural micro-metastasis of lung cancer is detected by touch print cytology or pleural lavage cytology, but its prognostic impact has not elucidated yet. We hypothesize that recurrence may depend on the amount of tumor cells disseminated in pleural cavity, if the invasiveness of all cancer is the same. To predict the amount of tumor cells disseminated in pleural cavity, we need pleural surface area, distributed pattern of cells and concentration of cells per unit area. Human pleural surface area has not reported yet. In this report, we calculate the normal human pleural surface area using CT image data processing. Methods; Twenty persons were checked CT scan, and we obtained the data from each image. In order to calculate the pleural surface, the outline of lung was firstly extruded from CT image data using home-made Digitizer program. And the distance between CT images was calculated from the extruded outline. Finally a normal human pleural surface was calculated from function between the distance of consecutive CT images and the calculated length. Results; Their mean age is $65{\pm}12$ years old (range $26{\sim}77$), body weight is $62{\pm}9\;kg\;(48{\sim}80)$, and height is $167{\pm}6\;cm\;(156{\sim}176)$. The number of images used is $36{\pm}7\;(24{\sim}51)$. Pleural surface area is $211,888{\pm}35,756\;mm^2\;(143,880{\sim}279,576)$. Right-side pleural surface area is $107,932\;mm^2$ and Lt is $103,955\;mm^2$. Costal, mediastinal and diaphragmatic surfaces of right-side pleura are $77,483\;mm^2,\;39,057\;mm^2,\;and\;8,608\;mm^2$ respectively, and left-side are $72,497\;mm^2,\;35,578\;mm^2,\;and\;4,120\;mm^2$ respectively. Conclusion; Normal human pleural surface area is calculated using CT image data at first and the result is about $0.212\;m^2$.

• 한국지반공학회논문집
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• 제27권11호
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• pp.93-101
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• 2011

X-ray micro-CT를 이용한 지반재료 내부 미세구조 및 공극구조의 정밀한 이미지 처리는 종종 이미지 내에 원천적으로 포함되는 화상결함으로 인해 제약된다. 본 논문에서는 X-ray micro-CT 이미지에 가장 일반적으로 나타나는 화상결함인 패임(영상 외곽과 중심부의 명암 차이) 및 동심원상(영상 중심으로부터 방사방향으로 연속적으로 나타나는 원)을 제거할 수 있는 이미지 보정 기법을 제시한다. 결함 제거는 좌표 변환법, 정규화 및 2차원 푸리에 변환에 의한 저역 통과 필터링 기법의 순차적 적용을 통해 이루어진다. 이미지 처리 기법의 효과를 다공성 현무암의 CT 이미지에서 화상결함들을 제거하고 이진화 후 적층하여 3차원 공극 구조를 추출하는 과정을 통해 설명하였다. 패임 및 동심원상 결함을 제거한 이미지와 원본 이미지의 비교 결과 결함 제거는 대상 재료 공극률의 과대평가를 방지할 수 있으며, 따라서 화상결함의 적절한 보정은 X-ray CT의 지반재료 적용 시 필수적인 과정으로 판단된다.

• Radiation Oncology Journal
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• 제16권1호
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• pp.81-86
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• 1998

목적 : Broad-beam Simulator-CT 개발을 위한 예비연구로서 통상의 모의 치료 영상에서 축상면, 시상면, 관상면 영상을 구현하려 하였다. 대상 및 방법 : 120kVp, 2mAs 동일 조건에서 갠트리 각도를 $4^{\circ}$ 간격으로 90장의 필름을 얻어 입체적인 Filtered back-projection을 시도하였다 외곽선을 찾아 제거하였고, 산란선 성분을 Deconvolution 방법으로 제거하여 좋은 영상을 얻도록 하였다. 결과 : 이 방법으로 축상면, 시상면, 관상면 영상을 얻었으며 각 방향에 대해 동일한 분해능을 갖았다. 그러나 영상의 질은 대단히 나빴다. 결론 : Broad-beam으로 된 CT 영상을 구현할 수 있었다. 이를 위하여 산란선 성분의 Deconvolution이 필요하였으며, 입체적인 back-projection을 실시하였으므로 축상, 시상, 관상 모든 방향에 대해 동일한 분해능을 갖고 있어서 DRR 등 Simulator-CT에 응용할 수 있음을 알 수 있었다. 그러나 실용적인 임상응용을 위해서는 영상의 질 개선이 필요하였다.

• 대한방사선기술학회지:방사선기술과학
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• 제44권4호
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• pp.367-373
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• 2021

A quality assurance of computed tomography(CT) have done seven items that were water attenuation coefficient, noise, homogeneity, spatial resolution, contrast resolution, slice thickness, artifact using by standard phantom. But there is no quality assurance items and methods for CT simulator at domestic institutions yet. Therefore the study aimed to access the CT dose index(CTDI), table tilting, image distortion, laser accuracy, table movement accuracy and CT seven items for CT simulator quality assurance. The CTDI at the center of the head phantom was 0.81 for 80 kVp, 1.55 for 100 kVp, 2.50 for 120 mm, 0.22 for 80 kVp at the center of the body phantom, 0.469 for 100 kVp, and 0.81 for 120 kVp. The table tilting was within the tolerance range of ±1.0° or less. Image distortion had 1 mm distortion in the left and right images based on the center, and the laser accuracy was measured within ±2 mm tolerance. The purpose of this study is to improve the quality assurance items suitable for the current situation in Korea in order to protect the normal tissues during the radiation treatment process and manage the CT simulator that is implemented to find the location of the tumor more clearly. In order to improve the accuracy of the CT simulator when looking at the results, the error range of each item should be small. It is hoped that the quality assurance items of the CT simulator will be improved by suggesting the quality assurance direction of the CT simulator in this study, and the results of radiation therapy will also improve.

• 정보처리학회논문지B
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• 제13B권6호
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• pp.601-606
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• 2006

의료기술의 비약적인 발전과 함께 의료기관에서 사용되는 영상 데이터량이 급속히 증가하고 있다. 따라서 대용량 의료 영상의 해석을 위해서는 의사들의 육안 검사보다 영상처리 기술을 이용한 자동화 방법이 필요하다. 본 논문에서는 복부 CT영상의 간 영역에 대해 GLCM(Gray Level Co-occurrence Matrix)을 이용하여 텍스처 정보를 취득하고, 이 데이터로부터 주성분 분석을 통해 간종양을 자동으로 검출하는 방법에 대해 제안한다. 기존의 간종양 검출은 명암도 한 가지 특징에 의한 방법이 대부분이었으나, 본 논문에서 CT영상에 대해 GLCM의 텍스처 정보 8가지를 이용해서 4개의 주성분 누적 영상으로 변환시켰다. 실험결과 4개의 주성분 누적 영상의 백분율 분산값은 89.9%였으며, 이를 명암도 한 가지 만을 이용한 간종양 검출방법과 면적을 비교했을 때 약 92%의 일치도를 보였다. 이는 영상데이터의 차원을 8개의 차원에서 그 절반인 4개의 차원으로 줄여도 간종양을 검출할 수 있음을 의미한다.

• Korean Journal of Radiology
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• 제19권6호
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• pp.1031-1041
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• 2018

Objective: To compare image quality and radiation dose of high-pitch dual-source spiral cardiothoracic computed tomography (CT) between non-electrocardiography (ECG)-synchronized and prospectively ECG-triggered data acquisitions in young children with congenital heart disease. Materials and Methods: Eighty-six children (${\leq}3$ years) with congenital heart disease who underwent high-pitch dual-source spiral cardiothoracic CT were included in this retrospective study. They were divided into two groups (n = 43 for each; group 1 with non-ECG-synchronization and group 2 with prospective ECG triggering). Patient-related parameters, radiation dose, and image quality were compared between the two groups. Results: There were no significant differences in patient-related parameters including age, cross-sectional area, body density, and water-equivalent area between the two groups (p > 0.05). Regarding radiation dose parameters, only volume CT dose index values were significantly different between group 1 ($1.13{\pm}0.09mGy$) and group 2 ($1.07{\pm}0.12mGy$, p < 0.02). Among image quality parameters, significantly higher image noise ($3.8{\pm}0.7$ Hounsfield units [HU] vs. $3.3{\pm}0.6HU$, p < 0.001), significantly lower signal-to-noise ratio ($105.0{\pm}28.9$ vs. $134.1{\pm}44.4$, p = 0.001) and contrast-to-noise ratio ($84.5{\pm}27.2$ vs. $110.1{\pm}43.2$, p = 0.002), and significantly less diaphragm motion artifacts ($3.8{\pm}0.5$ vs. $3.7{\pm}0.4$, p < 0.04) were found in group 1 compared with group 2. Image quality grades of cardiac structures, coronary arteries, ascending aorta, pulmonary trunk, lung markings, and chest wall showed no significant difference between groups (p > 0.05). Conclusion: In high-pitch dual-source spiral pediatric cardiothoracic CT, additional ECG triggering does not substantially reduce motion artifacts in young children with congenital heart disease.

• Advances in nano research
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• 제12권2호
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• pp.185-195
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• 2022

Deep learning is another field of artificial intelligence (AI) utilized for computer aided diagnosis (CAD) and image processing in scientific research. Considering numerous mechanical repetitive tasks, reading image slices need time and improper with geographical limits, so the counting of image information is hard due to its strong subjectivity that raise the error ratio in misdiagnosis. Regarding the highest mortality rate of Lung cancer, there is a need for biopsy for determining its class for additional treatment. Deep learning has recently given strong tools in diagnose of lung cancer and making therapeutic regimen. However, identifying the pathological lung cancer's class by CT images in beginning phase because of the absence of powerful AI models and public training data set is difficult. Convolutional Neural Network (CNN) was proposed with its essential function in recognizing the pathological CT images. 472 patients subjected to staging FDG-PET/CT were selected in 2 months prior to surgery or biopsy. CNN was developed and showed the accuracy of 87%, 69%, and 69% in training, validation, and test sets, respectively, for T1-T2 and T3-T4 lung cancer classification. Subsequently, CNN (or deep learning) could improve the CT images' data set, indicating that the application of classifiers is adequate to accomplish better exactness in distinguishing pathological CT images that performs better than few deep learning models, such as ResNet-34, Alex Net, and Dense Net with or without Soft max weights.

• 핵의학기술
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• 제17권2호
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• pp.67-71
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• 2013

PET/CT검사에서 CT촬영조건의 변화는 영상의 화질 및 환자가 받는 피폭선량에 영향을 미친다. 본 연구는 CT 매개 변수 중 Pitch와 X-선관 회전시간 변화에 따른 선량대비 CT 영상의 질과 이로 인해 PET상에서 SUV에 미치는 영향을 비교 평가하고자 하였다. Discovery STe PET/CT 장비를 이용하여 영상을 획득하였다. QA Phantom과 AAPM Phantom을 이용한 CT 영상 획득 시 Pitch는 0.562, 0.938, 1.375, 1.75:1로 4단계, X-선관 회전시간은 0.5에서 1.0까지 0.1초씩 증가시켜 6단계로 나누어 총 24개 조합을 적용한 영상을 각각 획득하였다. PET 영상은 $^{18}F-FDG$ 5.3 kBq/mL가 채워진 1994 NEMA PET Phantom을 이용하여 프레임당 2분 30초의 방출영상을 획득하였다. 각 조합의 CT 영상에 관심영역을 설정하고 CT number의 표준편차를 측정하였다. 동일한 영상에서 DLP변화에 따른 영상잡음의 예측값을 계산하여 예측값 대비 실측값의 비율을 구해 선량대비 영상잡음 효과를 비교하는 척도로 사용하였다. AAPM Phantom 영상에서 1.0 mm까지 식별이 가능한 지 확인하였다. NEMA PET Phantom의 방출영상에 관심영 역을 설정하고 SUV를 비교 평가하였다. Pitch가 0.562, 0.938, 1.375, 1.75:1로 변화할 때 영상잡음 효과는 QA Phantom에서 1.00, 1.03, 1.01, 0.96, AAPM Phantom에서 1.00, 1.04, 1.02, 0.97로 측정되었다. 회전시간의 증가에 따른 경우 QA Phantom에서 0.99, 1.02, 1.00, 1.00, 0.99, 0.99이었고, AAPM Phantom에서 1.01, 1.01, 0.99, 1.01, 1.01, 1.01로 SPSS Ver. 18을 이용하여 상관관계를 분석한 결과 피어슨 상관계수는 -0.059로 나타났다. 공간분해능에 대한 평가는 24개의 조합 모두에서 1.0 mm까지 육안으로 구별이 가능하였다. SUV의 경우 평균 SUV는 모든 조합에서 1.1로 모두 동일한 값을 나타내었다. Pitch 변화에 따른 CT 영상 평가에서 1.75:1을 적용 시 선량대비 가장 적은 영상잡음 효과를 보이며 공간분해능과 SUV에는 영향을 미치지 않는다. 그러나 회전시간 변화가 영상에 미치는 영향에는 유의한 차이가 없음을 알 수 있다. 결과에서와 같이 각 장비에 따른 선량대비 영상잡음이 적은 Pitch를 사용하고 환자의 체격에 따른 적절한 X-선관 회전시간을 이용한다면 환자의 피폭선량을 줄이면서 최적의 화질을 얻을 수 있는 프로토콜을 구성하는데 도움이 될 것이라 사료된다.

• Korean Journal of Radiology
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• 제23권4호
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• pp.402-412
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• 2022

Objective: To evaluate the image quality and lesion detectability of lower-dose CT (LDCT) of the abdomen and pelvis obtained using a deep learning image reconstruction (DLIR) algorithm compared with those of standard-dose CT (SDCT) images. Materials and Methods: This retrospective study included 123 patients (mean age ± standard deviation, 63 ± 11 years; male:female, 70:53) who underwent contrast-enhanced abdominopelvic LDCT between May and August 2020 and had prior SDCT obtained using the same CT scanner within a year. LDCT images were reconstructed with hybrid iterative reconstruction (h-IR) and DLIR at medium and high strengths (DLIR-M and DLIR-H), while SDCT images were reconstructed with h-IR. For quantitative image quality analysis, image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured in the liver, muscle, and aorta. Among the three different LDCT reconstruction algorithms, the one showing the smallest difference in quantitative parameters from those of SDCT images was selected for qualitative image quality analysis and lesion detectability evaluation. For qualitative analysis, overall image quality, image noise, image sharpness, image texture, and lesion conspicuity were graded using a 5-point scale by two radiologists. Observer performance in focal liver lesion detection was evaluated by comparing the jackknife free-response receiver operating characteristic figures-of-merit (FOM). Results: LDCT (35.1% dose reduction compared with SDCT) images obtained using DLIR-M showed similar quantitative measures to those of SDCT with h-IR images. All qualitative parameters of LDCT with DLIR-M images but image texture were similar to or significantly better than those of SDCT with h-IR images. The lesion detectability on LDCT with DLIR-M images was not significantly different from that of SDCT with h-IR images (reader-averaged FOM, 0.887 vs. 0.874, respectively; p = 0.581). Conclusion: Overall image quality and detectability of focal liver lesions is preserved in contrast-enhanced abdominopelvic LDCT obtained with DLIR-M relative to those in SDCT with h-IR.

• 치과방사선
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• 제22권2호
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• pp.283-290
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• 1992

The purpose of this study was to evaluate the spatial relationship of facial bone more accurately. For this study, the three-dimensional images of dry skull were reconstructed using computer image analysis system and three-dimensional reconstructive program involved CT. The obtained results were as follows: 1. Three-dimensional reconstructive CT results in images that have better resolution and more contrast 2. It showed good marginal images of anatomical structure on both three-dimensional CT and computer image analysis system, but the roof of orbit, the lacrimal bone and the squamous portion of temporal bone were hardly detectable. 3. The partial loss of image data were observed during the regeneration of saved image data on three-dimensional CT. 4. It saved the more time for reconstruction of three-dimensional images using computer image analysis system. But, the capacity of hardware was limited for inputting of image data and three-dimensional reconstructive process. 5. We could observe the spatial relationship between the region of interest and the surrounding structures by three-dimensional reconstructive images without invasive method.