• 핵의학기술
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• 제14권1호
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• pp.3-7
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• 2010

종양학에서 암의 진단, 병기 결정, 재발 판정, 그리고 치료에 대한 반응의 평가에 PET/CT의 유용함은 이미 인정되고 있다. 그런데 크기가 작거나 폐의 기저 부위, 혹은 간의 상부에 위치한 종양일수록 호흡으로 인한 위치 변위와 왜곡의 정도가 크며 PET영상의 SUV에도 영향을 미치게 된다. 따라서 정상 호흡 상태에서 얻어지는 PET 또는 CT영상을 토대로 방사선 치료를 하게 되면 치료해야 할 표적이 치료 범위에서 벗어나거나 정상 조직에 과도한 방사선이 조사될 수 있으므로 치료율이 낮아지거나 방사선에 의한 부작용이 증가할 수 있다. 본 논문의 목적은 호흡에 의한 인공산물을 최소화하고 보다 정확한 SUV 측정을 위해 ACT를 이용한 감쇠 보정 방법을 적용하여 그 유용성을 평가하고자 함이다. 하 흉부에 종양이 있는 13명의 환자를 대상으로 Discovery STE8 PET/CT스캐너를 사용하여 두 가지의 PET/CT영상을 얻었다. HCT를 사용한 감쇠 보정 영상과 ACT를 사용한 감쇠 보정 영상에서 측정한 인공물의 크기와 $SUV_{max}$를 비교 분석하였다. 인공물은 모든 환자의 하 흉부의 백색 음영 영역을 측정하여 평가하였다. $SUV_{max}$는 주요 종양의 $SUV_{max}$를 측정하여 평가하였다. 분석 프로그램은 Advantage Workstation v4.3을 사용하였다. 환자에게 7.4 MBq (0.2 mCi)/kg의 $^{18}F$-FDG를 투여한 1시간 뒤 스캔하였다. 방출 스캔은 3 min/bed로 스캔하였다. HCT 보정 영상과 비교하여 ACT 보정 영상에서 인공산물의 크기가 눈에 띄게 줄어든 것을 관찰할 수 있었다. 하 흉부의 저 보정으로 인한 인공산물 크기는 ACT 보정 영상과 HCT 보정 영상 각각 $1.5{\pm}3.5$ cm과 $13.4{\pm}4.2$ cm를 나타냈다. 주요 병소의 $SUV_{max}$의 변화는 ACT 보정 영상이 HCT 보정 영상보다 눈에 띄게 상승하였다. ACT 보정 영상에서 HCT영상과 비교하여 tumor의 $SUV_{max}$가 평균 $5.3{\pm}3.9%$ 상승하였다. 가장 큰 차이를 보인 종양은 lung의 lower lobe에 있는 종양으로 $SUV_{max}$ 7.7에서 $SUV_{max}$ 8.7로 13% 상승하였다. ACT를 이용한 감쇠 보정 영상은 하 흉부의 인공산물을 눈에 띄게 줄일 수 있어 보다 정확한 병소의 SUV를 측정 및 방사선 치료 범위 설정에 도움이 될 수 있을 것이다. 또한 ACT 기법은 병소가 횡격막 부근에 있는 환자의 경우 폐와 간의 경계를 보다 정확히 구분할 수 있어 판독시 도움이 될 수 있을 것이라고 판단된다. 추가 ACT 촬영에 의한 피폭 선량이 증가하는 점을 고려하여 적용한다면 임상적으로 유용한 효과가 있다고 사료된다.

• Journal of Magnetics
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• 제21권2호
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• pp.286-292
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• 2016

This study evaluates the change of computer tomography (CT) number in the case of the metal artifact reduction (MAR) algorithm, using the phantom. The images were obtained from dual CT using a gammex 467 tissue characterization phantom, which is similar to human tissues. The test method was performed by dividing pre and post MAR algorithm and measured CT values of nonmagnetic materials within the phantom. In addition, the changes of CT values for each material were compared and analyzed after measuring CT values up to 140 keV, using the spectral HU curve followed by CT scan. As a result, in the cases of N rod (trabecular bone) and E rod (trabecular bone), the CT numbers decreased as keV increasing but were constant above 90 keV. In the cases of I rod (dense bone) and K rod (dense bone), the CT numbers also decreased as keV increased but were uniform above 90 keV. The CT numbers from 40 keV to 140 keV were consistent in the cases of J rod (liver), D rod (liver), L rod (muscle), and F rod (muscle). For A rod (adipose), G rod (adipose), B rod (breast) and O rod (breast), the CT numbers increased as keV increased but were constant after 90 keV. The CT numbers from 40 keV to 140 keV were consistent in the cases of C rod (lung (exhale)), P rod (lung (exhale)), M rod (lung (inhale)) and H rod (lung (exhale)). Conclusively, because dual CT exhibits no changes in image quality and is able to analyze nonmagnetic materials by measuring the CT values of various materials, it will be used in the future as a useful tool for the diagnosis of lesions.

• Korean Journal of Radiology
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• 제23권9호
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• pp.921-930
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• 2022

Objective: To identify epidermal growth factor receptor (EGFR) mutations in lung adenocarcinoma based on ¹⁸F-fluorodeoxyglucose (FDG) PET/CT radiomics and clinical features and to distinguish EGFR exon 19 deletion (19 del) and exon 21 L858R missense (21 L858R) mutations using FDG PET/CT radiomics. Materials and Methods: We retrospectively analyzed 179 patients with lung adenocarcinoma. They were randomly assigned to training (n = 125) and testing (n = 54) cohorts in a 7:3 ratio. A total of 2632 radiomics features were extracted from the tumor region of interest from the PET (1316) and CT (1316) images. Six PET/CT radiomics features that remained after the feature selection step were used to calculate the radiomics model score (rad-score). Subsequently, a combined clinical and radiomics model was constructed based on sex, smoking history, tumor diameter, and rad-score. The performance of the combined model in identifying EGFR mutations was assessed using a receiver operating characteristic (ROC) curve. Furthermore, in a subsample of 99 patients, a PET/CT radiomics model for distinguishing 19 del and 21 L858R EGFR mutational subtypes was established, and its performance was evaluated. Results: The area under the ROC curve (AUROC) and accuracy of the combined clinical and PET/CT radiomics models were 0.882 and 81.6%, respectively, in the training cohort and 0.837 and 74.1%, respectively, in the testing cohort. The AUROC and accuracy of the radiomics model for distinguishing between 19 del and 21 L858R EGFR mutational subtypes were 0.708 and 66.7%, respectively, in the training cohort and 0.652 and 56.7%, respectively, in the testing cohort. Conclusion: The combined clinical and PET/CT radiomics model could identify the EGFR mutational status in lung adenocarcinoma with moderate accuracy. However, distinguishing between EGFR 19 del and 21 L858R mutational subtypes was more challenging using PET/CT radiomics.

• 한국전자통신학회논문지
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• 제17권3호
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• pp.529-536
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• 2022

지난 2년 동안 중증급성호흡기증후군 코로나바이러스-2(SARS-CoV-2)는 점점 더 많은 사람들에게 영향을 미치고 있다. 본 논문에서는 COVID-19 폐 CT 이미지를 분할하고 분류하기 위해서 서브코딩블록(SCB), 확장공간파라미드풀링(ASSP)와 어텐션게이트(AG)로 구성된 혼합 모드 특징 추출 방식의 새로운 U-Net 컨볼루션 신경망을 제안한다. 그리고 제안된 모델과 비교하기 위하여 FCN, U-Net, U-Net-SCB 모델을 설계한다. 제안된 U-Net-MMFE 는 COVID-19 CT 스캔 디지털 이미지 데이터에 대하여 atrous rate가 12이고, Adam 최적화 알고리즘을 사용할 때 다른 분할 모델에 비하여 94.79%의 우수한 주사위 분할 점수를 얻었다.

• 대한디지털의료영상학회논문지
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• 제17권1호
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• pp.13-18
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• 2015

Purpose : Skip the repetitive HRCT axial scan in order to reduce the exposure of patients during chest HRCT scan, Helical Scan Data into a reconstructed image, and exposure of the patient change and visually evaluate the usefulness of the HRCT images. Materials and method : Patients were enrolled in the survey are 50 people who underwent chest CT scans of patients who presented to the hospital from January 2015 to March 2015. 50 people surveyed 22 people men and 28 people women people showed an average distribution of 30 to 80 years age was 48 years. 50 patients to Somatom Sensation 64 ch (Siemens) model with 120 kVp tube voltage to a reference mAs tube current to mAs (Care dose, Siemens) as a whole, including the lungs and the chest CT scan was performed. Scan upon each patient CARE dose 4D (Automatic exposure control, Siemens Medical Solution Erlangen, Germany) was to maintain the proper radiation dose scan every cross-section through a device that automatically adjusts the tube current of. CT scan is the rotation time of the Tube slice collimation, slice width 0.6 mm, pitch factor was made under the terms of 1.4. CT scan obtained after the raw data (raw data) to the upper surface of the axial images and coronal images for each slice thickness 1 mm, 5 mm intervals in the high spatial frequency calculation method (hight spatial resolution algorithm, B60 sharp) was the use of the lung window center -500 HU, windows were reconstructed into images in the interval -1000 HU to see. Result : 1. Measure the total value of DLP 50 patients who proceed to chest CT group A (Helical Scan after scan performed with HRCT) and group B (Helical Scan after the HR image reconstruction to the original data) compared with the group divided, analysis As a result of the age, but show little difference for each age group it had a decreased average dose of about 9%. 2. A Radiation read the results of the two Radiologist and a doctor upper lobe and middle lobe of the lung takes effect the visual evaluation is not a big difference between the two images both, depending on the age of the patient, especially if the blood vessels of the lower lobe (A: 3.4, B: 4.6) and bronchi(A: 3.8, B4.7) image shake caused by breathing in anxiety (blurring lead) to the original data (raw data) showed that the reconstructed image is been more useful in diagnostic terms. Conclusion : Scan was confirmed a continuous, rapid motion video to get Helical scan is much lower lobe lung reduction in visual blurring, Helical scan data to not repeat the examination by obtaining HRCT images reorganization reduced the exposure of the patient.

• Nuclear Medicine and Molecular Imaging
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• 제42권1호
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• pp.81-81
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• 2008

A 69-year old male with cholangiocellular carcinoma (CCC) was assigned to our department for whole body PET/CT scan. $^{18}F$-FDG PET/CT images showed an intense hypermetabolic lobulating mass(SUVmax = 8.7 / size : 11.4 mm) in the right hepatic lobe with multiple metastatic lung nodules. We made three dimensional volume rendering fusion images by using advantage workstation 4.3 (GE health care) which provide quick anatomic overview and improve the planning process significantly.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2010년도 한국컴퓨터종합학술대회논문집 Vol.37 No.1(C)
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• pp.447-451
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• 2010

In this paper, we propose a simple but effective algorithm to increase the speed of Emphysema region classification. Emphysema region classification method based on CT image consumes a lot of time because of the large number of subregions due to the large size of CT image. Some of the sub-regions contain no Emphysema and the classification of these regions is worthless. To speed up the classification process, we create an algorithm to select Emphysema region candidates and only use these candidates in the Emphysema region classification instead of all of the sub-regions. First, the lung region is detected. Then we threshold the lung region and only select the dark pixels because Emphysema only appeared in the dark area of the CT image. Then the thresholded pixels are clustered into a region that called the Emphysema pre-detected region or Emphysema region candidate. This region is then divided into sub-region for the Emphysema region classification. The experimental result shows that Emphysema region classification using predetected Emphysema region decreases the size of lung region which will result in about 84.51% of time reduction in Emphysema region classification.

• 한국멀티미디어학회논문지
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• 제23권5호
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• pp.625-632
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• 2020

In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.

• 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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• 제22권1호
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• pp.28-34
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• 2011

적응 방사선 치료(Adaptive Radiation Therapy, ART)를 실행하기 위한 고도의 정확성을 갖는 변형 영상 정합 방법은 필수이다. 본 연구의 목적은 Megavoltage cone-beam CT (MV CBCT)영상의 Intensity 변화를 통한 영상 정합의 정확성의 향상이다. Intensity 변화 값을 도출 하기 위해 kilovoltage CT (kV CT)와 MV CBCT를 이용하여 12 종류의 전자 밀도 바를 제공하는 Cheese 팬텀의영상을 획득하고, 영상들로부터 kV CT와 MV CBCT의 Hounsfield Unit (HU)값들의 관계를 도출하였다. MV CBCT 영상의 잡음을 감소하기 위해 Gaussian smoothing 필터를 적용하였다. MV CBCT영상의 intensity는 마치 동일한 모달리티에서 획득된 영상과 같은 kV CT와 동일한 범위의 intensity로 변화시켰다. 이후 두 영상에 효율적이고 사용하기 쉬운 intensity 기반의 데몬 영상 정합이 적용되었다. 본 연구실에서 인체 내 폐를 모사하도록 제작된 변형 폐 팬텀을 이용하여 위와 같은 방법을 적용하여 영상 정합을 하였다. Cheese 팬텀 영상, 변형 폐 팬텀 영상을 이용한 변형영상 정합 결과는 상관 계수가 각각 6.07%, 18% 향상되었다. 변형 폐 팬텀 영상의 변형 영상 정합 정확성을 평가하기 위해 추가적으로 측정된 팬텀 내부에 삽입한 표적의 중심 좌표를 이용하여vector 차이를 계산하였다. 벡터 차이는 $2.23{\pm}1.19mm$, $1.39{\pm}0.97mm$였다. 본 연구에서 사용한 intensity 변화 방법을 통해 변형 영상 정합의 정확성이 향상됨을 확인 하였고, 본 연구는 영상 정합 정확성을 향상시키기 위한 해결 방법이 될 수 있다. 차후 연구 계획도 본 연구 내용에 의해 제안되었다.