• Title/Summary/Keyword: 3D Chest CT

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Detection of Foreign Body in Esophageal Foreign Body Model Using Three Dimensional Reconstruction Technique (식도 이물 모델에서 이물 탐색을 위한 삼차원 재구성법의 활용)

  • Woo, Kuk Sung;Yoo, Young Sam;Kim, Dong Won
    • Korean Journal of Bronchoesophagology
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    • v.18 no.1
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    • pp.13-18
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    • 2012
  • Objective This study was conducted to gather basic information of 3D CT in detecting and gaining information of esophageal foreign body (FB) models. Materials and Methods The chest model was made using PVC bottle, rubber balloon and plaster. Fish bone, Persimmon stone were used to mimic foreign bodies of esophageal model. The foreign body models were inserted into the balloon removing air from it and the balloon was sealed. The esophageal FB model was inserted into the chest model. The remaining space in the chest model was filled with fish paste and water to simulate soft tissue around esophagus. CT of chest model was reconstructed three-dimensionally by Rapidia software to make images of foreign body models. The axial CT, MPR image and VOI image were compared with real foreign body materials as to shape, size, location and orientation. Results Esophageal FB models were easily made. CT data gave good 3D images and showed realistic foreign body materials. Conclusion The results indicate the usefulness of 3D CT technique to help in diagnosis of esophageal foreign body models.

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Expert Opinion Questionnaire About Chest CT Scan Using A Negative Pressure Isolation Strecher in COVID-19 Patients: Image Quality and Infection Risk (COVID-19 환자에서 음압격리들것을 이용한 흉부 CT 검사에 대한 전문가 의견 설문: 영상품질과 감염위험)

  • Kwang Nam Jin;Bo Da Nam;Jaemin Shin;Sung Ho Hwang
    • Journal of the Korean Society of Radiology
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    • v.84 no.4
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    • pp.891-899
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    • 2023
  • Purpose To survey perceptions of certified physicians on the protocol of chest CT in patients with coronavirus (COVID-19) using a negative pressure isolation stretcher (NPIS). Materials and Methods This study collected questionnaire responses from a total of 27 certified physicians who had previously performed chest CT with NPIS in COVID-19 isolation hospitals. Results The nine surveyed hospitals performed an average of 116 chest CT examinations with NPIS each year. Of these, an average of 24 cases (21%) were contrast chest CT. Of the 9 pulmonologists we surveyed, 5 (56%) agreed that patients who showed abnormalities in serum D-dimer required contrast chest CT. All 9 surveyed radiologists agreed that the image quality of the chest CT with NPIS was sufficient for CT image interpretation regarding pneumonia or pulmonary embolism. Furthermore, in our 9 surveyed infectionologists, 5 (56%) agreed that a risk of secondary infection in the CT room after temporary opening of NPIS could be prevented through a process of disinfection. Conclusion Experienced physicians considered that the effects of NIPS on chest CT image quality was minimal in patients with COVID-19, and the risk of CT room contamination was easily controlled.

Comparison of Noise and Doses of Low Dose and High Resolution Chest CT for Automatic Tube Current Modulation and Fixed Tube Current Technique using Glass Dosimetry (유리선량계를 이용한 관전류자동조절기법과 고정관전류기법에서 저선량 및 고해상 흉부CT의 노이즈 및 선량 비교)

  • Park, Tae Seok;Han, Jun Hee;Jo, Seung Yeon;Lee, Eun Lim;Jo, Kyu Won;Kweon, Dae Cheol
    • Journal of Radiation Industry
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    • v.11 no.3
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    • pp.131-137
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    • 2017
  • To compare the radiation dose and image noise of low dose computed tomography (CT) and high resolution CT using the fixed tube current technique and automatic tube current modulation (CARE Dose 4D). Chest CT and human anthropomorphic phantom were used the RPL (radiophotoluminescence) dosimeters. For image evaluation, standard deviation of mean CT attenuation coefficient and CT attenuation coefficient was measured using ROI analysis function. The effective dose was calculated using CTDIvol and DLP. CARE Dose 4D was reduced by 74.7% and HRCT by 64.4% compared to the fixed tube current technique in low dose CT of chest phantom. In CTDIvol and DLP, the dose of CARE Dose 4D was reduced by fixed tube current technique. For effective dose, CARE Dose 4D was reduced by 47% and HRCT by 46.9% compared to the fixed tube current method, and the dose of CARE Dose 4D was significantly different (p<.05). Noise in the image was higher than that in the fixed tube current technique. Noise difference in the image of CARE Dose 4D in low dose CT was significant (p<.05). The low radiation dose and the noise difference of the CARE Dose 4D were compared with the fixed tube current technique in low dose CT and HRCT using chest phantom. The radiation doses using CARE Dose 4D were in accordance with the national and international dose standards. CARE Dose 4D should be applied to low dose CT and HRCT for clinical examination.

Automated Lung Segmentation on Chest Computed Tomography Images with Extensive Lung Parenchymal Abnormalities Using a Deep Neural Network

  • Seung-Jin Yoo;Soon Ho Yoon;Jong Hyuk Lee;Ki Hwan Kim;Hyoung In Choi;Sang Joon Park;Jin Mo Goo
    • Korean Journal of Radiology
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    • v.22 no.3
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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.

Automatic Segmentation of Pulmonary Structures using Gray-level Information of Chest CT Images (흉부 CT 영상의 밝기값 정보를 사용한 폐구조물 자동 분할)

  • Yim, Ye-Ny;Hong, Helen
    • Journal of KIISE:Software and Applications
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    • v.33 no.11
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    • pp.942-952
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    • 2006
  • We propose an automatic segmentation method for identifying pulmonary structures using gray-level information of chest CT images. Our method consists of following five steps. First, to segment pulmonary structures based on the difference of gray-level value, we select the threshold using optimal thresholding. Second, we separate the thorax from the background air and then the lungs and airways from the thorax by applying the inverse operation of 2D region growing in chest CT images. To eliminate non-pulmonary structures which has similar intensities with the lungs, we use 3D connected component labeling. Third, we segment the trachea and left and right mainstem bronchi using 3D branch-based region growing in chest CT images. Fourth, we can obtain accurate lung boundaries by subtracting the result of third step from the result of second step. Finally, we select the threshold in accordance with histogram analysis and then segment radio-dense pulmonary vessels by applying gray-level thresholding to the result of the second step. To evaluate the accuracy of proposed method, we make a visual inspection of segmentation result of lungs, airways and pulmonary vessels. We compare the result of the conventional region growing with the result of proposed 3D branch-based region growing. Experimental results show that our proposed method extracts lung boundaries, airways, and pulmonary vessels automatically and accurately.

Development of a Semi-Automated Detection Method and a Classification System for Bone Metastatic Lesions in Vertebral Body on 3D Chest CT (3차원 흉부 CT에서 추체 골 전이 병변에 대한 반자동 검출 기법 및 분류 시스템 개발)

  • Kim, Young Jae;Lee, Seung Hyun;Choi, Ja Young;Sun, Hye Young;Kim, Kwang Gi
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.38C no.10
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    • pp.887-895
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    • 2013
  • Metastatic bone cancer, the cancer which occurred in the various organs and progressively spread to bone, is one of the complications in cancer patients. This cancer is divided into the osteoblast and osteolytic metastasis. Although Computer Tomography(CT) could be an useful tool in diagnosis of bone metastasis, lesions are often missed by the visual inspection and it makes clinicians difficult to detect metastasis earlier. Therefore, in this study, we construct a three-dimensional(3D) volume rendering data from tomography images of the chest CT, and apply a 3D based image processing algorithm to them for detection bone metastasis lesions. Then we perform a three-dimensional visualization of the detected lesions.From our test using 10 clinical cases, we confirmed 94.1% of average sensitivity for osteoblast, and 90.0% of average sensitivity, respectively. Consequently, our findings showed a promising possibility and potential usefulness in diagnosis of metastastic bone cancer.

The Comparison Evaluation of SUV Using Different CT Devices in PET/CT Scans (PET 검사에서 CT 장비의 차이에 따른 PET/CT의 SUV 비교 평가)

  • Kim, Woo Hyun;Go, Hyeon Soo;Lee, Jeong Eun;Kim, Ho Sung;Ryu, Jae Kwang;Jung, Woo Young
    • The Korean Journal of Nuclear Medicine Technology
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    • v.18 no.1
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    • pp.10-18
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    • 2014
  • Purpose: Among different PET/CT devices which are composed of same PET model but different CT models, SUV, usually used for quantitative evaluation, was measured to assess the accuracy of follow up scans in different PET/CT and confirm that interequipment compatibility is useful in arranging the PET/CT exam appointment. Materials and Methods: Using ACR PET Phantom, PET NEMA IEC Body Phantom, SNM Chest Phantom and Ge-68 cylinder Phantom, $SUV_{mean}$ and $SUV_{max}$ was measured by 3 different models of PET/CT (Discovery 690, Discovery 690Elite and Discovery 710, GE) made in same company. ANOVA was used to evaluate the significant difference in the result. Results: In the result, the average of $SUV_{max}$ was D690 (25 mm-1.82, 16 mm-1.75, 12 mm-1.73, 8 mm-1.44), D690E (25 mm-1.76, 16 mm-1.92, 12 mm-1.78, 8 mm-1.55) and D710 (25 mm-1.84, 16 mm-1.89, 12 mm-1.77, 8 mm-1.61) in ACR Phantom, D690 (25 mm-2.26, 16 mm-2.25, 12 mm-1.92, 8 mm-1.85), D690E (25 mm-2.45, 16 mm-2.25, 12 mm-2.05 8 mm-1.91) and D710(25 mm-2.49, 16 mm-2.20, 1 2mm-2.30, 8 mm-2.05) in PET NEMA IEC Body Phantom, D690-1.04, D690E-1.10 and D710-1.09 in SNM Chest Phantom and D690-0.81, D690E-0.81, D710-0.84 in Ge-68 cylinder Phantom. The differences between average SUV of 4 phantoms were $SUV_{mean}$-1.87%, $SUV_{max}$-2.15%. And also as a result of ANOVA analysis, there was no significant difference statistically. Conclusion: If different models of PET/CT have same specification of PET system, there was no significant difference in $SUV_{mean}$ and $SUV_{max}$ even though they have different CT system. And also differences of $SUV_{mean}$ and $SUV_{max}$ in phantom images were under 5% which many manufacturers recommend. Therefore, follow up scan will be possible using different PET/CT if it has same specification of PET system with the previous PET/CT. This information will enable the accurate comparative analysis when conducting follow up scans and be helpful to schedule PET/CT exam more effectively.

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Measurement of Radiation Dose of HR CT and Low Dose CT by using Anthropomorphic Chest Phantom and Glass Dosimetry (인체등가형 흉부팬텀과 유리선량계를 이용한 고해상력 및 저선량 CT의 선량측정)

  • Kweon, Dae Cheol
    • Journal of the Korean Society of Radiology
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    • v.13 no.7
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    • pp.933-939
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    • 2019
  • The purpose of this study is to provide basic clinical data by evaluating images, measuring absorbed dose and effective dose by using high resolution CT and low dose CT by using anthropomorphic chest phantom and glass dosimeter. Tissue dose was measured by inserting a glass dosimeter into the anthropomorphic chest phantom. A 64-slice CT system (SOMATOM Sensation 64, Siemens AG, Forchheim, Germany) and CARE Dose 4D were used, and the parameters of the high resolution CT were 120 kVp, Eff. Scan parameters of mAs 104, scan time 7.93 s, slice 1.0 mm (Acq. 64 × 0.6 mm), convolution kernel (B60f sharp) were used, and low dose CT was 120 kVp, Eff. mAs 15, scan time 7.41 s, slice 3.0 mm (Acq. 64 × 0.6 mm), scan of convolution kernel B50f medium sharp. CTDIvol was measured at 8.01 mGy for high resolution CT and 1.18 mGy for low dose CT. Low dose CT scans showed 85.49% less absorbed dose than high resolution CT scans.

Quantitative analysis of three dimensional volumetric images in Chest CT (흉부 CT 검사에서 3차원 체적 영상의 정량적 분석)

  • Jang, Hyun-Cheol;Cho, Jae-Hwan;Park, Cheol-Soo
    • Journal of the Korean Society of Radiology
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    • v.5 no.5
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    • pp.255-260
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    • 2011
  • We wanted to evaluate the usefulness of three-dimensional reconstructive images using computed tomography for rib fracture patients. The reconstruction used in clinical multi planar reformation(MPR), volume rendering technique(VRT), and image data using quantitative methods and qualitative methods were compared. Much more, the artifact shadow was minimized to reconstruct with 3D volumetric image by using an law data in the analysis of the reconstructive image and chest CT scan of the evaluation result fractures of the thoracic patient. And we could know that the fractures of the thoracic determination and three dimension volume image reconstruction time were reduced.

Clinical Feasibility of Dual-Layer CT With Virtual Monochromatic Image for Preoperative Staging in Patients With Breast Cancer: A Comparison With Breast MRI

  • Bokdong Yeo;Kyung Min Shin;Byunggeon Park;Hye Jung Kim;Won Hwa Kim
    • Korean Journal of Radiology
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    • v.25 no.9
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    • pp.798-806
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    • 2024
  • Objective: Dual-layer CT (DLCT) can create virtual monochromatic images (VMIs) at various monochromatic X-ray energies, particularly at low keV levels, with high contrast-to-noise ratio. The purpose of this study was to assess the clinical feasibility of contrast-enhanced chest DLCT with a low keV VMI for preoperative breast cancer staging, in comparison to breast MRI. Materials and Methods: A total of 152 patients with 155 index breast cancers were enrolled in the study. VMIs were generated from contrast-enhanced chest DLCT at 40 keV and maximum intensity projection (MIP) with three-dimensional (3D) reconstruction was performed for both bilateral breast areas. Two radiologists reviewed in consensus the 3D MIP images of the chest DLCT with VMI and breast MRI in separate sessions with a 3-month wash-out period. The detection rate and mean tumor size of the index cancer were compared between the chest DLCT with VMI and breast MRI. Additionally, the agreement of tumor size measurement between the two imaging modalities were evaluated. Results: Of all index cancers, 84.5% (131/155) were detected in the chest DLCT with VMI, while 88.4% (137/155) were detected in the breast MRI (P = 0.210). The Bland-Altman agreement between the chest DLCT with VMI and breast MRI was a mean difference of -0.05 cm with 95% limits of agreement of -1.29 to 1.19 cm. The tumor size in the chest DLCT with VMI (2.3 ± 1.7 cm) was not significantly different from that in the breast MRI (2.4 ± 1.6 cm) (P = 0.106). Conclusion: The feasibility of chest DLCT with VMI was demonstrated for preoperative tumor staging in breast cancer patients, showing comparable cancer detectability and good agreement in tumor size measurement compared to breast MRI. This suggests that chest DLCT with VMI can serve as a potential alternative for patients who have contraindications to breast MRI.