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Ho TT, Tran MT, Cui X, Lin CL, Baek S, Kim WJ, Lee CH, Jin GY, Chae KJ, Choi S. Human-airway surface mesh smoothing based on graph convolutional neural networks. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 246:108061. [PMID: 38341897 DOI: 10.1016/j.cmpb.2024.108061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/22/2024] [Accepted: 02/05/2024] [Indexed: 02/13/2024]
Abstract
BACKGROUND AND OBJECTIVE A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations. METHOD The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties. RESULTS In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method. CONCLUSIONS The airway model constructed by the AMSL method reproduces branch diameters accurately without any shrinkage, especially in the case of smaller airways. The accurate estimation of airway geometry using a smoothing method is critical for estimating flow properties in CFD simulations.
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Affiliation(s)
- Thao Thi Ho
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Minh Tam Tran
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, South Korea
| | - Xinguang Cui
- School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Ching-Long Lin
- Department of Mechanical Engineering, IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA
| | - Stephen Baek
- School of Data Science, University of Virginia, Charlottesville, VA, USA; Department of Mechanical and Aerospace Engineering, University of Virginia, Charlottesville, VA, USA
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, School of Medicine, Kangwon National University Hospital, Kangwon National University, Chuncheon, South Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, South Korea.
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Tanabe N, Sato S, Shimada T, Kaji S, Shiraishi Y, Terada S, Maetani T, Mochizuki F, Shimizu K, Suzuki M, Chubachi S, Terada K, Tanimura K, Sakamoto R, Oguma T, Sato A, Kanasaki M, Muro S, Masuda I, Iijima H, Hirai T. A reference equation for lung volume on computed tomography in Japanese middle-aged and elderly adults. Respir Investig 2024; 62:121-127. [PMID: 38101279 DOI: 10.1016/j.resinv.2023.12.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/06/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023]
Abstract
BACKGROUND Effective use of lung volume data measured on computed tomography (CT) requires reference values for specific populations. This study examined whether an equation previously generated for multiple ethnic groups in the United States, including Asians predominantly composed of Chinese people, in the Multi-Ethnic Study of Atherosclerosis (MESA) could be used for Japanese people and, if necessary, to optimize this equation. Moreover, the equation was used to characterize patients with chronic obstructive pulmonary disease (COPD) and lung hyperexpansion. METHODS This study included a lung cancer screening CT cohort of asymptomatic never smokers aged ≥40 years from two institutions (n = 364 and 419) to validate and optimize the MESA equation and a COPD cohort (n = 199) to test its applicability. RESULTS In all asymptomatic never smokers, the variance explained by the predicted values (R2) based on the original MESA equation was 0.60. The original equation was optimized to minimize the root mean squared error (RMSE) by adjusting the scaling factor but not the age, sex, height, or body mass index terms of the equation. The RMSE changed from 714 ml in the original equation to 637 ml in the optimized equation. In the COPD cohort, lung hyperexpansion, defined based on the 95th percentile of the ratio of measured lung volume to predicted lung volume in never smokers (122 %), was observed in 60 (30 %) patients and was associated with centrilobular emphysema and air trapping on inspiratory/expiratory CT. CONCLUSIONS The MESA equation was optimized for Japanese middle-aged and elderly adults.
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Affiliation(s)
- Naoya Tanabe
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan.
| | - Susumu Sato
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan; Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Takafumi Shimada
- Department of Respiratory Medicine, Tsukuba Medical Center, Ibaraki, Japan
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
| | - Yusuke Shiraishi
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Satoru Terada
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan; Terada Clinic, Respiratory Medicine and General Practice, Himeji, Hyogo, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Fumi Mochizuki
- Department of Respiratory Medicine, Tsukuba Medical Center, Ibaraki, Japan
| | - Kaoruko Shimizu
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Masaru Suzuki
- Department of Respiratory Medicine, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Shotaro Chubachi
- Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
| | - Kunihiko Terada
- Terada Clinic, Respiratory Medicine and General Practice, Himeji, Hyogo, Japan
| | - Kazuya Tanimura
- Department of Respiratory Medicine, Nara Medical University, Kashihara, Nara, Japan
| | - Ryo Sakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Atsuyasu Sato
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - Shigeo Muro
- Department of Respiratory Medicine, Nara Medical University, Kashihara, Nara, Japan
| | - Izuru Masuda
- Medical Examination Center, Takeda Hospital, Kyoto, Japan
| | - Hiroaki Iijima
- Department of Respiratory Medicine, Tsukuba Medical Center, Ibaraki, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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Baradaran Mahdavi MM, Rafati M, Ghanei M, Arabfard M. Computer-assisted evaluation of small airway disease in CT scans of Iran-Iraq war victims of chemical warfare by a locally developed software: comparison between different quantitative methods. BMC Med Imaging 2023; 23:165. [PMID: 37872482 PMCID: PMC10594688 DOI: 10.1186/s12880-023-01114-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2022] [Accepted: 09/29/2023] [Indexed: 10/25/2023] Open
Abstract
OBJECTIVE Diagnosis of small airway disease on computed tomography (CT) scans is challenging in patients with a history of chemical warfare exposure. We developed a software package based on different methodologies to identify and quantify small airway disease in CT images. The primary aim was to identify the best automatic methodology for detecting small airway disease in CT scans of Iran-Iraq War victims of chemical warfare. METHODS This retrospective case-control study enrolled 46 patients with a history of chemical warfare exposure and 27 controls with inspiratory/expiratory (I/E) CT scans and spirometry tests. Image data were automatically segmented, and inspiratory images were registered into the expiratory images' frame using the locally developed software. Parametric response mapping (PRM) and air trapping index (ATI) mapping were performed on the CT images. Conventional QCT methods, including expiratory/inspiratory mean lung attenuation (E/I MLA) ratio, normal density E/I (ND E/I) MLA ratio, attenuation volume Index (AVI), %low attenuation areas (LAA) < -856 in exhale scans, and %LAA < -950 in inhale scans were also computed. QCT measurements were correlated with spirometry results and compared across the two study groups. RESULTS The correlation analysis showed a significant negative relationship between three air trapping (AT) measurements (PRM, ATI, and %LAAExp < -856) and spirometry parameters (Fev1, Fvc, Fev1/Fvc, and MMEF). Moreover, %LAAExp < -856 had the highest significant negative correlation with Fev1/Fvc (r = -0.643, P-value < 0.001). Three AT measurements demonstrated a significant difference between the study groups. The E/I ratio was also significantly different between the two groups (P-value < 0.001). Binary logistic regression models showed PRMFsad, %LAAExp < -856, and ATI as significant and strong predictors of the study outcome. Optimal cut-points for PRMFsad = 19%, %LAAExp < -856 = 23%, and ATI = 27% were identified to classify the participants into two groups with high accuracy. CONCLUSION QCT methods, including PRM, ATI, and %LAAExp < -856 can greatly advance the identification and quantification of SAD in chemical warfare victims. The results should be verified in well-designed prospective studies involving a large population.
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Affiliation(s)
- Mohammad Mehdi Baradaran Mahdavi
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran
| | - Mehravar Rafati
- Department of Medical Physics and Radiology, Faculty of Paramedicine, Kashan University of Medical Sciences, Kashan, Iran
| | - Mostafa Ghanei
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
| | - Masoud Arabfard
- Chemical Injuries Research Center, Systems Biology and Poisonings Institute, Baqiyatallah University of Medical Sciences, Tehran, Iran.
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Ho TT, Kim WJ, Lee CH, Jin GY, Chae KJ, Choi S. An unsupervised image registration method employing chest computed tomography images and deep neural networks. Comput Biol Med 2023; 154:106612. [PMID: 36738711 DOI: 10.1016/j.compbiomed.2023.106612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 01/11/2023] [Accepted: 01/28/2023] [Indexed: 02/04/2023]
Abstract
BACKGROUND Deformable image registration is crucial for multiple radiation therapy applications. Fast registration of computed tomography (CT) lung images is challenging because of the large and nonlinear deformation between inspiration and expiration. With advancements in deep learning techniques, learning-based registration methods are considered efficient alternatives to traditional methods in terms of accuracy and computational cost. METHOD In this study, an unsupervised lung registration network (LRN) with cycle-consistent training is proposed to align two acquired CT-derived lung datasets during breath-holds at inspiratory and expiratory levels without utilizing any ground-truth registration results. Generally, the LRN model uses three loss functions: image similarity, regularization, and Jacobian determinant. Here, LRN was trained on the CT datasets of 705 subjects and tested using 10 pairs of public CT DIR-Lab datasets. Furthermore, to evaluate the effectiveness of the registration technique, target registration errors (TREs) of the LRN model were compared with those of the conventional algorithm (sum of squared tissue volume difference; SSTVD) and a state-of-the-art unsupervised registration method (VoxelMorph). RESULTS The results showed that the LRN with an average TRE of 1.78 ± 1.56 mm outperformed VoxelMorph with an average TRE of 2.43 ± 2.43 mm, which is comparable to that of SSTVD with an average TRE of 1.66 ± 1.49 mm. In addition, estimating the displacement vector field without any folding voxel consumed less than 2 s, demonstrating the superiority of the learning-based method with respect to fiducial marker tracking and the overall soft tissue alignment with a nearly real-time speed. CONCLUSIONS Therefore, this proposed method shows significant potential for use in time-sensitive pulmonary studies, such as lung motion tracking and image-guided surgery.
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Affiliation(s)
- Thao Thi Ho
- School of Mechanical Engineering, Kyungpook National University, Daegu, South Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Chang Hyun Lee
- Department of Radiology, Seoul National University, College of Medicine, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University, Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, South Korea.
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Kim T, Lim MN, Kim WJ, Ho TT, Lee CH, Chae KJ, Bak SH, Jin GY, Park EK, Choi S. Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 837:155812. [PMID: 35550893 DOI: 10.1016/j.scitotenv.2022.155812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 04/13/2022] [Accepted: 05/05/2022] [Indexed: 06/15/2023]
Abstract
Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (Vair), tissue volume (Vtissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller Vair at TLC at all except the right upper and lower lobes; smaller Vtissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.
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Affiliation(s)
- Taewoo Kim
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Myoung-Nam Lim
- Biomedical Research Institute, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Thao Thi Ho
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gong Yong Jin
- Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, Republic of Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
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Dudurych I, Muiser S, McVeigh N, Kerstjens HAM, van den Berge M, de Bruijne M, Vliegenthart R. Bronchial wall parameters on CT in healthy never-smoking, smoking, COPD, and asthma populations: a systematic review and meta-analysis. Eur Radiol 2022; 32:5308-5318. [PMID: 35192013 PMCID: PMC9279249 DOI: 10.1007/s00330-022-08600-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Revised: 12/14/2021] [Accepted: 01/29/2022] [Indexed: 11/25/2022]
Abstract
Objective Research on computed tomography (CT) bronchial parameter measurements shows that there are conflicting results on the values for bronchial parameters in the never-smoking, smoking, asthma, and chronic obstructive pulmonary disease (COPD) populations. This review assesses the current CT methods for obtaining bronchial wall parameters and their comparison between populations. Methods A systematic review of MEDLINE and Embase was conducted following PRISMA guidelines (last search date 25th October 2021). Methodology data was collected and summarised. Values of percentage wall area (WA%), wall thickness (WT), summary airway measure (Pi10), and luminal area (Ai) were pooled and compared between populations. Results A total of 169 articles were included for methodologic review; 66 of these were included for meta-analysis. Most measurements were obtained from multiplanar reconstructions of segmented airways (93 of 169 articles), using various tools and algorithms; third generation airways in the upper and lower lobes were most frequently studied. COPD (12,746) and smoking (15,092) populations were largest across studies and mostly consisted of men (median 64.4%, IQR 61.5 – 66.1%). There were significant differences between populations; the largest WA% was found in COPD (mean SD 62.93 ± 7.41%, n = 6,045), and the asthma population had the largest Pi10 (4.03 ± 0.27 mm, n = 442). Ai normalised to body surface area (Ai/BSA) (12.46 ± 4 mm2, n = 134) was largest in the never-smoking population. Conclusions Studies on CT-derived bronchial parameter measurements are heterogenous in methodology and population, resulting in challenges to compare outcomes between studies. Significant differences between populations exist for several parameters, most notably in the wall area percentage; however, there is a large overlap in their ranges. Key Points • Diverse methodology in measuring airways contributes to overlap in ranges of bronchial parameters among the never-smoking, smoking, COPD, and asthma populations. • The combined number of never-smoking participants in studies is low, limiting insight into this population and the impact of participant characteristics on bronchial parameters. • Wall area percent of the right upper lobe apical segment is the most studied (87 articles) and differentiates all except smoking vs asthma populations. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08600-1.
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Affiliation(s)
- Ivan Dudurych
- Department of Radiology, EB49, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands
| | - Susan Muiser
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Niall McVeigh
- Department of Cardiothoracic Surgery, St. Vincent's University Hospital, Dublin, Ireland
| | - Huib A M Kerstjens
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Maarten van den Berge
- Department of Pulmonology, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands
| | - Marleen de Bruijne
- Department of Radiology and Nuclear Medicine, Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Rozemarijn Vliegenthart
- Department of Radiology, EB49, University Medical Centre Groningen, University of Groningen, Hanzeplein 1, 9700RB, Groningen, The Netherlands.
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Kim T, Kim WJ, Lee CH, Chae KJ, Bak SH, Kwon SO, Jin GY, Park EK, Choi S. Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique. Comput Biol Med 2021; 141:105162. [PMID: 34973583 DOI: 10.1016/j.compbiomed.2021.105162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 12/06/2021] [Accepted: 12/19/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND OBJECTIVE Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features. METHODS We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction. RESULTS The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively. CONCLUSIONS We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.
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Affiliation(s)
- Taewoo Kim
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea; Department of Radiology, College of Medicine, The University of Iowa, Iowa City, IA, USA
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - So Hyeon Bak
- Department of Radiology, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Sung Ok Kwon
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, Republic of Korea
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, Republic of Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
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Chae KJ, Jin GY, Choi J, Lee CH, Choi S, Choi H, Park J, Lin CL, Hoffman EA. Generation-based study of airway remodeling in smokers with normal-looking CT with normalization to control inter-subject variability. Eur J Radiol 2021; 138:109657. [PMID: 33773402 DOI: 10.1016/j.ejrad.2021.109657] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 02/01/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022]
Abstract
PURPOSE With the help of quantitative computed tomography (QCT), it is possible to identify smoking-associated airway remodeling. However, there is currently little information on whether QCT-based airway metrics are sensitive to early airway wall remodeling in subclinical phases of smoking-associated airway disease. This study aimed to evaluate a predictive model that normalized airway parameters and investigate structural airway alterations in smokers with normal-looking CT using the normalization scheme. METHODS In this retrospective analysis, 222 non-smokers (male 97, female 125) and 69 smokers (male 66, female 3) from January 2014 to December 2016 were included, and airway parameters were quantitatively analyzed. To control inter-subject variability, multiple linear regressions of tracheal wall thickness (WT), diameter (D), and luminal area (LA) were performed, adjusted for age, sex, and height. Using this normalization scheme, airway parameters with matched generation were compared between smokers and non-smokers. RESULTS Using the normalization scheme, it was possible to assess generation-based structural alterations of the airways in subclinical smokers. Smokers showed diffuse luminal narrowing of airways for most generations (P < 0.05, except 3rd generation), no change in wall thickness of the proximal bronchi (1st-3rd generation), and a thinning of distal airways (P <0.05, ≥4th generation). CONCLUSION QCT assessment for subclinical smokers can help identify minimal structural changes in airways induced by smoking.
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Affiliation(s)
- Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea.
| | - Jiwoong Choi
- Department of Internal Medicine, School of Medicine, University of Kansas, Kansas City, KS, USA; Department of Bioengineering, University of Kansas, Lawrence, KS, USA
| | - Chang Hyun Lee
- Department of Radiology, Seoul National University Hospital, Seoul, South Korea; Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul, South Korea
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, Daegu, South Korea
| | - Hyemi Choi
- Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Jeonbuk, South Korea
| | - Jeongjae Park
- Department of Statistics, Regional Cardiocerebrovascular Center, Wonkwang University School of Medicine, Iksan, Jeonbuk, South Korea
| | - Ching-Long Lin
- Department of Radiology & Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
| | - Eric A Hoffman
- Department of Radiology & Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, USA
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Song L, Leppig JA, Hubner RH, Lassen-Schmidt BC, Neumann K, Theilig DC, Feldhaus FW, Fahlenkamp UL, Hamm B, Song W, Jin Z, Doellinger F. Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function? Int J Chron Obstruct Pulmon Dis 2020; 15:1877-1886. [PMID: 32801683 PMCID: PMC7413697 DOI: 10.2147/copd.s253602] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Accepted: 07/02/2020] [Indexed: 11/23/2022] Open
Abstract
Purpose The aim of this retrospective study was to evaluate correlations between parameters of quantitative computed tomography (QCT) analysis, especially the 15th percentile of lung attenuation (P15), and parameters of clinical tests in a large group of patients with pulmonary emphysema. Patients and Methods One hundred and seventy-two patients with pulmonary emphysema and chronic obstructive pulmonary disease (COPD) global initiative for chronic obstructive lung disease (GOLD) stage 3 or 4 were assessed by nonenhanced thin-section CT scans in full inspiratory and expiratory breath-hold, pulmonary function test (PFT), a 6-minute walk test (6MWT), and quality of life questionnaires (SGRQ and CAT). QCT parameters included total lung volume (TLV), total emphysema score (TES), and P15, all measured at inspiration (IN) and expiration (EX). Differences between inspiration and expiration were calculated for TLV (TLVDiff), TES (TESDiff), and P15 (P15Diff). Spearman correlation analysis was performed. Results CT-measured lung volume in inspiration (TLVIN) correlated strongly with spirometry-measured total lung capacity (TLC) (r=0.81, p<0.001) and moderately to strongly with residual volume (RV), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV1)/FVC (r=0.60, 0.56, and −0.49, each p<0.001). Lung volume in expiration (TLVEX) correlated moderately to strongly with TLC, RV and FEV1/FVC ratio (r=0.75, 0.66, and −0.43, each p<0.001). TES and P15 showed stronger correlations with the carbon monoxide transfer coefficient (KCO%) (r= −0.42, 0.44, both p<0.001), when measured during expiration. P15Diff correlated moderately with KCO% and carbon monoxide diffusing capacity (DLCO%) (r= 0.41, 0.40, both p<0.001). The 6MWT and most QCT parameters showed significant differences between COPD GOLD 3 and 4 groups. Conclusion Our results suggest that QCT can help predict the severity of lung function decrease in patients with pulmonary emphysema and COPD GOLD 3 or 4. Some QCT parameters, including P15EX and P15Diff, correlated moderately to strongly with parameters of pulmonary function tests.
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Affiliation(s)
- Lan Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Jonas A Leppig
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf H Hubner
- Department of Internal Medicine/Infectious Diseases and Respiratory Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | - Konrad Neumann
- Institute of Biometrics and Clinical Epidemiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Dorothea C Theilig
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Felix W Feldhaus
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Ute L Fahlenkamp
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Zhengyu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China
| | - Felix Doellinger
- Department of Radiology, Charité Universitätsmedizin Berlin, Berlin, Germany
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From infancy to adulthood-Developmental changes in pulmonary quantitative computed tomography parameters. PLoS One 2020; 15:e0233622. [PMID: 32469974 PMCID: PMC7259551 DOI: 10.1371/journal.pone.0233622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 05/08/2020] [Indexed: 11/19/2022] Open
Abstract
Purpose Quantified computed tomography (qCT) is known for correlations with airflow obstruction and fibrotic changes of the lung. However, as qCT studies often focus on diseased and elderly subjects, current literature lacks physiological qCT values during body development. We evaluated chest CT examinations of a healthy cohort, reaching from infancy to adulthood, to determine physiological qCT values and changes during body development. Method Dose-optimized chest CT examinations performed over the last 3 years using a dual-source CT were retrospectively analysed. Exclusion criteria were age >30 years and any known or newly diagnosed lung pathology. Lung volume, mean lung density, full-width-at-half-maximum and low attenuated volume (LAV) were semi-automated quantified in 151 patients. qCT values between different age groups as well as unenhanced (Group 1) and contrast-enhanced (Group 2) protocols were compared. Models for projection of age-dependant changes in qCT values were fitted. Results Significant differences in qCT parameters were found between the age groups from 0 to 15 years (p < 0.05). All parameters except LAV merge into a plateau level above this age as shown by polynomial models (r2 between 0.85 and 0.67). In group 2, this plateau phase is shifted back around five years. Except for the volume, significant differences in all qCT values were found between group 1 and 2 (p < 0.01). Conclusion qCT parameters underly a specific age-dependant dynamic. Except for LAV, qCT parameters reach a plateau around adolescence. Contrast-enhanced protocols seem to shift this plateau backwards.
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Kim T, Cho HB, Kim WJ, Lee CH, Chae KJ, Choi SH, Lee KE, Bak SH, Kwon SO, Jin GY, Choi J, Park EK, Lin CL, Hoffman EA, Choi S. Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects. Respir Res 2020; 21:133. [PMID: 32471435 PMCID: PMC7260806 DOI: 10.1186/s12931-020-01399-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 05/19/2020] [Indexed: 11/24/2022] Open
Abstract
Background Dust exposure has been reported as a risk factor of pulmonary disease, leading to alterations of segmental airways and parenchymal lungs. This study aims to investigate alterations of quantitative computed tomography (QCT)-based airway structural and functional metrics due to cement-dust exposure. Methods To reduce confounding factors, subjects with normal spirometry without fibrosis, asthma and pneumonia histories were only selected, and a propensity score matching was applied to match age, sex, height, smoking status, and pack-years. Thus, from a larger data set (N = 609), only 41 cement dust-exposed subjects were compared with 164 non-cement dust-exposed subjects. QCT imaging metrics of airway hydraulic diameter (Dh), wall thickness (WT), and bifurcation angle (θ) were extracted at total lung capacity (TLC) and functional residual capacity (FRC), along with their deformation ratios between TLC and FRC. Results In TLC scan, dust-exposed subjects showed a decrease of Dh (airway narrowing) especially at lower-lobes (p < 0.05), an increase of WT (wall thickening) at all segmental airways (p < 0.05), and an alteration of θ at most of the central airways (p < 0.001) compared with non-dust-exposed subjects. Furthermore, dust-exposed subjects had smaller deformation ratios of WT at the segmental airways (p < 0.05) and θ at the right main bronchi and left main bronchi (p < 0.01), indicating airway stiffness. Conclusions Dust-exposed subjects with normal spirometry demonstrated airway narrowing at lower-lobes, wall thickening at all segmental airways, a different bifurcation angle at central airways, and a loss of airway wall elasticity at lower-lobes. The airway structural alterations may indicate different airway pathophysiology due to cement dusts.
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Affiliation(s)
- Taewoo Kim
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, South Korea
| | - Hyun Bin Cho
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, South Korea
| | - Woo Jin Kim
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Chang Hyun Lee
- Department of Radiology, College of Medicine, Seoul National University, Seoul, South Korea.,Department of Radiology, College of Medicine, The University of Iowa, Iowa City, Iowa, USA
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, Jeonju, South Korea
| | - So-Hyun Choi
- Department of Statistics, Kyungpook National University, Daegu, South Korea
| | - Kyeong Eun Lee
- Department of Statistics, Kyungpook National University, Daegu, South Korea
| | - So Hyeon Bak
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Sung Ok Kwon
- Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, School of Medicine, Kangwon National University, Chuncheon, South Korea
| | - Gong Yong Jin
- Department of Radiology, Research Institute of Clinical Medicine of Chonbuk National University-Biomedical Research Institute of Chonbuk National University Hospital, Jeonju, South Korea
| | - Jiwoong Choi
- IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa, USA
| | - Eun-Kee Park
- Department of Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, South Korea
| | - Ching-Long Lin
- IIHR-Hydroscience and Engineering, The University of Iowa, Iowa City, Iowa, USA
| | - Eric A Hoffman
- Department of Radiology, College of Medicine, The University of Iowa, Iowa City, Iowa, USA
| | - Sanghun Choi
- School of Mechanical Engineering, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, South Korea.
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Choe YH. Characteristics of Recent Articles Published in the Korean Journal of Radiology Based on the Citation Frequency. Korean J Radiol 2020; 21:1284. [PMID: 33236548 PMCID: PMC7689137 DOI: 10.3348/kjr.2020.1322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 11/05/2020] [Accepted: 11/05/2020] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yeon Hyeon Choe
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- HVSI Imaging Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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