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1.
Article in English | WPRIM | ID: wpr-919280

ABSTRACT

Objective@#The purpose of this study was to investigate the accuracy of one-step automated orthodontic diagnosis of skeletodental discrepancies using a convolutional neural network (CNN) and lateral cephalogram images with different qualities from nationwide multi-hospitals. @*Methods@#Among 2,174 lateral cephalograms, 1,993 cephalograms from two hospitals were used for training and internal test sets and 181 cephalograms from eight other hospitals were used for an external test set. They were divided into three classification groups according to anteroposterior skeletal discrepancies (Class I, II, and III), vertical skeletal discrepancies (normodivergent, hypodivergent, and hyperdivergent patterns), and vertical dental discrepancies (normal overbite, deep bite, and open bite) as a gold standard. Pre-trained DenseNet-169 was used as a CNN classifier model. Diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis, t-stochastic neighbor embedding (t-SNE), and gradientweighted class activation mapping (Grad-CAM). @*Results@#In the ROC analysis, the mean area under the curve and the mean accuracy of all classifications were high with both internal and external test sets (all, > 0.89 and > 0.80). In the t-SNE analysis, our model succeeded in creating good separation between three classification groups. Grad-CAM figures showed differences in the location and size of the focus areas between three classification groups in each diagnosis. @*Conclusions@#Since the accuracy of our model was validated with both internal and external test sets, it shows the possible usefulness of a one-step automated orthodontic diagnosis tool using a CNN model. However, it still needs technical improvement in terms of classifying vertical dental discrepancies.

2.
Article in English | WPRIM | ID: wpr-875256

ABSTRACT

Objective@#To assess the performance of content-based image retrieval (CBIR) of chest CT for diffuse interstitial lung disease (DILD). @*Materials and Methods@#The database was comprised by 246 pairs of chest CTs (initial and follow-up CTs within two years) from 246 patients with usual interstitial pneumonia (UIP, n = 100), nonspecific interstitial pneumonia (NSIP, n = 101), and cryptogenic organic pneumonia (COP, n = 45). Sixty cases (30-UIP, 20-NSIP, and 10-COP) were selected as the queries. The CBIR retrieved five similar CTs as a query from the database by comparing six image patterns (honeycombing, reticular opacity, emphysema, ground-glass opacity, consolidation and normal lung) of DILD, which were automatically quantified and classified by a convolutional neural network. We assessed the rates of retrieving the same pairs of query CTs, and the number of CTs with the same disease class as query CTs in top 1–5 retrievals. Chest radiologists evaluated the similarity between retrieved CTs and queries using a 5-scale grading system (5-almost identical; 4-same disease; 3-likelihood of same disease is half; 2-likely different; and 1-different disease). @*Results@#The rate of retrieving the same pairs of query CTs in top 1 retrieval was 61.7% (37/60) and in top 1–5 retrievals was 81.7% (49/60). The CBIR retrieved the same pairs of query CTs more in UIP compared to NSIP and COP (p = 0.008 and 0.002).On average, it retrieved 4.17 of five similar CTs from the same disease class. Radiologists rated 71.3% to 73.0% of the retrieved CTs with a similarity score of 4 or 5. @*Conclusion@#The proposed CBIR system showed good performance for retrieving chest CTs showing similar patterns for DILD.

3.
Korean Journal of Radiology ; : 1719-1729, 2021.
Article in English | WPRIM | ID: wpr-902492

ABSTRACT

Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

4.
Journal of Breast Cancer ; : 349-355, 2021.
Article in English | WPRIM | ID: wpr-899012

ABSTRACT

Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.

5.
Journal of Breast Cancer ; : 235-240, 2021.
Article in English | WPRIM | ID: wpr-898984

ABSTRACT

Tumor localization in patients receiving neoadjuvant chemotherapy (NACT) is challenging because substantial therapeutic remission of the original tumor after NACT is often noted.Currently, there is no guidance device that allows for an accurate estimation of the resection range in breast-conserving surgery after NACT. To increase the accuracy of tumor resection, we used a 3-dimensional-printed breast surgical guide based on magnetic resonance imaging (MRI) in the supine position for a breast cancer patient who underwent breast-conserving surgery after NACT. Using this device, the breast tumor with apparent therapeutic changes after NACT on imaging was successfully removed with clear resection margins by identifying the original tumor site in the affected breast. Irrespective of whether the residual tumor area after NACT is well defined, it is possible to confirm and target the tumor area on pre-NACT MRI using this device.

6.
Korean Journal of Radiology ; : 1719-1729, 2021.
Article in English | WPRIM | ID: wpr-894788

ABSTRACT

Objective@#Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD. @*Materials and Methods@#A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than -856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis. @*Results@#The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV 1) and FEV 1/forced vital capacity (FVC) (r = -0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV 1 and FEV 1/FVC (R2 = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R2 = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R2 = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM. @*Conclusion@#The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

7.
Journal of Breast Cancer ; : 349-355, 2021.
Article in English | WPRIM | ID: wpr-891308

ABSTRACT

Tumor localization is challenging in the context of ductal carcinoma in situ (DCIS) treated with breast-conserving surgery. Conventional localization methods are generally performed under the guidance of ultrasonography or mammography and are rarely performed with magnetic resonance imaging (MRI), which is more sensitive than the aforementioned modalities in detecting DCIS. Here, we report the application of MRI-based individualized 3-dimensional (3D)-printed breast surgical guides (BSGs) for patients with breast cancer.We successfully resected indeterminate and suspicious lesions that were only detected using preoperative MRI, and the final histopathologic results confirmed DCIS with clear resection margins. MRI guidance combined with 3D-printed BSGs can be used for DCIS localization, especially for lesions easily detectable using MRI only.

8.
Journal of Breast Cancer ; : 235-240, 2021.
Article in English | WPRIM | ID: wpr-891280

ABSTRACT

Tumor localization in patients receiving neoadjuvant chemotherapy (NACT) is challenging because substantial therapeutic remission of the original tumor after NACT is often noted.Currently, there is no guidance device that allows for an accurate estimation of the resection range in breast-conserving surgery after NACT. To increase the accuracy of tumor resection, we used a 3-dimensional-printed breast surgical guide based on magnetic resonance imaging (MRI) in the supine position for a breast cancer patient who underwent breast-conserving surgery after NACT. Using this device, the breast tumor with apparent therapeutic changes after NACT on imaging was successfully removed with clear resection margins by identifying the original tumor site in the affected breast. Irrespective of whether the residual tumor area after NACT is well defined, it is possible to confirm and target the tumor area on pre-NACT MRI using this device.

9.
Korean Journal of Radiology ; : 2073-2081, 2021.
Article in English | WPRIM | ID: wpr-918180

ABSTRACT

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.

10.
Article in English | WPRIM | ID: wpr-831666

ABSTRACT

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.

11.
Cancer Research and Treatment ; : 1103-1111, 2020.
Article | WPRIM | ID: wpr-831134

ABSTRACT

Purpose@#Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. @*Materials and Methods@#A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). @*Results@#The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. @*Conclusion@#In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting

12.
Korean Journal of Radiology ; : 1104-1113, 2020.
Article | WPRIM | ID: wpr-833583

ABSTRACT

Objective@#To assess the regional ventilation in patients with asthma-chronic obstructive pulmonary disease (COPD) overlapsyndrome (ACOS) using xenon-ventilation dual-energy CT (DECT), and to compare it to that in patients with COPD. @*Materials and Methods@#Twenty-one patients with ACOS and 46 patients with COPD underwent xenon-ventilation DECT. Theventilation abnormalities were visually determined to be 1) peripheral wedge/diffuse defect, 2) diffuse heterogeneous defect,3) lobar/segmental/subsegmental defect, and 4) no defect on xenon-ventilation maps. Emphysema index (EI), airway wallthickness (Pi10), and mean ventilation values in the whole lung, peripheral lung, and central lung areas were quantified andcompared between the two groups using the Student’s t test. @*Results@#Most patients with ACOS showed the peripheral wedge/diffuse defect (n = 14, 66.7%), whereas patients with COPDcommonly showed the diffuse heterogeneous defect and lobar/segmental/subsegmental defect (n = 21, 45.7% and n = 20,43.5%, respectively). The prevalence of ventilation defect patterns showed significant intergroup differences (p< 0.001). Thequantified ventilation values in the peripheral lung areas were significantly lower in patients with ACOS than in patients withCOPD (p= 0.045). The quantified Pi10 was significantly higher in patients with ACOS than in patients with COPD (p= 0.041);however, EI was not significantly different between the two groups. @*Conclusion@#The ventilation abnormalities on the visual and quantitative assessments of xenon-ventilation DECT differed betweenpatients with ACOS and patients with COPD. Xenon-ventilation DECT may demonstrate the different physiologic changes ofpulmonary ventilation in patients with ACOS and COPD.

13.
Article | WPRIM | ID: wpr-833540

ABSTRACT

Objective@#Patients with chronic obstructive pulmonary disease (COPD) are known to be at risk of osteoporosis. The purpose of this study was to evaluate the association between thoracic vertebral bone density measured on chest CT (DThorax) and clinical variables, including survival, in patients with COPD. @*Materials and Methods@#A total of 322 patients with COPD were selected from the Korean Obstructive Lung Disease (KOLD) cohort. DThorax was measured by averaging the CT values of three consecutive vertebral bodies at the level of the left main coronary artery with a round region of interest as large as possible within the anterior column of each vertebral body using an in-house software. Associations between DThorax and clinical variables, including survival, pulmonary function test (PFT) results, and CT densitometry, were evaluated. @*Results@#The median follow-up time was 7.3 years (range: 0.1–12.4 years). Fifty-six patients (17.4%) died. DThroax differed significantly between the different Global Initiative for Chronic Obstructive Lung Disease stages. DThroax correlated positively with body mass index (BMI), some PFT results, and the six-minute walk distance, and correlated negatively with the emphysema index (EI) (all p < 0.05). In the univariate Cox analysis, older age (hazard ratio [HR], 3.617; 95% confidence interval [CI], 2.119–6.173, p < 0.001), lower BMI (HR, 3.589; 95% CI, 2.122–6.071, p < 0.001), lower forced expiratory volume in one second (FEV1) (HR, 2.975; 95% CI, 1.682–5.262, p < 0.001), lower diffusing capacity of the lung for carbon monoxide corrected with hemoglobin (DLCO) (HR, 4.595; 95% CI, 2.665–7.924, p < 0.001), higher EI (HR, 3.722; 95% CI, 2.192–6.319, p < 0.001), presence of vertebral fractures (HR, 2.062; 95% CI, 1.154–3.683, p = 0.015), and lower DThorax (HR, 2.773; 95% CI, 1.620–4.746, p < 0.001) were significantly associated with all-cause mortality and lung-related mortality. In the multivariate Cox analysis, lower DThorax (HR, 1.957; 95% CI, 1.075–3.563, p = 0.028) along with older age, lower BMI, lower FEV1, and lower DLCO were independent predictors of all-cause mortality. @*Conclusion@#The thoracic vertebral bone density measured on chest CT demonstrated significant associations with the patients’ mortality and clinical variables of disease severity in the COPD patients included in KOLD cohort.

15.
Korean Journal of Radiology ; : 1275-1284, 2019.
Article in English | WPRIM | ID: wpr-760296

ABSTRACT

OBJECTIVE: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS AND METHODS: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes ( 100), time intervals to DWI, and DWI protocols. RESULTS: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). CONCLUSION: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.


Subject(s)
Brain Ischemia , Consensus , Diffusion , Ethics Committees, Research , Humans , Retrospective Studies
16.
Korean Journal of Radiology ; : 1216-1225, 2019.
Article in English | WPRIM | ID: wpr-760279

ABSTRACT

OBJECTIVE: The absence of collateral ventilation (CV) is crucial for effective bronchoscopic lung volume reduction (BLVR) with an endobronchial valve. Here, we assessed whether CT can predict the Chartis™ results. MATERIALS AND METHODS: This study included 69 patients (mean age: 70.9 ± 6.6 years; 66 [95.7%] males) who had undergone CT to assess BLVR eligibility. The Chartis™ system (Pulmonox Inc.) was used to check CV. Experienced thoracic radiologists independently determined the completeness of fissures on volumetric CT images. RESULTS: The comparison between the visual and quantitative analyses revealed that 5% defect criterion showed good agreement. The Chartis™ assessment was performed for 129 lobes; 11 (19.6%) of 56 lobes with complete fissures on CT showed positive CV, while this rate was significantly higher (40 of 49 lobes, i.e., 81.6%) for lobes with incomplete fissures. The size of the fissure defect did not affect the rate of CV. Of the patients who underwent BLVR, 22 of 24 patients (91.7%) with complete fissures and three of four patients with incomplete fissures (75%) achieved target lobe volume reduction (TLVR). CONCLUSION: The quantitative analysis of fissure shows that incomplete fissures increased the probability of CV on Chartis™, while the defect size did not affect the overall rates. TLVR could be achieved even in some patients with relatively large fissure defect, if they showed negative CV on Chartis™.


Subject(s)
Cone-Beam Computed Tomography , Emphysema , Humans , Lung , Pneumonectomy , Pulmonary Disease, Chronic Obstructive , Ventilation
17.
Article in Korean | WPRIM | ID: wpr-916776

ABSTRACT

Three-dimensional (3D) printing technology, with additive manufacturing, can aid in the production of various kinds of patient-specific medical devices and implants in medical fields, which cannot be covered by mass production systems for producing conventional devices/implants. The simulator-based medical image demonstrates the anatomical structure of the disease, which can be used for education, diagnosis, preparation of treatment plan and preoperative surgical guide, etc. The surgical guide is used as a patient-specific medical device for guiding incision, resection, insertion, and marking. As 3D printers can output materials that can be inserted into the human body, the patient-specific implant device that reflects the patient's anatomy and surgical plan could be of relevance. In addition, patient-specific aids, including gibs, splints, prostheses, and epitheses, could be used for a better outcome. Finally, bio-printing is also used to cultivate cells to produce functional artificial tissues.

18.
Article in Korean | WPRIM | ID: wpr-916774

ABSTRACT

MRI provides non-invasive and non-ionizing methods for the accurate anatomic depiction of the cardiovascular system. Based on the inherent flow sensitivity, MRI can be used to investigate hemodynamic features in patients with anatomical data within a single measurement. In particular, time-resolved and three-dimensional (3D) characterization of blood flow using 4D flow MRI has achieved considerable progress in recent years. The present article reviews the principle and procedures of 4D Flow MRI. Various fluid dynamic biomarkers for possible clinical usage are also described, including wall shear stress, turbulent kinetic energy, and relative pressure. Finally, this article provides an overview of the clinical applications of 4D Flow MRI in various cardiovascular regions.

19.
Article in English | WPRIM | ID: wpr-741432

ABSTRACT

OBJECTIVE: We aimed to evaluate correlations between computed tomography (CT) parameters and pulmonary function test (PFT) parameters according to disease severity in patients with chronic obstructive pulmonary disease (COPD), and to determine whether CT parameters can be used to predict PFT indices. MATERIALS AND METHODS: A total of 370 patients with COPD were grouped based on disease severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) I–IV criteria. Emphysema index (EI), air-trapping index, and airway parameters such as the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10) were measured using automatic segmentation software. Clinical characteristics including PFT results and quantitative CT parameters according to GOLD criteria were compared using ANOVA. The correlations between CT parameters and PFT indices, including the ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC) and FEV1, were assessed. To evaluate whether CT parameters can be used to predict PFT indices, multiple linear regression analyses were performed for all patients, Group 1 (GOLD I and II), and Group 2 (GOLD III and IV). RESULTS: Pulmonary function deteriorated with increase in disease severity according to the GOLD criteria (p < 0.001). Parenchymal attenuation parameters were significantly worse in patients with higher GOLD stages (p < 0.001), and Pi10 was highest for patients with GOLD III (4.41 ± 0.94 mm). Airway parameters were nonlinearly correlated with PFT results, and Pi10 demonstrated mild correlation with FEV1/FVC in patients with GOLD II and III (r = 0.16, p = 0.06 and r = 0.21, p = 0.04, respectively). Parenchymal attenuation parameters, airway parameters, EI, and Pi10 were identified as predictors of FEV1/FVC for the entire study sample and for Group 1 (R2 = 0.38 and 0.22, respectively; p < 0.001). However, only parenchymal attenuation parameter, EI, was identified as a predictor of FEV1/FVC for Group 2 (R2 = 0.37, p < 0.001). Similar results were obtained for FEV1. CONCLUSION: Airway and parenchymal attenuation parameters are independent predictors of pulmonary function in patients with mild COPD, whereas parenchymal attenuation parameters are dominant independent predictors of pulmonary function in patients with severe COPD.


Subject(s)
Emphysema , Forced Expiratory Volume , Humans , Linear Models , Pulmonary Disease, Chronic Obstructive , Respiratory Function Tests , Vital Capacity
20.
Article in English | WPRIM | ID: wpr-741397

ABSTRACT

OBJECTIVE: The aim of our study was to develop and validate a convolutional neural network (CNN) architecture to convert CT images reconstructed with one kernel to images with different reconstruction kernels without using a sinogram. MATERIALS AND METHODS: This retrospective study was approved by the Institutional Review Board. Ten chest CT scans were performed and reconstructed with the B10f, B30f, B50f, and B70f kernels. The dataset was divided into six, two, and two examinations for training, validation, and testing, respectively. We constructed a CNN architecture consisting of six convolutional layers, each with a 3 × 3 kernel with 64 filter banks. Quantitative performance was evaluated using root mean square error (RMSE) values. To validate clinical use, image conversion was conducted on 30 additional chest CT scans reconstructed with the B30f and B50f kernels. The influence of image conversion on emphysema quantification was assessed with Bland–Altman plots. RESULTS: Our scheme rapidly generated conversion results at the rate of 0.065 s/slice. Substantial reduction in RMSE was observed in the converted images in comparison with the original images with different kernels (mean reduction, 65.7%; range, 29.5–82.2%). The mean emphysema indices for B30f, B50f, converted B30f, and converted B50f were 5.4 ± 7.2%, 15.3 ± 7.2%, 5.9 ± 7.3%, and 16.8 ± 7.5%, respectively. The 95% limits of agreement between B30f and other kernels (B50f and converted B30f) ranged from −14.1% to −2.6% (mean, −8.3%) and −2.3% to 0.7% (mean, −0.8%), respectively. CONCLUSION: CNN-based CT kernel conversion shows adequate performance with high accuracy and speed, indicating its potential clinical use.


Subject(s)
Dataset , Emphysema , Ethics Committees, Research , Image Processing, Computer-Assisted , Machine Learning , Multidetector Computed Tomography , Retrospective Studies , Tomography, X-Ray Computed
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