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      CT Image Conversion among Different Reconstruction Kernels without a Sinogram by Using a Convolutional Neural Network

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          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.

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          Most cited references15

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          Statistical methods for assessing agreement between two methods of clinical measurement.

          In clinical measurement comparison of a new measurement technique with an established one is often needed to see whether they agree sufficiently for the new to replace the old. Such investigations are often analysed inappropriately, notably by using correlation coefficients. The use of correlation is misleading. An alternative approach, based on graphical techniques and simple calculations, is described, together with the relation between this analysis and the assessment of repeatability.
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            Deep Learning in Medical Imaging: General Overview

            The artificial neural network (ANN)–a machine learning technique inspired by the human neuronal synapse system–was introduced in the 1950s. However, the ANN was previously limited in its ability to solve actual problems, due to the vanishing gradient and overfitting problems with training of deep architecture, lack of computing power, and primarily the absence of sufficient data to train the computer system. Interest in this concept has lately resurfaced, due to the availability of big data, enhanced computing power with the current graphics processing units, and novel algorithms to train the deep neural network. Recent studies on this technology suggest its potentially to perform better than humans in some visual and auditory recognition tasks, which may portend its applications in medicine and healthcare, especially in medical imaging, in the foreseeable future. This review article offers perspectives on the history, development, and applications of deep learning technology, particularly regarding its applications in medical imaging.
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              Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes.

              To test the hypothesis-given the increasing emphasis on quantitative computed tomographic (CT) phenotypes of chronic obstructive pulmonary disease (COPD)-that a relationship exists between COPD exacerbation frequency and quantitative CT measures of emphysema and airway disease. This research protocol was approved by the institutional review board of each participating institution, and all participants provided written informed consent. One thousand two subjects who were enrolled in the COPDGene Study and met the GOLD (Global Initiative for Chronic Obstructive Lung Disease) criteria for COPD with quantitative CT analysis were included. Total lung emphysema percentage was measured by using the attenuation mask technique with a -950-HU threshold. An automated program measured the mean wall thickness and mean wall area percentage in six segmental bronchi. The frequency of COPD exacerbation in the prior year was determined by using a questionnaire. Statistical analysis was performed to examine the relationship of exacerbation frequency with lung function and quantitative CT measurements. In a multivariate analysis adjusted for lung function, bronchial wall thickness and total lung emphysema percentage were associated with COPD exacerbation frequency. Each 1-mm increase in bronchial wall thickness was associated with a 1.84-fold increase in annual exacerbation rate (P = .004). For patients with 35% or greater total emphysema, each 5% increase in emphysema was associated with a 1.18-fold increase in this rate (P = .047). Greater lung emphysema and airway wall thickness were associated with COPD exacerbations, independent of the severity of airflow obstruction. Quantitative CT can help identify subgroups of patients with COPD who experience exacerbations for targeted research and therapy development for individual phenotypes. © RSNA, 2011.
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                Author and article information

                Journal
                Korean J Radiol
                Korean J Radiol
                KJR
                Korean Journal of Radiology
                The Korean Society of Radiology
                1229-6929
                2005-8330
                February 2019
                28 December 2018
                : 20
                : 2
                : 295-303
                Affiliations
                [1 ]Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
                [2 ]Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
                Author notes
                Corresponding author: Joon Beom Seo, MD, Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505, Korea. Tel: (822) 3010-4400, Fax: (822) 476-4719, seojb@ 123456amc.seoul.kr

                *These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0001-7627-2000
                https://orcid.org/0000-0002-1380-6682
                https://orcid.org/0000-0003-0271-7884
                Article
                10.3348/kjr.2018.0249
                6342751
                30672169
                72195596-484d-4afb-bb5c-682f3a26fa50
                Copyright © 2019 The Korean Society of Radiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 19 April 2018
                : 07 August 2018
                Funding
                Funded by: Ministry of Trade, Industry and Energy, CrossRef https://doi.org/10.13039/501100003052;
                Award ID: 10072064
                Categories
                Thoracic Imaging
                Original Article

                Radiology & Imaging
                multidetector computed tomography,image reconstruction,machine learning,emphysema,cnn

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