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      The evolution of image reconstruction for CT—from filtered back projection to artificial intelligence

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          Abstract

          Abstract

          The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain.

          Key Points

          • Advanced CT reconstruction methods are indispensable in the current clinical setting.

          • IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT.

          • Artificial intelligence will potentially further increase the performance of reconstruction methods .

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          Most cited references96

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          Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013.

          In 2010, overweight and obesity were estimated to cause 3·4 million deaths, 3·9% of years of life lost, and 3·8% of disability-adjusted life-years (DALYs) worldwide. The rise in obesity has led to widespread calls for regular monitoring of changes in overweight and obesity prevalence in all populations. Comparable, up-to-date information about levels and trends is essential to quantify population health effects and to prompt decision makers to prioritise action. We estimate the global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013. We systematically identified surveys, reports, and published studies (n=1769) that included data for height and weight, both through physical measurements and self-reports. We used mixed effects linear regression to correct for bias in self-reports. We obtained data for prevalence of obesity and overweight by age, sex, country, and year (n=19,244) with a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs). Worldwide, the proportion of adults with a body-mass index (BMI) of 25 kg/m(2) or greater increased between 1980 and 2013 from 28·8% (95% UI 28·4-29·3) to 36·9% (36·3-37·4) in men, and from 29·8% (29·3-30·2) to 38·0% (37·5-38·5) in women. Prevalence has increased substantially in children and adolescents in developed countries; 23·8% (22·9-24·7) of boys and 22·6% (21·7-23·6) of girls were overweight or obese in 2013. The prevalence of overweight and obesity has also increased in children and adolescents in developing countries, from 8·1% (7·7-8·6) to 12·9% (12·3-13·5) in 2013 for boys and from 8·4% (8·1-8·8) to 13·4% (13·0-13·9) in girls. In adults, estimated prevalence of obesity exceeded 50% in men in Tonga and in women in Kuwait, Kiribati, Federated States of Micronesia, Libya, Qatar, Tonga, and Samoa. Since 2006, the increase in adult obesity in developed countries has slowed down. Because of the established health risks and substantial increases in prevalence, obesity has become a major global health challenge. Not only is obesity increasing, but no national success stories have been reported in the past 33 years. Urgent global action and leadership is needed to help countries to more effectively intervene. Bill & Melinda Gates Foundation. Copyright © 2014 Elsevier Ltd. All rights reserved.
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            Compressed sensing

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              Computerized transverse axial scanning (tomography). 1. Description of system.

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                Author and article information

                Contributors
                m.j.willemink@gmail.com
                Journal
                Eur Radiol
                Eur Radiol
                European Radiology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0938-7994
                1432-1084
                30 October 2018
                30 October 2018
                2019
                : 29
                : 5
                : 2185-2195
                Affiliations
                [1 ]ISNI 0000000419368956, GRID grid.168010.e, Department of Radiology, , Stanford University School of Medicine, ; 300 Pasteur Drive, Room M-039, Stanford, CA 94305-5105 USA
                [2 ]ISNI 0000000090126352, GRID grid.7692.a, Department of Radiology, , University Medical Center Utrecht, ; Utrecht, The Netherlands
                [3 ]ISNI 0000 0004 1936 8972, GRID grid.25879.31, Department of Radiology, Perelman School of Medicine, , University of Pennsylvania, ; Philadelphia, PA USA
                [4 ]ISNI 0000000123222966, GRID grid.6936.a, Department of Diagnostic and Interventional Radiology, , Technische Universität München, ; Munich, Germany
                Author information
                http://orcid.org/0000-0002-6991-6557
                Article
                5810
                10.1007/s00330-018-5810-7
                6443602
                30377791
                6dcde5dd-5bd2-4131-8ae8-85f9ddc25dbb
                © The Author(s) 2018

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 24 July 2018
                : 12 September 2018
                : 27 September 2018
                Funding
                Funded by: German Department of Education and Research (BMBF)
                Award ID: 13GW0072C
                Award Recipient :
                Funded by: German Research Foundation (DFG)
                Award ID: GRK 2274
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000968, American Heart Association;
                Award ID: 18POST34030192
                Award Recipient :
                Funded by: Stanford-Philips Fellowship Training Award
                Award ID: NA
                Award Recipient :
                Categories
                Computed Tomography
                Custom metadata
                © European Society of Radiology 2019

                Radiology & Imaging
                tomography, x-ray,image reconstruction,artificial intelligence
                Radiology & Imaging
                tomography, x-ray, image reconstruction, artificial intelligence

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