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      Application of Vendor-Neutral Iterative Reconstruction Technique to Pediatric Abdominal Computed Tomography

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          Abstract

          Objective

          To compare image qualities between vendor-neutral and vendor-specific hybrid iterative reconstruction (IR) techniques for abdominopelvic computed tomography (CT) in young patients.

          Materials and Methods

          In phantom study, we used an anthropomorphic pediatric phantom, age-equivalent to 5-year-old, and reconstructed CT data using traditional filtered back projection (FBP), vendor-specific and vendor-neutral IR techniques (ClariCT; ClariPI) in various radiation doses. Noise, low-contrast detectability and subjective spatial resolution were compared between FBP, vendor-specific (i.e., iDose1 to 5; Philips Healthcare), and vendor-neutral (i.e., ClariCT1 to 5) IR techniques in phantom. In 43 patients (median, 14 years; age range 1–19 years), noise, contrast-to-noise ratio (CNR), and qualitative image quality scores of abdominopelvic CT were compared between FBP, iDose level 4 (iDose4), and ClariCT level 2 (ClariCT2), which showed most similar image quality to clinically used vendor-specific IR images (i.e., iDose4) in phantom study. Noise, CNR, and qualitative imaging scores were compared using one-way repeated measure analysis of variance.

          Results

          In phantom study, ClariCT2 showed noise level similar to iDose4 (14.68–7.66 Hounsfield unit [HU] vs. 14.78–6.99 HU at CT dose index volume range of 0.8–3.8 mGy). Subjective low-contrast detectability and spatial resolution were similar between ClariCT2 and iDose4. In clinical study, ClariCT2 was equivalent to iDose4 for noise (14.26–17.33 vs. 16.01–18.90) and CNR (3.55–5.24 vs. 3.20–4.60) ( p > 0.05). For qualitative imaging scores, the overall image quality ([reader 1, reader 2]; 2.74 vs. 2.07, 3.02 vs. 2.28) and noise (2.88 vs. 2.23, 2.93 vs. 2.33) of ClariCT2 were superior to those of FBP ( p < 0.05), and not different from those of iDose4 (2.74 vs. 2.72, 3.02 vs. 2.98; 2.88 vs. 2.77, 2.93 vs. 2.86) ( p > 0.05).

          Conclusion

          Vendor-neutral IR technique shows image quality similar to that of clinically used vendor-specific hybrid IR technique for abdominopelvic CT in young patients.

          Related collections

          Most cited references 27

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          Iterative reconstruction methods in X-ray CT.

          Iterative reconstruction (IR) methods have recently re-emerged in transmission x-ray computed tomography (CT). They were successfully used in the early years of CT, but given up when the amount of measured data increased because of the higher computational demands of IR compared to analytical methods. The availability of large computational capacities in normal workstations and the ongoing efforts towards lower doses in CT have changed the situation; IR has become a hot topic for all major vendors of clinical CT systems in the past 5 years. This review strives to provide information on IR methods and aims at interested physicists and physicians already active in the field of CT. We give an overview on the terminology used and an introduction to the most important algorithmic concepts including references for further reading. As a practical example, details on a model-based iterative reconstruction algorithm implemented on a modern graphics adapter (GPU) are presented, followed by application examples for several dedicated CT scanners in order to demonstrate the performance and potential of iterative reconstruction methods. Finally, some general thoughts regarding the advantages and disadvantages of IR methods as well as open points for research in this field are discussed. Copyright © 2012 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
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            Iterative reconstruction techniques for computed tomography Part 1: technical principles.

            To explain the technical principles of and differences between commercially available iterative reconstruction (IR) algorithms for computed tomography (CT) in non-mathematical terms for radiologists and clinicians. Technical details of the different proprietary IR techniques were distilled from available scientific articles and manufacturers' white papers and were verified by the manufacturers. Clinical results were obtained from a literature search spanning January 2006 to January 2012, including only original research papers concerning IR for CT. IR for CT iteratively reduces noise and artefacts in either image space or raw data, or both. Reported dose reductions ranged from 23 % to 76 % compared to locally used default filtered back-projection (FBP) settings, with similar noise, artefacts, subjective, and objective image quality. IR has the potential to allow reducing the radiation dose while preserving image quality. Disadvantages of IR include blotchy image appearance and longer computational time. Future studies need to address differences between IR algorithms for clinical low-dose CT. • Iterative reconstruction technology for CT is presented in non-mathematical terms. • IR reduces noise and artefacts compared to filtered back-projection. • IR can improve image quality in routine-dose CT and lower the radiation dose. • IR's disadvantages include longer computation and blotchy appearance of some images.
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              • Article: not found

              CT radiation dose: current controversies and dose reduction strategies.

              The purpose of this article is to use clinical scenarios to explore aspects of ionizing radiation imparted to patients undergoing CT examinations. Examination appropriateness, effective doses, cancer risks, and pertinent dose reduction strategies are reviewed.
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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
                September 2019
                23 August 2019
                : 20
                : 9
                : 1358-1367
                Affiliations
                [1 ]Department of Radiology, Seoul National University Hospital, Seoul, Korea.
                [2 ]Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
                [3 ]Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea.
                [4 ]Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Korea.
                [5 ]Advanced Institute of Convergence Technology, Suwon, Korea.
                Author notes
                Corresponding author: Young Hun Choi, MD, Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Korea. Tel: (822) 2072-3600, Fax: (822) 747-5781, iater@ 123456snu.ac.kr
                Article
                10.3348/kjr.2018.0715
                6715563
                31464114
                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 ( http://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.

                Funding
                Funded by: Korea Health Industry Development Institute, CrossRef https://doi.org/10.13039/501100003710;
                Award ID: HI15C1532
                Funded by: Institute for Information and communications Technology Promotion, CrossRef https://doi.org/10.13039/501100010418;
                Award ID: 2017-0-01329
                Categories
                Pediatric Imaging
                Original Article

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