{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T05:58:12Z","timestamp":1776923892333,"version":"3.51.2"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T00:00:00Z","timestamp":1706486400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82271934"],"award-info":[{"award-number":["82271934"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Yangfan Project of Science and Technology Commission of Shanghai Municipality","award":["22YF1442400"],"award-info":[{"award-number":["22YF1442400"]}]},{"name":"Medicine and Engineering Combination Project of Shanghai Jiao Tong University","award":["YG2021QN08"],"award-info":[{"award-number":["YG2021QN08"]}]},{"name":"Research Fund of Tongren Hospital, Shanghai Jiao Tong University School of Medicine","award":["TRKYRC-XX202204"],"award-info":[{"award-number":["TRKYRC-XX202204"]}]},{"name":"Guangci Innovative Technology Launch Plan of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine","award":["2022-13"],"award-info":[{"award-number":["2022-13"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10\u00a0mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC\u2009&gt;\u20090.90 and CCC\u2009&gt;\u20090.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC\u2009&gt;\u20090.90 and CCC\u2009&gt;\u20090.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.<\/jats:p>","DOI":"10.1007\/s10278-023-00901-1","type":"journal-article","created":{"date-parts":[[2024,1,29]],"date-time":"2024-01-29T18:02:35Z","timestamp":1706551355000},"page":"123-133","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9817-2294","authenticated-orcid":false,"given":"Jingyu","family":"Zhong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyun","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianying","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaomeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianxing","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haipeng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6612-8520","authenticated-orcid":false,"given":"Weiwu","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"issue":"4","key":"901_CR1","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441-446. https:\/\/doi.org\/10.1016\/j.ejca.2011.11.036","journal-title":"Eur J Cancer"},{"issue":"2","key":"901_CR2","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563-577. https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"901_CR3","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","volume":"14","author":"JP O'Connor","year":"2017","unstructured":"O'Connor JP, Aboagye EO, Adams JE et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169-186. https:\/\/doi.org\/10.1038\/nrclinonc.2016.162","journal-title":"Nat Rev Clin Oncol"},{"issue":"12","key":"901_CR4","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749-762. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141","journal-title":"Nat Rev Clin Oncol"},{"issue":"2","key":"901_CR5","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1038\/s41571-022-00707-0","volume":"20","author":"EP Huang","year":"2023","unstructured":"Huang EP, O'Connor JPB, McShane LM et al (2023) Criteria for the translation of radiomics into clinically useful tests. Nat Rev Clin Oncol. 2023 Feb;20(2):69-82. https:\/\/doi.org\/10.1038\/s41571-022-00707-0","journal-title":"Nat Rev Clin Oncol."},{"issue":"1","key":"901_CR6","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13244-020-00887-2","volume":"11","author":"JE van Timmeren","year":"2020","unstructured":"van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging-\u201chow-to\u201d guide and critical reflection. Insights Imaging 11(1):91. https:\/\/doi.org\/10.1186\/s13244-020-00887-2","journal-title":"Insights Imaging"},{"issue":"7","key":"901_CR7","doi-asserted-by":"publisher","first-page":"1124","DOI":"10.3348\/kjr.2018.0070","volume":"20","author":"JE Park","year":"2019","unstructured":"Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20(7):1124-1137. https:\/\/doi.org\/10.3348\/kjr.2018.0070","journal-title":"Korean J Radiol"},{"issue":"13","key":"901_CR8","doi-asserted-by":"publisher","first-page":"2638","DOI":"10.1007\/s00259-019-04391-8","volume":"46","author":"A Zwanenburg","year":"2019","unstructured":"Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46(13):2638-2655. https:\/\/doi.org\/10.1007\/s00259-019-04391-8","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"901_CR9","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1186\/s42492-019-0025-6","volume":"2","author":"R Cattell","year":"2019","unstructured":"Cattell R, Chen S, Huang C (2019) Robustness of radiomic features in magnetic resonance imaging: review and a phantom study. Vis Comput Ind Biomed Art 2(1):19. https:\/\/doi.org\/10.1186\/s42492-019-0025-6","journal-title":"Vis Comput Ind Biomed Art"},{"key":"901_CR10","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.phro.2021.10.007","volume":"20","author":"E Pfaehler","year":"2021","unstructured":"Pfaehler E, Zhovannik I, Wei L et al (2021) A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features. Phys Imaging Radiat Oncol 20:69-75. https:\/\/doi.org\/10.1016\/j.phro.2021.10.007","journal-title":"Phys Imaging Radiat Oncol"},{"issue":"2","key":"901_CR11","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1148\/radiol.2018172361","volume":"288","author":"R Berenguer","year":"2018","unstructured":"Berenguer R, Pastor-Juan MDR, Canales-V\u00e1zquez J et al (2018) Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology 288(2):407\u2013415. https:\/\/doi.org\/10.1148\/radiol.2018172361","journal-title":"Radiology"},{"issue":"3","key":"901_CR12","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1148\/radiol.2019190928","volume":"293","author":"M Meyer","year":"2019","unstructured":"Meyer M, Ronald J, Vernuccio F et al (2019) Reproducibility of CT radiomic features within the same patient: influence of radiation dose and CT reconstruction settings. Radiology 293(3):583-591. https:\/\/doi.org\/10.1148\/radiol.2019190928","journal-title":"Radiology"},{"issue":"8","key":"901_CR13","doi-asserted-by":"publisher","first-page":"5480","DOI":"10.1007\/s00330-022-08628-3","volume":"32","author":"Y Chen","year":"2022","unstructured":"Chen Y, Zhong J, Wang L et al (2022) Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT. Eur Radiol 32(8):5480-5490. https:\/\/doi.org\/10.1007\/s00330-022-08628-3","journal-title":"Eur Radiol"},{"issue":"2","key":"901_CR14","doi-asserted-by":"publisher","first-page":"812","DOI":"10.1007\/s00330-022-09119-1","volume":"33","author":"J Zhong","year":"2023","unstructured":"Zhong J, Xia Y, Chen Y et al (2023) Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study. Eur Radiol 33(2):812-824. https:\/\/doi.org\/10.1007\/s00330-022-09119-1","journal-title":"Eur Radiol"},{"issue":"1","key":"901_CR15","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1186\/s13244-023-01426-5","volume":"14","author":"J Zhong","year":"2023","unstructured":"Zhong J, Pan Z, Chen Y et al (2023) Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability. Insights Imaging 14(1):79. https:\/\/doi.org\/10.1186\/s13244-023-01426-5","journal-title":"Insights Imaging"},{"issue":"3","key":"901_CR16","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s00330-021-08249-2","volume":"32","author":"S Lennartz","year":"2022","unstructured":"Lennartz S, O'Shea A, Parakh A, Persigehl T, Baessler B, Kambadakone A (2022) Robustness of dual-energy CT-derived radiomic features across three different scanner types. Eur Radiol 32(3):1959-1970. https:\/\/doi.org\/10.1007\/s00330-021-08249-2","journal-title":"Eur Radiol"},{"issue":"4","key":"901_CR17","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1097\/RLI.0000000000000834","volume":"57","author":"X Peng","year":"2022","unstructured":"Peng X, Yang S, Zhou L et al (2022) Repeatability and reproducibility of computed tomography radiomics for pulmonary nodules: a multicenter phantom study. Invest Radiol 57(4):242-253. https:\/\/doi.org\/10.1097\/RLI.0000000000000834","journal-title":"Invest Radiol"},{"issue":"10","key":"901_CR18","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1007\/s00261-018-1527-y","volume":"43","author":"V Baliyan","year":"2018","unstructured":"Baliyan V, Kordbacheh H, Parameswaran B, Ganeshan B, Sahani D, Kambadakone A (2018) Virtual monoenergetic imaging in rapid kVp-switching dual-energy CT (DECT) of the abdomen: impact on CT texture analysis. Abdom Radiol (NY) 43(10):2693- 2701. https:\/\/doi.org\/10.1007\/s00261-018-1527-y","journal-title":"Abdom Radiol (NY)"},{"issue":"18","key":"901_CR19","doi-asserted-by":"publisher","first-page":"4710","DOI":"10.3390\/cancers13184710","volume":"13","author":"A Euler","year":"2021","unstructured":"Euler A, Laqua FC, Cester D et al (2021) Virtual monoenergetic images of dual-energy ct-impact on repeatability, reproducibility, and classification in radiomics. Cancers (Basel) 13(18):4710. https:\/\/doi.org\/10.3390\/cancers13184710","journal-title":"Cancers (Basel)"},{"issue":"2","key":"901_CR20","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1148\/radiol.2015132766","volume":"276","author":"LL Geyer","year":"2015","unstructured":"Geyer LL, Schoepf UJ, Meinel FG et al (2015) State of the art: iterative CT reconstruction techniques. Radiology 276(2):339-357. https:\/\/doi.org\/10.1148\/radiol.2015132766","journal-title":"Radiology"},{"issue":"5","key":"901_CR21","doi-asserted-by":"publisher","first-page":"2185","DOI":"10.1007\/s00330-018-5810-7","volume":"29","author":"MJ Willemink","year":"2019","unstructured":"Willemink MJ, No\u00ebl PB (2019) The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur Radiol 29(5):2185-2195. https:\/\/doi.org\/10.1007\/s00330-018-5810-7","journal-title":"Eur Radiol"},{"key":"901_CR22","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.ejmp.2020.11.012","volume":"79","author":"R Singh","year":"2020","unstructured":"Singh R, Wu W, Wang G, Kalra MK (2020) Artificial intelligence in image reconstruction: the change is here. Phys Med 79:113-125. https:\/\/doi.org\/10.1016\/j.ejmp.2020.11.012","journal-title":"Phys Med"},{"issue":"1","key":"901_CR23","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s00330-019-06359-6","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Frandon J, Larbi A, Beregi JP, Pereira F (2020) CT iterative reconstruction algorithms: a task-based image quality assessment. Eur Radiol 30(1):487-500. https:\/\/doi.org\/10.1007\/s00330-019-06359-6","journal-title":"Eur Radiol"},{"issue":"7","key":"901_CR24","doi-asserted-by":"publisher","first-page":"3951","DOI":"10.1007\/s00330-020-06724-w","volume":"30","author":"J Greffier","year":"2020","unstructured":"Greffier J, Hamard A, Pereira F et al (2020) Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study. Eur Radiol 30(7):3951-3959. https:\/\/doi.org\/10.1007\/s00330-020-06724-w","journal-title":"Eur Radiol"},{"key":"901_CR25","doi-asserted-by":"publisher","unstructured":"Greffier J, Si-Mohamed S, Guiu B et al (2022) Comparison of virtual monoenergetic imaging between a rapid kilovoltage switching dual-energy computed tomography with deep-learning and four dual-energy CTs with iterative reconstruction. Quant Imaging Med Surg 12(2):1149\u20131162. https:\/\/doi.org\/10.21037\/qims-21-708","DOI":"10.21037\/qims-21-708"},{"key":"901_CR26","doi-asserted-by":"publisher","first-page":"110198","DOI":"10.1016\/j.ejrad.2022.110198","volume":"149","author":"S Masuda","year":"2022","unstructured":"Masuda S, Yamada Y, Minamishima K, Owaki Y, Yamazaki A, Jinzaki M (2022) Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction. Eur J Radiol 149:110198. https:\/\/doi.org\/10.1016\/j.ejrad.2022.110198","journal-title":"Eur J Radiol"},{"key":"901_CR27","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1007\/s10278-023-00806-z","volume":"36","author":"J Zhong","year":"2023","unstructured":"Zhong J, Shen H, Chen Y et al (2023) Evaluation of image quality and detectability of deep learning image reconstruction (DLIR) algorithm in single- and dual-energy CT. J Digit Imaging 36:1390-1407. https:\/\/doi.org\/10.1007\/s10278-023-00806-z","journal-title":"J Digit Imaging"},{"issue":"8","key":"901_CR28","doi-asserted-by":"publisher","first-page":"5331","DOI":"10.1007\/s00330-023-09556-6","volume":"33","author":"J Zhong","year":"2023","unstructured":"Zhong J, Wang L, Shen H et al (2023) Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers. Eur Radiol 33(8):5331-5343. https:\/\/doi.org\/10.1007\/s00330-023-09556-6","journal-title":"Eur Radiol"},{"issue":"8","key":"901_CR29","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1002\/acm2.12666","volume":"20","author":"BA Varghese","year":"2019","unstructured":"Varghese BA, Hwang D, Cen SY et al (2019) Reliability of CT-based texture features: Phantom study. J Appl Clin Med Phys 20(8):155-163. https:\/\/doi.org\/10.1002\/acm2.12666","journal-title":"J Appl Clin Med Phys"},{"issue":"6","key":"901_CR30","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.jcct.2018.11.004","volume":"13","author":"M Kolossv\u00e1ry","year":"2019","unstructured":"Kolossv\u00e1ry M, Szilveszter B, Kar\u00e1dy J, Drobni ZD, Merkely B, Maurovich-Horvat P (2019) Effect of image reconstruction algorithms on volumetric and radiomic parameters of coronary plaques. J Cardiovasc Comput Tomogr 13(6):325-330. https:\/\/doi.org\/10.1016\/j.jcct.2018.11.004","journal-title":"J Cardiovasc Comput Tomogr"},{"key":"901_CR31","doi-asserted-by":"publisher","unstructured":"Ye K, Chen M, Zhu Q, Lu Y, Yuan H (2021) Effect of adaptive statistical iterative reconstruction-V (ASIR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules. Quant Imaging Med Surg 11(6):2344\u20132353. https:\/\/doi.org\/10.21037\/qims-20-932","DOI":"10.21037\/qims-20-932"},{"issue":"7","key":"901_CR32","doi-asserted-by":"publisher","first-page":"4587","DOI":"10.1007\/s00330-022-08592-y","volume":"32","author":"F Michallek","year":"2022","unstructured":"Michallek F, Genske U, Niehues SM, Hamm B, Jahnke P (2022) Deep learning reconstruction improves radiomics feature stability and discriminative power in abdominal CT imaging: a phantom study. Eur Radiol 32(7):4587-4595. https:\/\/doi.org\/10.1007\/s00330-022-08592-y","journal-title":"Eur Radiol"},{"key":"901_CR33","unstructured":"The National Health Commission of People\u2019s Republic of China (2018) Diagnostic reference levels for adults in X-ray computed tomography. Accessed via http:\/\/www.nhc.gov.cn\/wjw\/pcrb\/201810\/d3bb2f7acef248f0a1347a2da93cb41f.shtml on Dec 2022"},{"issue":"1","key":"901_CR34","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1148\/radiol.11100978","volume":"259","author":"K Matsumoto","year":"2011","unstructured":"Matsumoto K, Jinzaki M, Tanami Y, Ueno A, Yamada M, Kuribayashi S (2011) Virtual monochromatic spectral imaging with fast kilovoltage switching: improved image quality as compared with that obtained with conventional 120-kVp CT. Radiology 259(1):257-262. https:\/\/doi.org\/10.1148\/radiol.11100978","journal-title":"Radiology"},{"issue":"2","key":"901_CR35","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1177\/096228029900800204","volume":"8","author":"JM Bland","year":"1999","unstructured":"Bland JM, Altman DG (1999) Measuring agreement in method comparison studies. Stat Methods Med Res 8(2):135\u2013160. https:\/\/doi.org\/10.1177\/096228029900800204","journal-title":"Stat Methods Med Res"},{"issue":"2","key":"901_CR36","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15(2):155\u2013163. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J Chiropr Med"},{"issue":"1","key":"901_CR37","doi-asserted-by":"publisher","first-page":"255","DOI":"10.2307\/2532051","volume":"45","author":"LI Lin","year":"1989","unstructured":"Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45(1):255\u2013268.","journal-title":"Biometrics"},{"issue":"1","key":"901_CR38","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1111\/j.0006-341X.2000.00324.x","volume":"56","author":"LI Lin","year":"2000","unstructured":"Lin LI (2000) A note on the concordance correlation coefficient. Biometrics 56(1):324\u2013325.","journal-title":"Biometrics"},{"issue":"3","key":"901_CR39","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1002\/jmri.27635","volume":"54","author":"B Eck","year":"2021","unstructured":"Eck B, Chirra PV, Muchhala A et al (2021) Prospective evaluation of repeatability and robustness of radiomic descriptors in healthy brain tissue regions in vivo across systematic variations in T2-weighted magnetic resonance imaging acquisition parameters. J Magn Reson Imaging 54(3):1009-1021. https:\/\/doi.org\/10.1002\/jmri.27635","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"901_CR40","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1002\/jmri.28191","volume":"56","author":"RN Mitchell-Hay","year":"2022","unstructured":"Mitchell-Hay RN, Ahearn TS, Murray AD, Waiter GD (2022) Investigation of the inter- and intrascanner reproducibility and repeatability of radiomics features in T1-weighted brain MRI. J Magn Reson Imaging 56(5):1559\u20131568. https:\/\/doi.org\/10.1002\/jmri.28191","journal-title":"J Magn Reson Imaging"},{"key":"901_CR41","doi-asserted-by":"publisher","unstructured":"Adelsmayr G, Janisch M, Kaufmann-B\u00fchler AK et al (2023) CT texture analysis reliability in pulmonary lesions: the influence of 3D vs. 2D lesion segmentation and volume definition by a Hounsfield-unit threshold. Eur Radiol 33(5):3064\u20133071. https:\/\/doi.org\/10.1007\/s00330-023-09500-8","DOI":"10.1007\/s00330-023-09500-8"},{"key":"901_CR42","unstructured":"Mangiafico SS (2016) Summary and analysis of extension program evaluation in R, version 1.19.10, revised 2016. Accessed via http:\/\/rcompanion.org\/handbook\/ on Dec 2022"},{"issue":"4","key":"901_CR43","doi-asserted-by":"publisher","first-page":"402","DOI":"10.3348\/kjr.2021.0683","volume":"23","author":"J Park","year":"2022","unstructured":"Park J, Shin J, Min IK et al (2022) Image quality and lesion detectability of lower-dose abdominopelvic CT obtained using deep learning image reconstruction. Korean J Radiol 23(4):402-412. https:\/\/doi.org\/10.3348\/kjr.2021.0683","journal-title":"Korean J Radiol"},{"issue":"1","key":"901_CR44","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/s00330-021-08121-3","volume":"32","author":"Y Noda","year":"2022","unstructured":"Noda Y, Kawai N, Nagata S et al (2022) Deep learning image reconstruction algorithm for pancreatic protocol dual-energy computed tomography: image quality and quantification of iodine concentration. Eur Radiol 32(1):384-394. https:\/\/doi.org\/10.1007\/s00330-021-08121-3","journal-title":"Eur Radiol"},{"issue":"6","key":"901_CR45","doi-asserted-by":"publisher","first-page":"3974","DOI":"10.1007\/s00330-021-08459-8","volume":"32","author":"HJ Park","year":"2022","unstructured":"Park HJ, Choi SY, Lee JE et al (2022) Deep learning image reconstruction algorithm for abdominal multidetector CT at different tube voltages: assessment of image quality and radiation dose in a phantom study. Eur Radiol 32(6):3974-3984. https:\/\/doi.org\/10.1007\/s00330-021-08459-8","journal-title":"Eur Radiol"},{"issue":"10","key":"901_CR46","doi-asserted-by":"publisher","first-page":"7098","DOI":"10.1007\/s00330-022-09018-5","volume":"32","author":"JJ Xu","year":"2022","unstructured":"Xu JJ, L\u00f6nn L, Budtz-J\u00f8rgensen E, Hansen KL, Ulriksen PS (2022) Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT. Eur Radiol 32(10):7098-7107. https:\/\/doi.org\/10.1007\/s00330-022-09018-5","journal-title":"Eur Radiol"},{"issue":"8","key":"901_CR47","doi-asserted-by":"publisher","first-page":"5499","DOI":"10.1007\/s00330-022-08647-0","volume":"32","author":"M Sato","year":"2022","unstructured":"Sato M, Ichikawa Y, Domae K et al (2022) Deep learning image reconstruction for improving image quality of contrast-enhanced dual-energy CT in abdomen. Eur Radiol 32(8):5499-5507. https:\/\/doi.org\/10.1007\/s00330-022-08647-0","journal-title":"Eur Radiol"},{"issue":"4","key":"901_CR48","doi-asserted-by":"publisher","first-page":"1536","DOI":"10.1007\/s00261-023-03845-w","volume":"48","author":"JJ Xu","year":"2023","unstructured":"Xu JJ, L\u00f6nn L, Budtz-J\u00f8rgensen E, Jawad S, Ulriksen PS, Hansen KL (2023) Evaluation of thin-slice abdominal DECT using deep-learning image reconstruction in 74 keV virtual monoenergetic images: an image quality comparison. Abdom Radiol (NY) 48(4):1536-1544. https:\/\/doi.org\/10.1007\/s00261-023-03845-w","journal-title":"Abdom Radiol (NY)"},{"issue":"5","key":"901_CR49","doi-asserted-by":"publisher","first-page":"258","DOI":"10.3390\/diagnostics10050258","volume":"10","author":"M Espinasse","year":"2020","unstructured":"Espinasse M, Pitre-Champagnat S, Charmettant B et al (2020) CT Texture analysis challenges: influence of acquisition and reconstruction parameters: a comprehensive review. Diagnostics (Basel) 10(5):258. https:\/\/doi.org\/10.3390\/diagnostics10050258","journal-title":"Diagnostics (Basel)"},{"issue":"5","key":"901_CR50","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1097\/RLI.0000000000000839","volume":"57","author":"SB Lee","year":"2022","unstructured":"Lee SB, Cho YJ, Hong Y et al (2022) Deep learning-based image conversion improves the reproducibility of computed tomography radiomics features: a phantom study. Invest Radiol 57(5):308-317. https:\/\/doi.org\/10.1097\/RLI.0000000000000839","journal-title":"Invest Radiol"},{"key":"901_CR51","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1148\/radiol.2019181960","volume":"292","author":"J Choe","year":"2019","unstructured":"Choe J, Lee SM, Do KH et al (2019) Deep Learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses. Radiology 292:365\u2013 373. https:\/\/doi.org\/10.1148\/radiol.2019181960","journal-title":"Radiology"},{"issue":"3","key":"901_CR52","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1002\/mp.15491","volume":"49","author":"Y Li","year":"2022","unstructured":"Li Y, Reyhan M, Zhang Y et al (2022) The impact of phantom design and material-dependence on repeatability and reproducibility of CT-based radiomics features. Med Phys 49(3):1648-1659. https:\/\/doi.org\/10.1002\/mp.15491","journal-title":"Med Phys"},{"issue":"10","key":"901_CR53","doi-asserted-by":"publisher","first-page":"6359","DOI":"10.1002\/mp.15918","volume":"49","author":"H Kawashima","year":"2022","unstructured":"Kawashima H, Ichikawa K, Takata T, Seto I (2022) Comparative assessment of noise properties for two deep learning CT image reconstruction techniques and filtered back projection. Med Phys 49(10):6359-6367. https:\/\/doi.org\/10.1002\/mp.15918","journal-title":"Med Phys"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00901-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-023-00901-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-023-00901-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T15:17:56Z","timestamp":1709306276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-023-00901-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,29]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["901"],"URL":"https:\/\/doi.org\/10.1007\/s10278-023-00901-1","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,29]]},"assertion":[{"value":"2 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Institutional Review Board approval was not required because of the nature of our study, which was a phantom study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Written informed consent was not required for this study because of the nature of our study, which was a phantom study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"Consent to publish was not required because of the nature of our study, which was a phantom study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Mr. Wei Lu and Dr. Jianying Li are employees of GE Healthcare. However, they neither had access nor control over the phantom data acquisition and analysis. All other authors of this manuscript have no relevant financial or non-financial interests to disclose.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}