{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:02:21Z","timestamp":1782990141840,"version":"3.54.5"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:00:00Z","timestamp":1688083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81873886"],"award-info":[{"award-number":["81873886"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81873887"],"award-info":[{"award-number":["81873887"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82171884"],"award-info":[{"award-number":["82171884"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China Youth project","award":["82101981"],"award-info":[{"award-number":["82101981"]}]},{"name":"Shanghai Science and technology innovation action plan, technology standard project Grant numbers","award":["19DZ2203800"],"award-info":[{"award-number":["19DZ2203800"]}]},{"name":"Shanghai Science and technology innovation action plan, technology standard project","award":["19DZ2203800"],"award-info":[{"award-number":["19DZ2203800"]}]},{"name":"Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant: Shanghai Jiao Tong University school of medicine Double hundred outstanding person projrect","award":["20191904"],"award-info":[{"award-number":["20191904"]}]},{"name":"Shanghai Jiao Tong University medical engineering cross project","award":["YG2022QN016"],"award-info":[{"award-number":["YG2022QN016"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-023-09807-6","type":"journal-article","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T10:02:35Z","timestamp":1688119355000},"page":"8477-8487","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A deep learning method for the automated assessment of paradoxical pulsation after myocardial infarction using multicenter cardiac MRI data"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2718-8416","authenticated-orcid":false,"given":"Bing-Hua","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong-Wen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong-Aolei","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji-Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Hong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling-Zhan","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kennedy","family":"Watson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke","family":"Wesemann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiani","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei-Bo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian-Rong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ChaoLu","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Pu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7381-5436","authenticated-orcid":false,"given":"Lian-Ming","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"key":"9807_CR1","doi-asserted-by":"publisher","first-page":"989","DOI":"10.1016\/j.jcmg.2016.06.015","volume":"10","author":"A Kidambi","year":"2017","unstructured":"Kidambi A, Motwani M, Uddin A et al (2017) Myocardial extracellular volume estimation by CMR predicts functional recovery following acute MI. JACC Cardiovasc Imaging 10:989\u2013999","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR2","doi-asserted-by":"publisher","first-page":"1430","DOI":"10.1016\/j.jcmg.2015.10.005","volume":"8","author":"N D'Elia","year":"2015","unstructured":"D\u2019Elia N, D\u2019Hooge J, Marwick TH (2015) Association between myocardial mechanics and ischemic LV remodeling. JACC Cardiovasc Imaging 8:1430\u20131443","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR3","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1378\/chest.77.5.586","volume":"77","author":"HS Cabin","year":"1980","unstructured":"Cabin HS, Roberts WC (1980) Left ventricular aneurysm, intraaneurysmal thrombus and systemic embolus in coronary heart disease. Chest 77:586\u2013590","journal-title":"Chest"},{"key":"9807_CR4","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1148\/radiol.2361031699","volume":"236","author":"E Konen","year":"2005","unstructured":"Konen E, Merchant N, Gutierrez C et al (2005) True versus false left ventricular aneurysm: differentiation with MR imaging\u2013initial experience. Radiology 236:65\u201370","journal-title":"Radiology"},{"key":"9807_CR5","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1056\/NEJM198410183111601","volume":"311","author":"J Meizlish","year":"1984","unstructured":"Meizlish J, Berger H, Plankey M, Errico D, Levy W, Zaret B (1984) Functional left ventricular aneurysm formation after acute anterior transmural myocardial infarction. Incidence, natural history, and prognostic implications. N Engl J Med 311:1001\u20131006","journal-title":"N Engl J Med"},{"key":"9807_CR6","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/oxfordjournals.eurheartj.a060045","volume":"13","author":"W Shen","year":"1992","unstructured":"Shen W, Tribouilloy C, Mirode A, Dufoss\u00e9 H, Lesbre J (1992) Left ventricular aneurysm and prognosis in patients with first acute transmural anterior myocardial infarction and isolated left anterior descending artery disease. Eur Heart J 13:39\u201344","journal-title":"Eur Heart J"},{"key":"9807_CR7","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1016\/j.crad.2005.01.007","volume":"60","author":"GJ Heatlie","year":"2005","unstructured":"Heatlie GJ, Mohiaddin R (2005) Left ventricular aneurysm: comprehensive assessment of morphology, structure and thrombus using cardiovascular magnetic resonance. Clin Radiol 60:687\u2013692","journal-title":"Clin Radiol"},{"key":"9807_CR8","doi-asserted-by":"crossref","unstructured":"Lam C, Gale H, Drake E (1964) Surgical treatment of left ventricular aneurysms. JAMA 187:1-3","DOI":"10.1001\/jama.1964.03060140007001"},{"key":"9807_CR9","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1161\/01.CIR.72.2.280","volume":"72","author":"M Matsumoto","year":"1985","unstructured":"Matsumoto M, Watanabe F, Goto A et al (1985) Left ventricular aneurysm and the prediction of left ventricular enlargement studied by two-dimensional echocardiography: quantitative assessment of aneurysm size in relation to clinical course. Circulation 72:280\u2013286","journal-title":"Circulation"},{"key":"9807_CR10","doi-asserted-by":"publisher","first-page":"3931","DOI":"10.1007\/s00330-020-07454-9","volume":"31","author":"H Zhou","year":"2021","unstructured":"Zhou H, Li L, Liu Z et al (2021) Deep learning algorithm to improve hypertrophic cardiomyopathy mutation prediction using cardiac cine images. Eur Radiol 31:3931\u20133940","journal-title":"Eur Radiol"},{"key":"9807_CR11","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1002\/jmri.27573","volume":"54","author":"J Montalt-Tordera","year":"2021","unstructured":"Montalt-Tordera J, Quail M, Steeden JA, Muthurangu V (2021) Reducing contrast agent dose in cardiovascular MR angiography with deep learning. J Magn Reson Imaging 54:795\u2013805","journal-title":"J Magn Reson Imaging"},{"key":"9807_CR12","doi-asserted-by":"publisher","first-page":"2127","DOI":"10.1002\/mrm.28568","volume":"85","author":"JI Hamilton","year":"2021","unstructured":"Hamilton JI, Currey D, Rajagopalan S, Seiberlich N (2021) Deep learning reconstruction for cardiac magnetic resonance fingerprinting T1 and T2 mapping. Magn Reson Med 85:2127\u20132135","journal-title":"Magn Reson Med"},{"key":"9807_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s12968-021-00712-9","volume":"23","author":"S Ghadimi","year":"2021","unstructured":"Ghadimi S, Auger DA, Feng X et al (2021) Fully-automated global and segmental strain analysis of DENSE cardiovascular magnetic resonance using deep learning for segmentation and phase unwrapping. J Cardiovasc Magn Reson 23:20","journal-title":"J Cardiovasc Magn Reson"},{"key":"9807_CR14","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2894322","author":"J Duan","year":"2019","unstructured":"Duan J, Bello G, Schlemper J et al (2019) Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach. IEEE Trans Med Imaging. https:\/\/doi.org\/10.1109\/TMI.2019.2894322","journal-title":"IEEE Trans Med Imaging"},{"key":"9807_CR15","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","volume":"12","author":"G Litjens","year":"2019","unstructured":"Litjens G, Ciompi F, Wolterink JM et al (2019) State-of-the-art deep learning in cardiovascular image analysis. JACC Cardiovasc Imaging 12:1549\u20131565","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR16","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3389\/fcvm.2020.00025","volume":"7","author":"C Chen","year":"2020","unstructured":"Chen C, Qin C, Qiu H et al (2020) Deep learning for cardiac image segmentation: a review. Front Cardiovasc Med 7:25","journal-title":"Front Cardiovasc Med"},{"key":"9807_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s12968-020-00690-4","volume":"22","author":"N Martini","year":"2020","unstructured":"Martini N, Aimo A, Barison A et al (2020) Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance. J Cardiovasc Magn Reson 22:84","journal-title":"J Cardiovasc Magn Reson"},{"key":"9807_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26658","author":"JW Goldfarb","year":"2019","unstructured":"Goldfarb JW, Craft J, Cao JJ (2019) Water-fat separation and parameter mapping in cardiac MRI via deep learning with a convolutional neural network. J Magn Reson Imaging. https:\/\/doi.org\/10.1002\/jmri.26658","journal-title":"J Magn Reson Imaging"},{"key":"9807_CR19","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444","journal-title":"Nature"},{"key":"9807_CR20","doi-asserted-by":"publisher","first-page":"2231","DOI":"10.1016\/j.jacc.2018.08.1038","volume":"72","author":"K Thygesen","year":"2018","unstructured":"Thygesen K, Alpert JS, Jaffe AS et al (2018) Fourth universal definition of myocardial infarction (2018). J Am Coll Cardiol 72:2231\u20132264","journal-title":"J Am Coll Cardiol"},{"key":"9807_CR21","unstructured":"Douglas PZ, Peter L, Robert OB, Douglas LM, Gordon FT, Eugene B (2018) Braunwald\u2019s heart disease: a textbook of cardiovascular medicine, 11th Edition. Elsevier, Canada. BMH Med J:3067\u20133069"},{"key":"9807_CR22","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1161\/CIRCIMAGING.113.000982","volume":"7","author":"E Joyce","year":"2014","unstructured":"Joyce E, Hoogslag GE, Leong DP et al (2014) Association between left ventricular global longitudinal strain and adverse left ventricular dilatation after ST-segment-elevation myocardial infarction. Circ Cardiovasc Imaging 7:74\u201381","journal-title":"Circ Cardiovasc Imaging"},{"key":"9807_CR23","unstructured":"Geras KJ, Wolfson S, Shen Y et al (2017) High-resolution breast cancer screening with multiview deep convolutional neural networks. arXiv preprint arXiv:1703.07047. https:\/\/arxiv.org\/abs\/1703.07047. Accessed 19 June 2023"},{"key":"9807_CR24","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jcmg.2019.08.009","volume":"13","author":"LE Juarez-Orozco","year":"2020","unstructured":"Juarez-Orozco LE, Martinez-Manzanera O, van der Zant FM, Knol RJJ, Knuuti J (2020) Deep learning in quantitative PET myocardial perfusion imaging: a study on cardiovascular event prediction. JACC Cardiovasc Imaging 13:180\u2013182","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR25","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1016\/j.jacc.2003.07.012","volume":"42","author":"RH Mehta","year":"2003","unstructured":"Mehta RH, Harjai KJ, Cox D et al (2003) Clinical and angiographic correlates and outcomes of suboptimal coronary flow inpatients with acute myocardial infarction undergoing primary percutaneous coronary intervention. J Am Coll Cardiol 42:1739\u20131746","journal-title":"J Am Coll Cardiol"},{"key":"9807_CR26","first-page":"e38","volume":"133","author":"D Mozaffarian","year":"2016","unstructured":"Writing Group M, Mozaffarian D, Benjamin EJ et al (2016) Heart disease and stroke statistics-2016 update: a report from the American Heart Association. Circulation 133:e38-360","journal-title":"Circulation"},{"key":"9807_CR27","doi-asserted-by":"publisher","first-page":"2315","DOI":"10.1161\/01.CIR.89.5.2315","volume":"89","author":"KB Gupta","year":"1994","unstructured":"Gupta KB, Ratcliffe MB, Fallert MA, Edmunds LH Jr, Bogen DK (1994) Changes in passive mechanical stiffness of myocardial tissue with aneurysm formation. Circulation 89:2315\u20132326","journal-title":"Circulation"},{"key":"9807_CR28","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1001\/archinte.159.1.15","volume":"159","author":"RJ Goldberg","year":"1999","unstructured":"Goldberg RJ, Konstam MA (1999) Assessing the population burden from heart failure: need for sentinel population-based surveillance systems. Arch Intern Med 159:15\u201317","journal-title":"Arch Intern Med"},{"key":"9807_CR29","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1093\/eurjhf\/hfq131","volume":"12","author":"AM Shah","year":"2010","unstructured":"Shah AM, Solomon SD (2010) A unified view of ventricular remodelling. Eur J Heart Fail 12:779\u2013781","journal-title":"Eur J Heart Fail"},{"key":"9807_CR30","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.jcmg.2020.05.036","volume":"14","author":"T Pezel","year":"2021","unstructured":"Pezel T, Viallon M, Croisille P et al (2021) Imaging Interstitial fibrosis, left ventricular remodeling, and function in stage A and B heart failure. JACC Cardiovasc Imaging 14:1038\u20131052","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR31","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1148\/radiol.2018180513","volume":"290","author":"Q Tao","year":"2019","unstructured":"Tao Q, Yan W, Wang Y et al (2019) Deep learning-based method for fully automatic quantification of left ventricle function from cine mr images: a multivendor, multicenter study. Radiology 290:81\u201388","journal-title":"Radiology"},{"key":"9807_CR32","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1148\/radiol.2019182304","volume":"291","author":"N Zhang","year":"2019","unstructured":"Zhang N, Yang G, Gao Z et al (2019) Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac Cine MRI. Radiology 291:606\u2013617","journal-title":"Radiology"},{"key":"9807_CR33","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1016\/j.jcmg.2019.02.024","volume":"13","author":"K Kusunose","year":"2020","unstructured":"Kusunose K, Abe T, Haga A et al (2020) A deep learning approach for assessment of regional wall motion abnormality from echocardiographic images. JACC Cardiovasc Imaging 13:374\u2013381","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR34","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1016\/j.jcmg.2021.04.010","volume":"14","author":"A Pandey","year":"2021","unstructured":"Pandey A, Kagiyama N, Yanamala N et al (2021) Deep-Learning models for the echocardiographic assessment of diastolic dysfunction. JACC Cardiovasc Imaging 14:1887\u20131900","journal-title":"JACC Cardiovasc Imaging"},{"key":"9807_CR35","doi-asserted-by":"crossref","unstructured":"Heras M, Sanz G, Betriu A, Mont L, de Flores T, Navarro-L\u00f3pez F (1990) Does left ventricular aneurysm influence survival after acute myocardial infarction? Eur Heart J 11:441\u2013446","DOI":"10.1093\/oxfordjournals.eurheartj.a059727"},{"key":"9807_CR36","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1001\/jamacardio.2020.3690","volume":"6","author":"FF Gong","year":"2021","unstructured":"Gong FF, Vaitenas I, Malaisrie SC, Maganti K (2021) Mechanical complications of acute myocardial infarction: a review. JAMA Cardiol 6:341\u2013349","journal-title":"JAMA Cardiol"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09807-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-09807-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09807-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T17:06:27Z","timestamp":1700759187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-023-09807-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,30]]},"references-count":36,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["9807"],"URL":"https:\/\/doi.org\/10.1007\/s00330-023-09807-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,30]]},"assertion":[{"value":"30 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 June 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Lian-Ming Wu.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors state that there neither exists a conflict of interest nor that there is financial information to disclose.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Lian-Ming Wu kindly provided statistical advice for this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all participants in this study.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Study subjects or cohorts have not been previously reported.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Prospective\u2022 Observational\u2022 Multicentre study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}