{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T19:43:48Z","timestamp":1793994228199,"version":"4.8.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001537","name":"University of Auckland","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001537","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Cardiovasc Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Changes in cardiovascular hemodynamics are closely related to the development of aortic regurgitation (AR), a type of valvular heart disease. Metrics derived from blood flows are used to indicate AR onset and evaluate its severity. These metrics can be non-invasively obtained using four-dimensional (4D) flow magnetic resonance imaging (MRI), where accuracy is primarily dependent on spatial resolution. However, insufficient resolution often results from limitations in 4D flow MRI and complex aortic regurgitation hemodynamics. To address this, computational fluid dynamics simulations were transformed into synthetic 4D flow MRI data and used to train a variety of neural networks. These networks generated super-resolution, full-field phase images with an upsample factor of 4. Results showed decreased velocity error, high structural similarity scores, and improved learning capabilities from previous work. Further validation was performed on two sets of in vivo 4D flow MRI data and demonstrated success in de-noising flow images. This approach presents an opportunity to comprehensively analyse AR hemodynamics in a non-invasive manner.<\/jats:p>","DOI":"10.1007\/s10554-023-02815-z","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T06:56:38Z","timestamp":1677135398000},"page":"1189-1202","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Super-resolution 4D flow MRI to quantify aortic regurgitation using computational fluid dynamics and deep learning"],"prefix":"10.1007","volume":"39","author":[{"given":"Derek","family":"Long","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cameron","family":"McMurdo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Edward","family":"Ferdian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charl\u00e8ne A.","family":"Mauger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Marlevi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martyn P.","family":"Nash","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alistair A.","family":"Young","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"issue":"1","key":"2815_CR1","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1161\/CIRCULATIONAHA.104.488825","volume":"112","author":"R Bekeredjian","year":"2005","unstructured":"Bekeredjian R, Grayburn PA (2005) Valvular heart disease. Circulation 112(1):125\u2013134. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.104.488825","journal-title":"Circulation"},{"key":"2815_CR2","doi-asserted-by":"publisher","first-page":"138","DOI":"10.3389\/fphy.2020.00138","volume":"8","author":"E Ferdian","year":"2020","unstructured":"Ferdian E, Suinesiaputra A, Dubowitz DJ, Zhao D, Wang A, Cowan B, Young AA (2020) 4DFlowNet: super-resolution 4D flow MRI using deep learning and computational fluid dynamics. Front Phys 8:138. https:\/\/doi.org\/10.3389\/fphy.2020.00138","journal-title":"Front Phys"},{"issue":"21","key":"2815_CR3","doi-asserted-by":"publisher","first-page":"2687","DOI":"10.1161\/01.CIR.0000038498.59829.38","volume":"106","author":"HP Chaliki","year":"2002","unstructured":"Chaliki HP, Mohty D, Avierinos J-F, Scott CG, Schaff HV, Tajik AJ, Enriquez-Sarano M (2002) Outcomes after aortic valve replacement in patients with severe aortic regurgitation and markedly reduced left ventricular function. Circulation 106(21):2687\u20132693. https:\/\/doi.org\/10.1161\/01.CIR.0000038498.59829.38","journal-title":"Circulation"},{"issue":"1","key":"2815_CR4","doi-asserted-by":"publisher","first-page":"1375","DOI":"10.1038\/s41598-018-37714-0","volume":"9","author":"D Marlevi","year":"2019","unstructured":"Marlevi D, Ruijsink B, Balmus M, Dillon-Murphy D, Fovargue D, Pushparajah K, Bertoglio C, Colarieti-Tosti M, Larsson M, Lamata P, Figueroa CA, Razavi R, Nordsletten DA (2019) Estimation of cardiovascular relative pressure using virtual work-energy. Sci Rep 9(1):1375. https:\/\/doi.org\/10.1038\/s41598-018-37714-0","journal-title":"Sci Rep"},{"issue":"5","key":"2815_CR5","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1002\/jmri.23632","volume":"36","author":"M Markl","year":"2012","unstructured":"Markl M, Frydrychowicz A, Kozerke S, Hope M, Wieben O (2012) 4D flow MRI. J Magn Reson Imaging 36(5):1015\u20131036. https:\/\/doi.org\/10.1002\/jmri.23632","journal-title":"J Magn Reson Imaging"},{"key":"2815_CR6","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s11748-017-0834-5","volume":"65","author":"K Itatani","year":"2017","unstructured":"Itatani K, Miyazaki S, Furusawa T, Numata S, Yamazaki S, Morimoto K, Makino R, Morichi H, Nishino T, Yaku H (2017) New imaging tools in cardiovascular medicine: computational fluid dynamics and 4D flow MRI. Gen Thorac Cardiovasc Surg 65:611\u2013621","journal-title":"Gen Thorac Cardiovasc Surg"},{"key":"2815_CR7","doi-asserted-by":"publisher","DOI":"10.1136\/openhrt-2019-001158","author":"A Alvarez","year":"2020","unstructured":"Alvarez A, Martinez V, Pizarro G, Recio M, Cabrera J\u00c1 (2020) Clinical use of 4D flow MRI for quantification of aortic regurgitation. Open Heart. https:\/\/doi.org\/10.1136\/openhrt-2019-001158","journal-title":"Open Heart"},{"key":"2815_CR8","doi-asserted-by":"publisher","DOI":"10.21037\/cdt-20-452","author":"F Catapano","year":"2020","unstructured":"Catapano F, Pambianchi G, Cundari G, Rebelo J, Cilia F, Carbone I, Catalano C, Francone M, Galea N (2020) 4D flow imaging of the thoracic aorta: is there an added clinical value? Cardiovasc Diagn Ther. https:\/\/doi.org\/10.21037\/cdt-20-452","journal-title":"Cardiovasc Diagn Ther"},{"issue":"5","key":"2815_CR9","doi-asserted-by":"publisher","first-page":"010356","DOI":"10.1161\/CIRCIMAGING.119.010356","volume":"13","author":"Y Bohbot","year":"2020","unstructured":"Bohbot Y, Renard C, Manrique A, Levy F, Mar\u00e9chaux S, Gerber BL, Tribouilloy C (2020) Usefulness of cardiac magnetic resonance imaging in aortic stenosis. Circ Cardiovasc Imaging 13(5):010356. https:\/\/doi.org\/10.1161\/CIRCIMAGING.119.010356","journal-title":"Circ Cardiovasc Imaging"},{"issue":"9","key":"2815_CR10","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1093\/ehjci\/jeab058","volume":"22","author":"SCS Minderhoud","year":"2021","unstructured":"Minderhoud SCS, Wentzel JJ, Roos-Hesselink JW, Hirsch A (2021) Six-year serial follow-up with aortic 4D flow cardiovascular magnetic resonance in a patient with bicuspid aortic valve. Eur Heart J Cardiovasc Imaging 22(9):144\u2013144. https:\/\/doi.org\/10.1093\/ehjci\/jeab058","journal-title":"Eur Heart J Cardiovasc Imaging"},{"issue":"5","key":"2815_CR11","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/s0009-9260(98)80003-2","volume":"53","author":"L Jones","year":"1998","unstructured":"Jones L, Pressdee D, Lamont P, Baird R, Murphy K (1998) A phase contrast (PC) rephase\/dephase sequence of magnetic resonance angiography (MRA): a new technique for imaging distal run-off in the pre-operative evaluation of peripheral vascular disease. Clin Radiol 53(5):333\u2013337. https:\/\/doi.org\/10.1016\/s0009-9260(98)80003-2","journal-title":"Clin Radiol"},{"issue":"2","key":"2815_CR12","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.mri.2014.10.009","volume":"33","author":"J Jiang","year":"2015","unstructured":"Jiang J, Kokeny P, Ying W, Magnano C, Zivadinov R, Mark Haacke E (2015) Quantifying errors in flow measurement using phase contrast magnetic resonance imaging: comparison of several boundary detection methods. Magn Reson Imaging 33(2):185\u2013193. https:\/\/doi.org\/10.1016\/j.mri.2014.10.009","journal-title":"Magn Reson Imaging"},{"key":"2815_CR13","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/978-1-59259-977-6_12","volume-title":"Echocardiographic evaluation of aortic regurgitation","author":"SM Sallach","year":"2007","unstructured":"Sallach SM, Reimold SC (2007). In: Solomon SD, Bulwer B (eds) Echocardiographic evaluation of aortic regurgitation. Humana Press, Totowa, pp 223\u2013238. https:\/\/doi.org\/10.1007\/978-1-59259-977-6_12"},{"key":"2815_CR14","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. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"issue":"2, Special Issu","key":"2815_CR15","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1016\/j.zemedi.2018.12.003","volume":"29","author":"A Maier","year":"2019","unstructured":"Maier A, Syben C, Lasser T, Riess C (2019) A gentle introduction to deep learning in medical image processing. Z Med Phys 29(2, Special Issue: Deep Learning in Medical Physics):86\u2013101. https:\/\/doi.org\/10.1016\/j.zemedi.2018.12.003","journal-title":"Z Med Phys"},{"issue":"3","key":"2815_CR16","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","volume":"20","author":"SC Park","year":"2003","unstructured":"Park SC, Park MK, Kang MG (2003) Super-resolution image reconstruction: a technical overview. IEEE Signal Process Mag 20(3):21\u201336. https:\/\/doi.org\/10.1109\/MSP.2003.1203207","journal-title":"IEEE Signal Process Mag"},{"key":"2815_CR17","unstructured":"Chen Y, Christodoulou AG, Zhou Z, Shi F, Xie Y, Li D (2020) MRI super-resolution with GAN and 3D multi-level DenseNet: smaller, faster, and better. Single image super-resolution for MRI with deep learning"},{"key":"2815_CR18","doi-asserted-by":"publisher","unstructured":"Chen Y, Xie Y, Zhou Z, Shi F, Christodoulou AG, Li D (2018) Brain MRI super resolution using 3D deep densely connected neural networks. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), 2018. https:\/\/doi.org\/10.1109\/isbi.2018.8363679","DOI":"10.1109\/isbi.2018.8363679"},{"issue":"9","key":"2815_CR19","doi-asserted-by":"publisher","first-page":"4148","DOI":"10.1002\/mp.13717","volume":"46","author":"J Chun","year":"2019","unstructured":"Chun J, Zhang H, Gach HM, Olberg S, Mazur T, Green O, Kim T, Kim H, Kim JS, Mutic S, Park JC (2019) MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: in the presence of limited training data and unknown translation model. Med Phys 46(9):4148\u20134164. https:\/\/doi.org\/10.1002\/mp.13717","journal-title":"Med Phys"},{"key":"2815_CR20","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-89636-z","author":"DR Rutkowski","year":"2021","unstructured":"Rutkowski DR, Rold\u00e1n-Alzate A, Johnson KM (2021) Enhancement of cerebrovascular 4D flow MRI velocity fields using machine learning and computational fluid dynamics simulation data. Nature. https:\/\/doi.org\/10.1038\/s41598-021-89636-z","journal-title":"Nature"},{"key":"2815_CR21","doi-asserted-by":"publisher","first-page":"112623","DOI":"10.1016\/j.cma.2019.112623","volume":"358","author":"G Kissas","year":"2020","unstructured":"Kissas G, Yang Y, Hwuang E, Witschey WR, Detre JA, Perdikaris P (2020) Machine learning in cardiovascular flows modeling: predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks. Comput Methods Appl Mech Eng 358:112623. https:\/\/doi.org\/10.1016\/j.cma.2019.112623","journal-title":"Comput Methods Appl Mech Eng"},{"key":"2815_CR22","doi-asserted-by":"publisher","unstructured":"Ferdian E, Marlevi D, Schollenberger J, Aristova M, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA, Young AA (2021) Cerebrovascular super-resolution 4D Flow MRI\u2014using deep learning to non-invasively quantify velocity, flow, and relative pressure. bioRxiv. https:\/\/doi.org\/10.1101\/2021.08.25.457611","DOI":"10.1101\/2021.08.25.457611"},{"key":"2815_CR23","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.mri.2019.05.038","volume":"64","author":"C Zhao","year":"2019","unstructured":"Zhao C, Shao M, Carass A, Li H, Dewey BE, Ellingsen LM, Woo J, Guttman MA, Blitz AM, Stone M, Calabresi PA, Halperin H, Prince JL (2019) Applications of a deep learning method for anti-aliasing and super-resolution in MRI. Magn Reson Imaging 64:132\u2013141. https:\/\/doi.org\/10.1016\/j.mri.2019.05.038","journal-title":"Magn Reson Imaging"},{"issue":"3","key":"2815_CR24","doi-asserted-by":"publisher","first-page":"142","DOI":"10.14797\/mdcj-9-3-142","volume":"9","author":"JC Lopez-Mattei","year":"2013","unstructured":"Lopez-Mattei JC, Shah DJ (2013) The role of cardiac magnetic resonance in valvular heart disease. Methodist Debakey Cardiovasc J 9(3):142\u2013148. https:\/\/doi.org\/10.14797\/mdcj-9-3-142","journal-title":"Methodist Debakey Cardiovasc J"},{"key":"2815_CR25","doi-asserted-by":"publisher","first-page":"128837","DOI":"10.1109\/ACCESS.2019.2939201","volume":"7","author":"L Jiao","year":"2019","unstructured":"Jiao L, Zhang F, Liu F, Yang S, Li L, Feng Z, Qu R (2019) A survey of deep learning-based object detection. IEEE Access 7:128837\u2013128868. https:\/\/doi.org\/10.1109\/ACCESS.2019.2939201","journal-title":"IEEE Access"},{"key":"2815_CR26","doi-asserted-by":"crossref","unstructured":"Zhao C, Carass A, Dewey BE, Woo J, Oh J, Calabresi PA, Reich DS, Sati P, Pham DL, Prince JL (2018) A deep learning based anti-aliasing self super-resolution algorithm for MRI. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-L\u00f3pez C, Fichtinger G (eds) Medical image computing and computer assisted intervention\u2014MICCAI 2018, 2018. Springer, Cham, pp 100\u2013108","DOI":"10.1007\/978-3-030-00928-1_12"},{"issue":"3","key":"2815_CR27","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TMI.2020.3037187","volume":"40","author":"C Zhao","year":"2021","unstructured":"Zhao C, Dewey BE, Pham DL, Calabresi PA, Reich DS, Prince JL (2021) SMORE: a self-supervised anti-aliasing and super-resolution algorithm for MRI using deep learning. IEEE Trans Med Imaging 40(3):805\u2013817. https:\/\/doi.org\/10.1109\/TMI.2020.3037187","journal-title":"IEEE Trans Med Imaging"},{"key":"2815_CR28","doi-asserted-by":"publisher","unstructured":"Pham C-H, Ducournau A, Fablet R, Rousseau F (2017) Brain MRI super-resolution using deep 3D convolutional networks. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017), 2017, pp 197\u2013200. https:\/\/doi.org\/10.1109\/ISBI.2017.7950500","DOI":"10.1109\/ISBI.2017.7950500"},{"key":"2815_CR29","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. CoRR abs\/1512.03385. arXiv: 1512.03385"},{"key":"2815_CR30","unstructured":"Huang G, Liu Z, Weinberger KQ (2016) Densely connected convolutional networks. CoRR abs\/1608.06993. arXiv: 1608.06993"},{"key":"2815_CR31","unstructured":"Wang C, Liao HM, Yeh I, Wu Y, Chen P, Hsieh J (2019) CSPNet: a new backbone that can enhance learning capability of CNN. CoRR abs\/1911.11929. arXiv: 1911.11929"},{"issue":"1","key":"2815_CR32","first-page":"111","volume":"15","author":"C Tei","year":"1985","unstructured":"Tei C, Kisanuki A, Arikawa K, Otsuji Y, Arima S, Kashima T, Tanaka H, Kushitani M, Shah PM (1985) Continuous wave Doppler echocardiographic assessment of aortic stenosis and aortic regurgitation. J Cardiogr 15(1):111\u2013121","journal-title":"J Cardiogr"},{"key":"2815_CR33","unstructured":"Madhavan S, Kemmerling EC (2016) The effect of inlet boundary conditions in image-based CFD modeling of aortic flow. In: APS Division of Fluid Dynamics meeting abstracts, 2016. APS meeting abstracts, p 15-001"},{"key":"2815_CR34","doi-asserted-by":"publisher","first-page":"986","DOI":"10.1007\/978-3-540-39964-3_62","volume-title":"On the move to meaningful Internet systems 2003: CoopIS, DOA, and ODBASE","author":"G Guo","year":"2003","unstructured":"Guo G, Wang H, Bell D, Bi Y, Greer K (2003) KNN model-based approach in classification. In: Meersman R, Tari Z, Schmidt DC (eds) On the move to meaningful Internet systems 2003: CoopIS, DOA, and ODBASE. Springer, Berlin, pp 986\u2013996"},{"key":"2815_CR35","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: a system for large-scale machine learning. In: 12th USENIX symposium on operating systems design and implementation (OSDI 16), 2016. USENIX Association, Savannah, pp 265\u2013283"},{"key":"2815_CR36","unstructured":"Kingma DP, Ba J (2017) Adam: a method for stochastic optimization"},{"issue":"3","key":"2815_CR37","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1016\/j.ijforecast.2015.12.003","volume":"32","author":"S Kim","year":"2016","unstructured":"Kim S, Kim H (2016) A new metric of absolute percentage error for intermittent demand forecasts. Int J Forecast 32(3):669\u2013679. https:\/\/doi.org\/10.1016\/j.ijforecast.2015.12.003","journal-title":"Int J Forecast"},{"issue":"4","key":"2815_CR38","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600\u2013612","journal-title":"IEEE Trans Image Process"},{"key":"2815_CR39","doi-asserted-by":"crossref","unstructured":"Ahrens J, Geveci B, Law C (2005) ParaView: an end-user tool for large data visualization. In: The visualization handbook 717(8). Elsevier, Amsterdam","DOI":"10.1016\/B978-012387582-2\/50038-1"},{"key":"2815_CR40","doi-asserted-by":"publisher","unstructured":"Pambrun J-F, Noumeir R (2015) Limitations of the SSIM quality metric in the context of diagnostic imaging. In: 2015 IEEE international conference on image processing (ICIP), 2015, pp 2960\u20132963. https:\/\/doi.org\/10.1109\/ICIP.2015.7351345","DOI":"10.1109\/ICIP.2015.7351345"},{"issue":"6","key":"2815_CR41","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1016\/0002-8703(93)90544-J","volume":"126","author":"SA Rebergen","year":"1993","unstructured":"Rebergen SA, van der Wall EE, Doornbos J, de Roos A (1993) Magnetic resonance measurement of velocity and flow: technique, validation, and cardiovascular applications. Am Heart J 126(6):1439\u20131456. https:\/\/doi.org\/10.1016\/0002-8703(93)90544-J","journal-title":"Am Heart J"},{"key":"2815_CR42","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D 404:132306. https:\/\/doi.org\/10.1016\/j.physd.2019.132306","journal-title":"Physica D"},{"issue":"4","key":"2815_CR43","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/S0735-1097(83)80311-8","volume":"2","author":"RB Stamm","year":"1983","unstructured":"Stamm RB, Martin RP (1983) Quantification of pressure gradients across stenotic valves by Doppler ultrasound. J Am Coll Cardiol 2(4):707\u2013718","journal-title":"J Am Coll Cardiol"},{"issue":"6","key":"2815_CR44","doi-asserted-by":"publisher","first-page":"3096","DOI":"10.1002\/mrm.28928","volume":"86","author":"D Marlevi","year":"2021","unstructured":"Marlevi D, Schollenberger J, Aristova M, Ferdian E, Ma Y, Young AA, Edelman ER, Schnell S, Figueroa CA, Nordsletten DA (2021) Noninvasive quantification of cerebrovascular pressure changes using 4D flow MRI. Magn Reson Med 86(6):3096\u20133110","journal-title":"Magn Reson Med"}],"container-title":["The International Journal of Cardiovascular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10554-023-02815-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10554-023-02815-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10554-023-02815-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T03:43:30Z","timestamp":1685159010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10554-023-02815-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":44,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["2815"],"URL":"https:\/\/doi.org\/10.1007\/s10554-023-02815-z","relation":{},"ISSN":["1875-8312"],"issn-type":[{"value":"1875-8312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]},"assertion":[{"value":"22 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}