{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T15:29:14Z","timestamp":1772724554947,"version":"3.50.1"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031723834","type":"print"},{"value":"9783031723841","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-72384-1_72","type":"book-chapter","created":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:02:53Z","timestamp":1727866973000},"page":"768-778","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Variational Field Constraint Learning for\u00a0Degree of\u00a0Coronary Artery Ischemia Assessment"],"prefix":"10.1007","author":[{"given":"Qi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Xiujian","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Heye","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chenchu","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Yixuan","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Tan","sequence":"additional","affiliation":[]},{"given":"Zhifan","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,3]]},"reference":[{"key":"72_CR1","doi-asserted-by":"crossref","unstructured":"Yuehua Li, Mengmeng Yu, Xu\u00a0Dai, Zhigang Lu, Chengxing Shen, Yining Wang, Bin Lu, and Jiayin Zhang. Detection of hemodynamically significant coronary stenosis: Ct myocardial perfusion versus machine learning ct fractional flow reserve. Radiology, 293(2):305\u2013314, 2019.","DOI":"10.1148\/radiol.2019190098"},{"key":"72_CR2","doi-asserted-by":"crossref","unstructured":"Zhifan Gao, Xin Wang, Shanhui Sun, Dan Wu, Junjie Bai, Youbing Yin, Xin Liu, Heye Zhang, and Victor Hugo\u00a0C de\u00a0Albuquerque. Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static ct angiography imaging. Neural Networks, 123:82\u201393, 2020.","DOI":"10.1016\/j.neunet.2019.11.017"},{"key":"72_CR3","doi-asserted-by":"crossref","unstructured":"PIJLS NH. Fractional flow reserve: A useful index to evaluate the influence of an epicardial coronary stenosis on myocardial blood flow. Circulation, 92:3183\u20133193, 1995.","DOI":"10.1161\/01.CIR.92.11.3183"},{"key":"72_CR4","doi-asserted-by":"crossref","unstructured":"Bernard de\u00a0Bruyne, Jozef Bartunek, Stanislas\u00a0U Sys, Nico\u00a0HJ Pijls, Guy\u00a0R Heyndrickx, and William Wijns. Simultaneous coronary pressure and flow velocity measurements in humans: feasibility, reproducibility, and hemodynamic dependence of coronary flow velocity reserve, hyperemic flow versus pressure slope index, and fractional flow reserve. Circulation, 94(8):1842\u20131849, 1996.","DOI":"10.1161\/01.CIR.94.8.1842"},{"key":"72_CR5","doi-asserted-by":"crossref","unstructured":"Nico\u00a0HJ Pijls, William\u00a0F Fearon, Pim\u00a0AL Tonino, Uwe Siebert, Fumiaki Ikeno, Bernhard Bornschein, Marcel van\u2019t Veer, Volker Klauss, Ganesh Manoharan, Thomas Engstr\u00f8m, et\u00a0al. Fractional flow reserve versus angiography for guiding percutaneous coronary intervention in patients with multivessel coronary artery disease: 2-year follow-up of the fame (fractional flow reserve versus angiography for multivessel evaluation) study. Journal of the American College of Cardiology, 56(3):177\u2013184, 2010.","DOI":"10.1016\/j.jacc.2010.04.012"},{"key":"72_CR6","doi-asserted-by":"crossref","unstructured":"Baihong Xie, Xiujian Liu, Heye Zhang, Chenchu Xu, Tieyong Zeng, Yixuan Yuan, Guang Yang, and Zhifan Gao. Conditional physics-informed graph neural network for fractional flow reserve assessment. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 110\u2013120. Springer, 2023.","DOI":"10.1007\/978-3-031-43990-2_11"},{"key":"72_CR7","doi-asserted-by":"crossref","unstructured":"Charles\u00a0A Taylor, Kersten Petersen, Nan Xiao, Matthew Sinclair, Ying Bai, Sabrina\u00a0R Lynch, Adam UpdePac, and Michiel Schaap. Patient-specific modeling of blood flow in the coronary arteries. Computer Methods in Applied Mechanics and Engineering, 417:116414, 2023.","DOI":"10.1016\/j.cma.2023.116414"},{"key":"72_CR8","doi-asserted-by":"crossref","unstructured":"Christian Tesche, Carlo\u00a0N De\u00a0Cecco, Stefan Baumann, Matthias Renker, Tindal\u00a0W McLaurin, Taylor\u00a0M Duguay, Richard\u00a0R Bayer\u00a02nd, Daniel\u00a0H Steinberg, Katharine\u00a0L Grant, Christian Canstein, et\u00a0al. Coronary ct angiography\u2013derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology, 288(1):64\u201372, 2018.","DOI":"10.1148\/radiol.2018171291"},{"key":"72_CR9","doi-asserted-by":"crossref","unstructured":"Leonardo Chirco and Sandro Manservisi. An adjoint based pressure boundary optimal control approach for fluid-structure interaction problems. Computers & Fluids, 182:118\u2013127, 2019.","DOI":"10.1016\/j.compfluid.2019.02.017"},{"key":"72_CR10","doi-asserted-by":"crossref","unstructured":"Hyeonyong Hae, Soo-Jin Kang, Won-Jang Kim, So-Yeon Choi, June-Goo Lee, Youngoh Bae, Hyungjoo Cho, Dong\u00a0Hyun Yang, Joon-Won Kang, Tae-Hwan Lim, et\u00a0al. Machine learning assessment of myocardial ischemia using angiography: Development and retrospective validation. Plos Medicine, 15(11):e1002693, 2018.","DOI":"10.1371\/journal.pmed.1002693"},{"key":"72_CR11","doi-asserted-by":"crossref","unstructured":"Sifan Wang, Hanwen Wang, and Paris Perdikaris. Learning the solution operator of parametric partial differential equations with physics-informed deeponets. Science Advances, 7(40):eabi8605, 2021.","DOI":"10.1126\/sciadv.abi8605"},{"key":"72_CR12","doi-asserted-by":"crossref","unstructured":"Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter\u00a0R Witschey, John\u00a0A Detre, and Paris Perdikaris. Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks. Computer Methods in Applied Mechanics and Engineering, 358:112623, 2020.","DOI":"10.1016\/j.cma.2019.112623"},{"key":"72_CR13","doi-asserted-by":"crossref","unstructured":"Xuelan Zhang, Baoyan Mao, Yue Che, Jiaheng Kang, Mingyao Luo, Aike Qiao, Youjun Liu, Hitomi Anzai, Makoto Ohta, Yuting Guo, et\u00a0al. Physics-informed neural networks (pinns) for 4d hemodynamics prediction: An investigation of optimal framework based on vascular morphology. Computers in Biology and Medicine, 164:107287, 2023.","DOI":"10.1016\/j.compbiomed.2023.107287"},{"key":"72_CR14","doi-asserted-by":"crossref","unstructured":"Mohammad Sarabian, Hessam Babaee, and Kaveh Laksari. Physics-informed neural networks for brain hemodynamic predictions using medical imaging. IEEE Transactions on Medical Imaging, 41(9):2285\u20132303, 2022.","DOI":"10.1109\/TMI.2022.3161653"},{"key":"72_CR15","unstructured":"Nadim Saad, Gaurav Gupta, Shima Alizadeh, and Danielle\u00a0C Maddix. Guiding continuous operator learning through physics-based boundary constraints. arXiv preprint arXiv:2212.07477"},{"key":"72_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2023.112405","volume":"491","author":"Henrik Rosenberger","year":"2023","unstructured":"Henrik Rosenberger and Benjamin Sanderse. No pressure? energy-consistent roms for the incompressible navier-stokes equations with time-dependent boundary conditions. Journal of Computational Physics, 491:112405, 2023.","journal-title":"Journal of Computational Physics"},{"key":"72_CR17","doi-asserted-by":"crossref","unstructured":"Luca Pegolotti, Martin\u00a0R Pfaller, Alison\u00a0L Marsden, and Simone Deparis. Model order reduction of flow based on a modular geometrical approximation of blood vessels. Computer Methods in Applied Mechanics and Engineering, 380:113762, 2021.","DOI":"10.1016\/j.cma.2021.113762"},{"key":"72_CR18","doi-asserted-by":"crossref","unstructured":"Martin\u00a0R Pfaller, Jonathan Pham, Aekaansh Verma, Luca Pegolotti, Nathan\u00a0M Wilson, David\u00a0W Parker, Weiguang Yang, and Alison\u00a0L Marsden. Automated generation of 0d and 1d reduced-order models of patient-specific blood flow. International Journal for Numerical Methods in Biomedical Engineering, 38(10):e3639, 2022.","DOI":"10.1002\/cnm.3639"},{"key":"72_CR19","doi-asserted-by":"crossref","unstructured":"Qi\u00a0Zhang, Yahui Zhang, Liling Hao, Yujia Zhong, Kunlin Wu, Zhuo Wang, Shuai Tian, Qi\u00a0Lin, and Guifu Wu. A personalized 0d-1d model of cardiovascular system for the hemodynamic simulation of enhanced external counterpulsation. Computer Methods and Programs in Biomedicine, 227:107224, 2022.","DOI":"10.1016\/j.cmpb.2022.107224"},{"key":"72_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106767","volume":"157","author":"Cheong-Ah Lee","year":"2023","unstructured":"Cheong-Ah Lee, Hafiz Muhammad\u00a0Umer Farooqi, and Dong-Guk Paeng. Axial shear rate: A hemorheological factor for erythrocyte aggregation under womersley flow in an elastic vessel based on numerical simulation. Computers in Biology and Medicine, 157:106767, 2023.","journal-title":"Computers in Biology and Medicine"},{"issue":"1","key":"72_CR21","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.jbiomech.2012.10.012","volume":"46","author":"Umberto Morbiducci","year":"2013","unstructured":"Umberto Morbiducci, Raffaele Ponzini, Diego Gallo, Cristina Bignardi, and Giovanna Rizzo. Inflow boundary conditions for image-based computational hemodynamics: impact of idealized versus measured velocity profiles in the human aorta. Journal of Biomechanics, 46(1):102\u2013109, 2013.","journal-title":"Journal of Biomechanics"},{"key":"72_CR22","doi-asserted-by":"crossref","unstructured":"Luca Pegolotti, Martin\u00a0R Pfaller, Alison\u00a0L Marsden, and Simone Deparis. Model order reduction of flow based on a modular geometrical approximation of blood vessels. Computer Methods in Applied Mechanics and Engineering, 380:113762, 2021.","DOI":"10.1016\/j.cma.2021.113762"},{"key":"72_CR23","doi-asserted-by":"crossref","unstructured":"H\u00a0Huang and F\u00a0Costanzo. On the use of space\u2013time finite elements in the solution of elasto-dynamic problems with strain discontinuities. Computer Methods in Applied Mechanics and Engineering, 191(46):5315\u20135343, 2002.","DOI":"10.1016\/S0045-7825(02)00460-7"},{"key":"72_CR24","doi-asserted-by":"crossref","unstructured":"Paul\u00a0D Morris, Andrew Narracott, Hendrik von Tengg-Kobligk, Daniel Alejandro\u00a0Silva Soto, Sarah Hsiao, Angela Lungu, Paul Evans, Neil\u00a0W Bressloff, Patricia\u00a0V Lawford, D\u00a0Rodney Hose, et\u00a0al. Computational fluid dynamics modelling in cardiovascular medicine. Heart, 102(1):18\u201328, 2016.","DOI":"10.1136\/heartjnl-2015-308044"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72384-1_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,2]],"date-time":"2024-10-02T11:23:02Z","timestamp":1727868182000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72384-1_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031723834","9783031723841"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72384-1_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2024\/en\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}