Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networ...
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| Vydané v: | Computer methods in applied mechanics and engineering Ročník 358; číslo C; s. 112623 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Amsterdam
Elsevier B.V
01.01.2020
Elsevier BV Elsevier |
| Predmet: | |
| ISSN: | 0045-7825, 1879-2138 |
| On-line prístup: | Získať plný text |
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| Abstract | Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In this work we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. The effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.
•We introduce physics-informed neural networks to solve conservation laws in graph topologies.•We show how non-dimensionalization and normalization can mitigate vanishing gradient pathologies.•For the first time, a physics-informed neural network is applied to real noisy clinical data.•Arterial pressure is predicted from MRI data of blood velocity and wall displacement.•Our model also allows for calibrating boundary conditions of conventional flow simulators. |
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| AbstractList | Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In this work we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. The effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject.
•We introduce physics-informed neural networks to solve conservation laws in graph topologies.•We show how non-dimensionalization and normalization can mitigate vanishing gradient pathologies.•For the first time, a physics-informed neural network is applied to real noisy clinical data.•Arterial pressure is predicted from MRI data of blood velocity and wall displacement.•Our model also allows for calibrating boundary conditions of conventional flow simulators. Advances in computational science offer a principled pipeline for predictive modeling of cardiovascular flows and aspire to provide a valuable tool for monitoring, diagnostics and surgical planning. Such models can be nowadays deployed on large patient-specific topologies of systemic arterial networks and return detailed predictions on flow patterns, wall shear stresses, and pulse wave propagation. However, their success heavily relies on tedious pre-processing and calibration procedures that typically induce a significant computational cost, thus hampering their clinical applicability. In this work we put forth a machine learning framework that enables the seamless synthesis of non-invasive in-vivo measurement techniques and computational flow dynamics models derived from first physical principles. We illustrate this new paradigm by showing how one-dimensional models of pulsatile flow can be used to constrain the output of deep neural networks such that their predictions satisfy the conservation of mass and momentum principles. Once trained on noisy and scattered clinical data of flow and wall displacement, these networks can return physically consistent predictions for velocity, pressure and wall displacement pulse wave propagation, all without the need to employ conventional simulators. A simple post-processing of these outputs can also provide a relatively cheap and effective way for estimating Windkessel model parameters that are required for the calibration of traditional computational models. The effectiveness of the proposed techniques is demonstrated through a series of prototype benchmarks, as well as a realistic clinical case involving in-vivo measurements near the aorta/carotid bifurcation of a healthy human subject. |
| ArticleNumber | 112623 |
| Author | Kissas, Georgios Yang, Yibo Hwuang, Eileen Perdikaris, Paris Witschey, Walter R. Detre, John A. |
| Author_xml | – sequence: 1 givenname: Georgios surname: Kissas fullname: Kissas, Georgios organization: Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA – sequence: 2 givenname: Yibo surname: Yang fullname: Yang, Yibo organization: Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA – sequence: 3 givenname: Eileen surname: Hwuang fullname: Hwuang, Eileen organization: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA – sequence: 4 givenname: Walter R. surname: Witschey fullname: Witschey, Walter R. organization: Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA – sequence: 5 givenname: John A. surname: Detre fullname: Detre, John A. organization: Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA – sequence: 6 givenname: Paris orcidid: 0000-0002-2816-3229 surname: Perdikaris fullname: Perdikaris, Paris email: pgp@seas.upenn.edu organization: Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, USA |
| BackLink | https://www.osti.gov/biblio/1562339$$D View this record in Osti.gov |
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| Cites_doi | 10.1007/s10439-014-1221-3 10.1016/j.jcp.2018.04.018 10.1002/cnm.2554 10.1016/j.jcp.2018.10.045 10.1016/j.cmpb.2011.10.013 10.1016/j.jcp.2012.10.028 10.1152/ajpheart.00037.2009 10.1038/nm.3436 10.1093/brain/awr253 10.1029/94WR01498 10.1007/s10439-014-0970-3 10.1073/pnas.1814392115 10.1016/j.neuroimage.2006.01.015 10.1016/j.cma.2005.07.014 10.1007/s11517-008-0420-1 10.3389/fphys.2019.00853 10.1007/s10439-008-9527-7 10.1002/fld.543 10.1152/ajpheart.00821.2010 10.1007/s10439-010-0132-1 10.1002/jmri.23632 10.1007/s10439-010-0011-9 10.1002/cnm.2692 10.1002/cnm.2598 10.1002/cnm.2799 10.1152/ajpheart.1999.276.1.H257 10.1016/j.jbiomech.2007.05.027 10.1016/j.cma.2005.11.011 10.1080/00401706.1987.10488205 10.1109/9.679017 10.1118/1.1455742 10.3171/jns.1982.57.6.0769 10.1016/j.jcp.2019.05.027 10.1007/s10439-006-9240-3 10.1080/001075199181693 10.1023/B:ENGI.0000007979.32871.e2 10.1016/S0140-6736(85)92413-4 10.1007/s11517-008-0359-2 10.1016/j.jcp.2017.07.050 10.1098/rsif.2018.0546 10.1088/0031-9155/25/4/017 10.1161/01.HYP.26.1.2 10.1002/fld.1606 |
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| References | Schiavazzi, Baretta, Pennati, Hsia, Marsden (b60) 2017; 33 Raissi, Perdikaris, Karniadakis (b38) 2017 Gelman, Carlin, Stern, Dunson, Vehtari, Rubin (b67) 2013 Formaggia, Quarteroni, Veneziani (b4) 2010 Kingma, Ba (b47) 2014 O’Rourke (b10) 1995; 26 Sherwin, Franke, Peiró, Parker (b23) 2003; 47 Raissi, Perdikaris, Karniadakis (b31) 2019; 378 Fossan, Mariscal-Harana, Alastruey, Hellevik (b62) 2018; 15 Olufsen (b29) 1999; 276 Ruder (b46) 2016 CIBC (b16) 2016 Matthys, Alastruey, Peiró, Khir, Segers, Verdonck, Parker, Sherwin (b24) 2007; 40 Raissi, Perdikaris, Karniadakis (b32) 2017; 348 Zhu, Zabaras, Koutsourelakis, Perdikaris (b69) 2019 Raissi, Perdikaris, Karniadakis (b39) 2017 Reymond, Merenda, Perren, Rufenacht, Stergiopulos (b6) 2009; 297 Spilker, Feinstein, Parker, Reddy, Taylor (b57) 2007; 35 Mitchell, van Buchem, Sigurdsson, Gotal, Jonsdottir, Kjartansson, Garcia, Aspelund, Harris, Gudnason (b18) 2011; 134 Kak, Slaney, Wang (b2) 2002; 29 Markl, Frydrychowicz, Kozerke, Hope, Wieben (b19) 2012; 36 Kett-White, Hutchinson, Al-Rawi, Gupta, Pickard, Kirkpatrick (b12) 2002; 50 Antiga, Piccinelli, Botti, Ene-Iordache, Remuzzi, Steinman (b14) 2008; 46 Yushkevich, Piven, Hazlett, Smith, Ho, Gee, Gerig (b15) 2006; 31 Lakshminarayanan, Pritzel, Blundell (b65) 2017 Yang, Perdikaris (b68) 2018 Rose, Blackburn, Gillum, Prineas (b1) 1982 Yu, Boston, Simaan, Antaki (b58) 1998; 43 LeCun, Bottou, Orr, Müller (b51) 2012 Haris, Singh, Cai, Kogan, McGarvey, DeBrosse, Zsido, Witschey, Koomalsingh, Pilla (b3) 2014; 20 Audebert, Bucur, Vibert, Gerbeau, Vignon-Clementel (b53) 2015 Alastruey, Moore, Parker, David, Peiró, Sherwin (b54) 2008; 56 Abadi, Barham, Chen, Chen, Davis, Dean, Devin, Ghemawat, Irving, Isard (b44) 2016 Westerhof, Lankhaar, Westerhof (b27) 2009; 47 X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010, pp. 249–256. Ma, Choi, Hourlier-Fargette, Xue, Chung, Lee, Wang, Xie, Kang, Wang (b9) 2018; 115 Zhu, Zabaras (b34) 2018; 366 Rasmussen (b63) 2004 Rudy, Kutz, Brunton (b64) 2018 Osband, Aslanides, Cassirer (b66) 2018 Yang, Perdikaris (b33) 2019; 394 Chen, Quarteroni, Rozza (b26) 2013; 29 Plein, Bloomer, Ridgway, Jones, Bainbridge, Sivananthan (b20) 2001; 14 Revie, Stevenson, Chase, Hann, Lambermont, Ghuysen, Kolh, Shaw, Heldmann, Desaive (b25) 2013; 109 Paszke, Gross, Chintala, Chanan, Yang, DeVito, Lin, Desmaison, Antiga, Lerer (b43) 2017 Urquiza, Blanco, Vénere, Feijóo (b22) 2006; 195 Sherwin, Formaggia, Peiro, Franke (b36) 2003; 43 Perdikaris, Karniadakis (b61) 2014; 42 Edelstein, Hutchison, Johnson, Redpath (b13) 1980; 25 Duchi, Hazan, Singer (b48) 2011; 12 Grinberg, Karniadakis (b28) 2008; 36 Grinberg, Cheever, Anor, Madsen, Karniadakis (b7) 2011; 39 Barker, Jalinous, Freeston (b21) 1985; 325 Pant, Fabrèges, Gerbeau, Vignon-Clementel (b59) 2014; 30 Lamponi (b37) 2004 Duanmu, Chen, Gao, Yang, Luo, Hill (b52) 2019; 10 Stein (b42) 1987; 29 Aaslid, Markwalder, Nornes (b17) 1982; 57 Ismail, Wall, Gee (b55) 2013; 244 Famiglietti, Wood (b50) 1994; 30 Figueroa, Vignon-Clementel, Jansen, Hughes, Taylor (b5) 2006; 195 Perdikaris, Grinberg, Karniadakis (b30) 2015; 43 Xiao, Alastruey, Alberto Figueroa (b41) 2014; 30 Spilker, Taylor (b56) 2010; 38 Cockburn, Karniadakis, Shu (b45) 2012 Reymond, Bohraus, Perren, Lazeyras, Stergiopulos (b35) 2011; 301 Tartakovsky, Marrero, Tartakovsky, Barajas-Solano (b40) 2018 Chan, Poon, Wong, Zhang (b11) 2007 Stefanovska (b8) 1999; 40 Raissi (10.1016/j.cma.2019.112623_b32) 2017; 348 Xiao (10.1016/j.cma.2019.112623_b41) 2014; 30 Raissi (10.1016/j.cma.2019.112623_b39) 2017 Zhu (10.1016/j.cma.2019.112623_b69) 2019 Stefanovska (10.1016/j.cma.2019.112623_b8) 1999; 40 Yushkevich (10.1016/j.cma.2019.112623_b15) 2006; 31 Mitchell (10.1016/j.cma.2019.112623_b18) 2011; 134 Ruder (10.1016/j.cma.2019.112623_b46) 2016 Ma (10.1016/j.cma.2019.112623_b9) 2018; 115 Olufsen (10.1016/j.cma.2019.112623_b29) 1999; 276 Rasmussen (10.1016/j.cma.2019.112623_b63) 2004 Osband (10.1016/j.cma.2019.112623_b66) 2018 O’Rourke (10.1016/j.cma.2019.112623_b10) 1995; 26 Spilker (10.1016/j.cma.2019.112623_b56) 2010; 38 Pant (10.1016/j.cma.2019.112623_b59) 2014; 30 Raissi (10.1016/j.cma.2019.112623_b31) 2019; 378 Stein (10.1016/j.cma.2019.112623_b42) 1987; 29 Plein (10.1016/j.cma.2019.112623_b20) 2001; 14 Rose (10.1016/j.cma.2019.112623_b1) 1982 Markl (10.1016/j.cma.2019.112623_b19) 2012; 36 Antiga (10.1016/j.cma.2019.112623_b14) 2008; 46 Chan (10.1016/j.cma.2019.112623_b11) 2007 Famiglietti (10.1016/j.cma.2019.112623_b50) 1994; 30 Alastruey (10.1016/j.cma.2019.112623_b54) 2008; 56 Lakshminarayanan (10.1016/j.cma.2019.112623_b65) 2017 Haris (10.1016/j.cma.2019.112623_b3) 2014; 20 Sherwin (10.1016/j.cma.2019.112623_b36) 2003; 43 Sherwin (10.1016/j.cma.2019.112623_b23) 2003; 47 Perdikaris (10.1016/j.cma.2019.112623_b61) 2014; 42 Zhu (10.1016/j.cma.2019.112623_b34) 2018; 366 Figueroa (10.1016/j.cma.2019.112623_b5) 2006; 195 Edelstein (10.1016/j.cma.2019.112623_b13) 1980; 25 LeCun (10.1016/j.cma.2019.112623_b51) 2012 Schiavazzi (10.1016/j.cma.2019.112623_b60) 2017; 33 Chen (10.1016/j.cma.2019.112623_b26) 2013; 29 Grinberg (10.1016/j.cma.2019.112623_b28) 2008; 36 Reymond (10.1016/j.cma.2019.112623_b6) 2009; 297 Lamponi (10.1016/j.cma.2019.112623_b37) 2004 Tartakovsky (10.1016/j.cma.2019.112623_b40) 2018 Paszke (10.1016/j.cma.2019.112623_b43) 2017 Cockburn (10.1016/j.cma.2019.112623_b45) 2012 Yang (10.1016/j.cma.2019.112623_b33) 2019; 394 Barker (10.1016/j.cma.2019.112623_b21) 1985; 325 Ismail (10.1016/j.cma.2019.112623_b55) 2013; 244 Duchi (10.1016/j.cma.2019.112623_b48) 2011; 12 Gelman (10.1016/j.cma.2019.112623_b67) 2013 Formaggia (10.1016/j.cma.2019.112623_b4) 2010 Revie (10.1016/j.cma.2019.112623_b25) 2013; 109 10.1016/j.cma.2019.112623_b49 Urquiza (10.1016/j.cma.2019.112623_b22) 2006; 195 Perdikaris (10.1016/j.cma.2019.112623_b30) 2015; 43 Raissi (10.1016/j.cma.2019.112623_b38) 2017 Rudy (10.1016/j.cma.2019.112623_b64) 2018 Abadi (10.1016/j.cma.2019.112623_b44) 2016 Kett-White (10.1016/j.cma.2019.112623_b12) 2002; 50 Grinberg (10.1016/j.cma.2019.112623_b7) 2011; 39 Westerhof (10.1016/j.cma.2019.112623_b27) 2009; 47 Yang (10.1016/j.cma.2019.112623_b68) 2018 Spilker (10.1016/j.cma.2019.112623_b57) 2007; 35 Fossan (10.1016/j.cma.2019.112623_b62) 2018; 15 Reymond (10.1016/j.cma.2019.112623_b35) 2011; 301 Yu (10.1016/j.cma.2019.112623_b58) 1998; 43 CIBC (10.1016/j.cma.2019.112623_b16) 2016 Matthys (10.1016/j.cma.2019.112623_b24) 2007; 40 Kingma (10.1016/j.cma.2019.112623_b47) 2014 Duanmu (10.1016/j.cma.2019.112623_b52) 2019; 10 Kak (10.1016/j.cma.2019.112623_b2) 2002; 29 Aaslid (10.1016/j.cma.2019.112623_b17) 1982; 57 Audebert (10.1016/j.cma.2019.112623_b53) 2015 |
| References_xml | – year: 2010 ident: b4 article-title: Cardiovascular Mathematics: Modeling and simulation of the circulatory system – year: 2017 ident: b38 article-title: Physics informed deep learning (part I): Data-driven solutions of nonlinear partial differential equations – volume: 20 start-page: 209 year: 2014 ident: b3 article-title: A technique for in vivo mapping of myocardial creatine kinase metabolism publication-title: Nature Med. – volume: 15 start-page: 20180546 year: 2018 ident: b62 article-title: Optimization of topological complexity for one-dimensional arterial blood flow models publication-title: J. R. Soc. Interface – volume: 109 start-page: 197 year: 2013 end-page: 210 ident: b25 article-title: Validation of subject-specific cardiovascular system models from porcine measurements publication-title: Comput. Methods Programs Biomed. – volume: 301 start-page: H1173 year: 2011 end-page: H1182 ident: b35 article-title: Validation of a patient-specific one-dimensional model of the systemic arterial tree publication-title: Amer. J. Physiol. Heart Circ. Physiol. – volume: 38 start-page: 2635 year: 2010 end-page: 2648 ident: b56 article-title: Tuning multidomain hemodynamic simulations to match physiological measurements publication-title: Ann. Biomed. Eng. – year: 2014 ident: b47 article-title: Adam: A method for stochastic optimization – start-page: 8617 year: 2018 end-page: 8629 ident: b66 article-title: Randomized prior functions for deep reinforcement learning publication-title: Advances in Neural Information Processing Systems – volume: 46 start-page: 1097 year: 2008 ident: b14 article-title: An image-based modeling framework for patient-specific computational hemodynamics publication-title: Med. Biol. Eng. Comput. – year: 2016 ident: b16 article-title: Seg3D: Volumetric Image Segmentation and Visualization – volume: 325 start-page: 1106 year: 1985 end-page: 1107 ident: b21 article-title: Non-invasive magnetic stimulation of human motor cortex publication-title: Lancet – volume: 26 start-page: 2 year: 1995 end-page: 9 ident: b10 article-title: Mechanical principles in arterial disease publication-title: Hypertension – volume: 276 start-page: H257 year: 1999 end-page: H268 ident: b29 article-title: Structured tree outflow condition for blood flow in larger systemic arteries publication-title: Amer. J. Physiol. Heart Circ. Physiol. – volume: 366 start-page: 415 year: 2018 end-page: 447 ident: b34 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. – year: 2004 ident: b37 article-title: One dimensional and multiscale models for blood flow circulation – volume: 57 start-page: 769 year: 1982 end-page: 774 ident: b17 article-title: Noninvasive transcranial doppler ultrasound recording of flow velocity in basal cerebral arteries publication-title: J. Neurosurg. – volume: 29 start-page: 143 year: 1987 end-page: 151 ident: b42 article-title: Large sample properties of simulations using latin hypercube sampling publication-title: Technometrics – year: 2015 ident: b53 article-title: Closed-loop cardiovascular system model and partial hepatectomy simulation – volume: 39 start-page: 297 year: 2011 end-page: 309 ident: b7 article-title: Modeling blood flow circulation in intracranial arterial networks: A comparative 3D/1D simulation study publication-title: Ann. Biomed. Eng. – volume: 36 start-page: 1496 year: 2008 end-page: 1514 ident: b28 article-title: Outflow boundary conditions for arterial networks with multiple outlets publication-title: Ann. Biomed. Eng. – volume: 40 start-page: 31 year: 1999 end-page: 55 ident: b8 article-title: Physics of the human cardiovascular system publication-title: Contemp. Phys. – year: 1982 ident: b1 article-title: Cardiovascular survey methods – volume: 195 start-page: 5685 year: 2006 end-page: 5706 ident: b5 article-title: A coupled momentum method for modeling blood flow in three-dimensional deformable arteries publication-title: Comput. Methods Appl. Mech. Engrg. – year: 2019 ident: b69 article-title: Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data – volume: 115 start-page: 11144 year: 2018 end-page: 11149 ident: b9 article-title: Relation between blood pressure and pulse wave velocity for human arteries publication-title: Proc. Natl. Acad. Sci. – volume: 29 start-page: 107 year: 2002 ident: b2 article-title: Principles of computerized tomographic imaging publication-title: Med. Phys. – volume: 47 start-page: 131 year: 2009 end-page: 141 ident: b27 article-title: The arterial Windkessel publication-title: Med. Biol. Eng. Comput. – volume: 30 start-page: 3061 year: 1994 end-page: 3078 ident: b50 article-title: Multiscale modeling of spatially variable water and energy balance processes publication-title: Water Resour. Res. – year: 2018 ident: b64 article-title: Deep learning of dynamics and signal-noise decomposition with time-stepping constraints – volume: 33 year: 2017 ident: b60 article-title: Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty publication-title: Int. J. Numer. Methods Biomed. Eng. – volume: 25 start-page: 751 year: 1980 ident: b13 article-title: Spin warp NMR imaging and applications to human whole-body imaging publication-title: Phys. Med. Biol. – volume: 195 start-page: 4002 year: 2006 end-page: 4017 ident: b22 article-title: Multidimensional modelling for the carotid artery blood flow publication-title: Comput. Methods Appl. Mech. Engrg. – year: 2017 ident: b39 article-title: Physics informed deep learning (part II): Data-driven discovery of nonlinear partial differential equations – year: 2017 ident: b43 article-title: Automatic differentiation in pytorch – year: 2013 ident: b67 article-title: Bayesian data analysis – year: 2018 ident: b40 article-title: Learning parameters and constitutive relationships with physics informed deep neural networks – volume: 40 start-page: 3476 year: 2007 end-page: 3486 ident: b24 article-title: Pulse wave propagation in a model human arterial network: Assessment of 1-D numerical simulations against in vitro measurements publication-title: J. Biomech. – volume: 14 start-page: 230 year: 2001 end-page: 236 ident: b20 article-title: Steady-state free precession magnetic resonance imaging of the heart: Comparison with segmented k-space gradient-echo imaging publication-title: J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med. – volume: 50 start-page: 1213 year: 2002 end-page: 1222 ident: b12 article-title: Adverse cerebral events detected after subarachnoid hemorrhage using brain oxygen and microdialysis probes publication-title: Neurosurgery – start-page: 121 year: 2007 end-page: 123 ident: b11 article-title: A hybrid body sensor network for continuous and long-term measurement of arterial blood pressure publication-title: 2007 4th IEEE/EMBS International Summer School and Symposium on Medical Devices and Biosensors – year: 2012 ident: b45 article-title: Discontinuous Galerkin methods: Theory, computation and applications, Vol. 11 – volume: 43 start-page: 765 year: 1998 end-page: 778 ident: b58 article-title: Estimation of systemic vascular bed parameters for artificial heart control publication-title: IEEE Trans. Automat. Control – volume: 31 start-page: 1116 year: 2006 end-page: 1128 ident: b15 article-title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability publication-title: Neuroimage – start-page: 63 year: 2004 end-page: 71 ident: b63 publication-title: Gaussian Processes in Machine Learning – volume: 43 start-page: 673 year: 2003 end-page: 700 ident: b36 article-title: Computational modelling of 1D blood flow with variable mechanical properties and its application to the simulation of wave propagation in the human arterial system publication-title: Internat. J. Numer. Methods Fluids – volume: 134 start-page: 3398 year: 2011 end-page: 3407 ident: b18 article-title: Arterial stiffness, pressure and flow pulsatility and brain structure and function: The Age, Gene/Environment Susceptibility–Reykjavik study publication-title: Brain – volume: 297 start-page: H208 year: 2009 end-page: H222 ident: b6 article-title: Validation of a one-dimensional model of the systemic arterial tree publication-title: Amer. J. Physiol. Heart Circ. Physiol. – volume: 56 start-page: 1061 year: 2008 end-page: 1067 ident: b54 article-title: Reduced modelling of blood flow in the cerebral circulation: Coupling 1-D, 0-D and cerebral auto-regulation models publication-title: Int. J. Numer. Methods Fluids – start-page: 265 year: 2016 end-page: 283 ident: b44 article-title: Tensorflow: A system for large-scale machine learning publication-title: OSDI, Vol. 16 – volume: 394 start-page: 136 year: 2019 end-page: 152 ident: b33 article-title: Adversarial uncertainty quantification in physics-informed neural networks publication-title: J. Comput. Phys. – year: 2018 ident: b68 article-title: Physics-informed deep generative models – year: 2016 ident: b46 article-title: An overview of gradient descent optimization algorithms – volume: 10 year: 2019 ident: b52 article-title: A one-dimensional hemodynamic model of the coronary arterial tree publication-title: Front. Physiol. – volume: 348 start-page: 683 year: 2017 end-page: 693 ident: b32 article-title: Machine learning of linear differential equations using gaussian processes publication-title: J. Comput. Phys. – start-page: 6402 year: 2017 end-page: 6413 ident: b65 article-title: Simple and scalable predictive uncertainty estimation using deep ensembles publication-title: Advances in Neural Information Processing Systems – volume: 12 start-page: 2121 year: 2011 end-page: 2159 ident: b48 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: J. Mach. Learn. Res. – volume: 42 start-page: 1012 year: 2014 end-page: 1023 ident: b61 article-title: Fractional-order viscoelasticity in one-dimensional blood flow models publication-title: Ann. Biomed. Eng. – volume: 47 start-page: 217 year: 2003 end-page: 250 ident: b23 article-title: One-dimensional modelling of a vascular network in space-time variables publication-title: J. Eng. Math. – volume: 30 start-page: 1614 year: 2014 end-page: 1648 ident: b59 article-title: A methodological paradigm for patient-specific multi-scale CFD simulations: From clinical measurements to parameter estimates for individual analysis publication-title: Int. J. Numer. Methods Biomed. Eng. – volume: 36 start-page: 1015 year: 2012 end-page: 1036 ident: b19 article-title: 4D flow MRI publication-title: J. Magn. Reson. Imaging – volume: 29 start-page: 698 year: 2013 end-page: 721 ident: b26 article-title: Simulation-based uncertainty quantification of human arterial network hemodynamics publication-title: Int. J. Numer. Methods Biomed. Eng. – volume: 378 start-page: 686 year: 2019 end-page: 707 ident: b31 article-title: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. – volume: 30 start-page: 204 year: 2014 end-page: 231 ident: b41 article-title: A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models publication-title: Int. J. Numer. Methods Biomed. Eng. – start-page: 9 year: 2012 end-page: 48 ident: b51 article-title: Efficient backprop publication-title: Neural Networks: Tricks of the Trade – reference: X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010, pp. 249–256. – volume: 43 start-page: 1432 year: 2015 end-page: 1442 ident: b30 article-title: An effective fractal-tree closure model for simulating blood flow in large arterial networks publication-title: Ann. Biomed. Eng. – volume: 244 start-page: 113 year: 2013 end-page: 130 ident: b55 article-title: Adjoint-based inverse analysis of windkessel parameters for patient-specific vascular models publication-title: J. Comput. Phys. – volume: 35 start-page: 546 year: 2007 end-page: 559 ident: b57 article-title: Morphometry-based impedance boundary conditions for patient-specific modeling of blood flow in pulmonary arteries publication-title: Ann. Biomed. Eng. – year: 2017 ident: 10.1016/j.cma.2019.112623_b38 – year: 2017 ident: 10.1016/j.cma.2019.112623_b39 – year: 2012 ident: 10.1016/j.cma.2019.112623_b45 – volume: 43 start-page: 1432 issue: 6 year: 2015 ident: 10.1016/j.cma.2019.112623_b30 article-title: An effective fractal-tree closure model for simulating blood flow in large arterial networks publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-014-1221-3 – volume: 366 start-page: 415 year: 2018 ident: 10.1016/j.cma.2019.112623_b34 article-title: Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.04.018 – start-page: 265 year: 2016 ident: 10.1016/j.cma.2019.112623_b44 article-title: Tensorflow: A system for large-scale machine learning – volume: 50 start-page: 1213 issue: 6 year: 2002 ident: 10.1016/j.cma.2019.112623_b12 article-title: Adverse cerebral events detected after subarachnoid hemorrhage using brain oxygen and microdialysis probes publication-title: Neurosurgery – volume: 29 start-page: 698 issue: 6 year: 2013 ident: 10.1016/j.cma.2019.112623_b26 article-title: Simulation-based uncertainty quantification of human arterial network hemodynamics publication-title: Int. J. Numer. Methods Biomed. Eng. doi: 10.1002/cnm.2554 – volume: 378 start-page: 686 year: 2019 ident: 10.1016/j.cma.2019.112623_b31 article-title: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2018.10.045 – volume: 109 start-page: 197 issue: 2 year: 2013 ident: 10.1016/j.cma.2019.112623_b25 article-title: Validation of subject-specific cardiovascular system models from porcine measurements publication-title: Comput. Methods Programs Biomed. doi: 10.1016/j.cmpb.2011.10.013 – volume: 244 start-page: 113 year: 2013 ident: 10.1016/j.cma.2019.112623_b55 article-title: Adjoint-based inverse analysis of windkessel parameters for patient-specific vascular models publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2012.10.028 – start-page: 6402 year: 2017 ident: 10.1016/j.cma.2019.112623_b65 article-title: Simple and scalable predictive uncertainty estimation using deep ensembles – volume: 297 start-page: H208 issue: 1 year: 2009 ident: 10.1016/j.cma.2019.112623_b6 article-title: Validation of a one-dimensional model of the systemic arterial tree publication-title: Amer. J. Physiol. Heart Circ. Physiol. doi: 10.1152/ajpheart.00037.2009 – volume: 20 start-page: 209 issue: 2 year: 2014 ident: 10.1016/j.cma.2019.112623_b3 article-title: A technique for in vivo mapping of myocardial creatine kinase metabolism publication-title: Nature Med. doi: 10.1038/nm.3436 – volume: 134 start-page: 3398 issue: 11 year: 2011 ident: 10.1016/j.cma.2019.112623_b18 article-title: Arterial stiffness, pressure and flow pulsatility and brain structure and function: The Age, Gene/Environment Susceptibility–Reykjavik study publication-title: Brain doi: 10.1093/brain/awr253 – volume: 30 start-page: 3061 issue: 11 year: 1994 ident: 10.1016/j.cma.2019.112623_b50 article-title: Multiscale modeling of spatially variable water and energy balance processes publication-title: Water Resour. Res. doi: 10.1029/94WR01498 – volume: 42 start-page: 1012 issue: 5 year: 2014 ident: 10.1016/j.cma.2019.112623_b61 article-title: Fractional-order viscoelasticity in one-dimensional blood flow models publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-014-0970-3 – volume: 115 start-page: 11144 issue: 44 year: 2018 ident: 10.1016/j.cma.2019.112623_b9 article-title: Relation between blood pressure and pulse wave velocity for human arteries publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.1814392115 – start-page: 121 year: 2007 ident: 10.1016/j.cma.2019.112623_b11 article-title: A hybrid body sensor network for continuous and long-term measurement of arterial blood pressure – year: 2010 ident: 10.1016/j.cma.2019.112623_b4 – volume: 31 start-page: 1116 issue: 3 year: 2006 ident: 10.1016/j.cma.2019.112623_b15 article-title: User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability publication-title: Neuroimage doi: 10.1016/j.neuroimage.2006.01.015 – volume: 195 start-page: 4002 issue: 33–36 year: 2006 ident: 10.1016/j.cma.2019.112623_b22 article-title: Multidimensional modelling for the carotid artery blood flow publication-title: Comput. Methods Appl. Mech. Engrg. doi: 10.1016/j.cma.2005.07.014 – volume: 46 start-page: 1097 issue: 11 year: 2008 ident: 10.1016/j.cma.2019.112623_b14 article-title: An image-based modeling framework for patient-specific computational hemodynamics publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-008-0420-1 – year: 2019 ident: 10.1016/j.cma.2019.112623_b69 – year: 2017 ident: 10.1016/j.cma.2019.112623_b43 – volume: 10 year: 2019 ident: 10.1016/j.cma.2019.112623_b52 article-title: A one-dimensional hemodynamic model of the coronary arterial tree publication-title: Front. Physiol. doi: 10.3389/fphys.2019.00853 – volume: 36 start-page: 1496 issue: 9 year: 2008 ident: 10.1016/j.cma.2019.112623_b28 article-title: Outflow boundary conditions for arterial networks with multiple outlets publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-008-9527-7 – volume: 43 start-page: 673 issue: 6–7 year: 2003 ident: 10.1016/j.cma.2019.112623_b36 article-title: Computational modelling of 1D blood flow with variable mechanical properties and its application to the simulation of wave propagation in the human arterial system publication-title: Internat. J. Numer. Methods Fluids doi: 10.1002/fld.543 – volume: 301 start-page: H1173 issue: 3 year: 2011 ident: 10.1016/j.cma.2019.112623_b35 article-title: Validation of a patient-specific one-dimensional model of the systemic arterial tree publication-title: Amer. J. Physiol. Heart Circ. Physiol. doi: 10.1152/ajpheart.00821.2010 – volume: 39 start-page: 297 issue: 1 year: 2011 ident: 10.1016/j.cma.2019.112623_b7 article-title: Modeling blood flow circulation in intracranial arterial networks: A comparative 3D/1D simulation study publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-010-0132-1 – volume: 36 start-page: 1015 issue: 5 year: 2012 ident: 10.1016/j.cma.2019.112623_b19 article-title: 4D flow MRI publication-title: J. Magn. Reson. Imaging doi: 10.1002/jmri.23632 – volume: 38 start-page: 2635 issue: 8 year: 2010 ident: 10.1016/j.cma.2019.112623_b56 article-title: Tuning multidomain hemodynamic simulations to match physiological measurements publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-010-0011-9 – start-page: 63 year: 2004 ident: 10.1016/j.cma.2019.112623_b63 – volume: 30 start-page: 1614 issue: 12 year: 2014 ident: 10.1016/j.cma.2019.112623_b59 article-title: A methodological paradigm for patient-specific multi-scale CFD simulations: From clinical measurements to parameter estimates for individual analysis publication-title: Int. J. Numer. Methods Biomed. Eng. doi: 10.1002/cnm.2692 – year: 1982 ident: 10.1016/j.cma.2019.112623_b1 – volume: 30 start-page: 204 issue: 2 year: 2014 ident: 10.1016/j.cma.2019.112623_b41 article-title: A systematic comparison between 1-D and 3-D hemodynamics in compliant arterial models publication-title: Int. J. Numer. Methods Biomed. Eng. doi: 10.1002/cnm.2598 – volume: 33 issue: 3 year: 2017 ident: 10.1016/j.cma.2019.112623_b60 article-title: Patient-specific parameter estimation in single-ventricle lumped circulation models under uncertainty publication-title: Int. J. Numer. Methods Biomed. Eng. doi: 10.1002/cnm.2799 – volume: 276 start-page: H257 issue: 1 year: 1999 ident: 10.1016/j.cma.2019.112623_b29 article-title: Structured tree outflow condition for blood flow in larger systemic arteries publication-title: Amer. J. Physiol. Heart Circ. Physiol. doi: 10.1152/ajpheart.1999.276.1.H257 – volume: 40 start-page: 3476 issue: 15 year: 2007 ident: 10.1016/j.cma.2019.112623_b24 article-title: Pulse wave propagation in a model human arterial network: Assessment of 1-D numerical simulations against in vitro measurements publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2007.05.027 – year: 2004 ident: 10.1016/j.cma.2019.112623_b37 – volume: 14 start-page: 230 issue: 3 year: 2001 ident: 10.1016/j.cma.2019.112623_b20 article-title: Steady-state free precession magnetic resonance imaging of the heart: Comparison with segmented k-space gradient-echo imaging publication-title: J. Magn. Reson. Imaging Official J. Int. Soc. Magn. Reson. Med. – volume: 195 start-page: 5685 issue: 41–43 year: 2006 ident: 10.1016/j.cma.2019.112623_b5 article-title: A coupled momentum method for modeling blood flow in three-dimensional deformable arteries publication-title: Comput. Methods Appl. Mech. Engrg. doi: 10.1016/j.cma.2005.11.011 – volume: 29 start-page: 143 issue: 2 year: 1987 ident: 10.1016/j.cma.2019.112623_b42 article-title: Large sample properties of simulations using latin hypercube sampling publication-title: Technometrics doi: 10.1080/00401706.1987.10488205 – volume: 43 start-page: 765 issue: 6 year: 1998 ident: 10.1016/j.cma.2019.112623_b58 article-title: Estimation of systemic vascular bed parameters for artificial heart control publication-title: IEEE Trans. Automat. Control doi: 10.1109/9.679017 – year: 2018 ident: 10.1016/j.cma.2019.112623_b40 – volume: 29 start-page: 107 issue: 1 year: 2002 ident: 10.1016/j.cma.2019.112623_b2 article-title: Principles of computerized tomographic imaging publication-title: Med. Phys. doi: 10.1118/1.1455742 – volume: 57 start-page: 769 issue: 6 year: 1982 ident: 10.1016/j.cma.2019.112623_b17 article-title: Noninvasive transcranial doppler ultrasound recording of flow velocity in basal cerebral arteries publication-title: J. Neurosurg. doi: 10.3171/jns.1982.57.6.0769 – volume: 394 start-page: 136 year: 2019 ident: 10.1016/j.cma.2019.112623_b33 article-title: Adversarial uncertainty quantification in physics-informed neural networks publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2019.05.027 – year: 2013 ident: 10.1016/j.cma.2019.112623_b67 – volume: 12 start-page: 2121 issue: Jul year: 2011 ident: 10.1016/j.cma.2019.112623_b48 article-title: Adaptive subgradient methods for online learning and stochastic optimization publication-title: J. Mach. Learn. Res. – volume: 35 start-page: 546 issue: 4 year: 2007 ident: 10.1016/j.cma.2019.112623_b57 article-title: Morphometry-based impedance boundary conditions for patient-specific modeling of blood flow in pulmonary arteries publication-title: Ann. Biomed. Eng. doi: 10.1007/s10439-006-9240-3 – year: 2014 ident: 10.1016/j.cma.2019.112623_b47 – start-page: 9 year: 2012 ident: 10.1016/j.cma.2019.112623_b51 article-title: Efficient backprop – volume: 40 start-page: 31 issue: 1 year: 1999 ident: 10.1016/j.cma.2019.112623_b8 article-title: Physics of the human cardiovascular system publication-title: Contemp. Phys. doi: 10.1080/001075199181693 – year: 2016 ident: 10.1016/j.cma.2019.112623_b16 – volume: 47 start-page: 217 issue: 3–4 year: 2003 ident: 10.1016/j.cma.2019.112623_b23 article-title: One-dimensional modelling of a vascular network in space-time variables publication-title: J. Eng. Math. doi: 10.1023/B:ENGI.0000007979.32871.e2 – ident: 10.1016/j.cma.2019.112623_b49 – start-page: 8617 year: 2018 ident: 10.1016/j.cma.2019.112623_b66 article-title: Randomized prior functions for deep reinforcement learning – volume: 325 start-page: 1106 issue: 8437 year: 1985 ident: 10.1016/j.cma.2019.112623_b21 article-title: Non-invasive magnetic stimulation of human motor cortex publication-title: Lancet doi: 10.1016/S0140-6736(85)92413-4 – year: 2015 ident: 10.1016/j.cma.2019.112623_b53 – volume: 47 start-page: 131 issue: 2 year: 2009 ident: 10.1016/j.cma.2019.112623_b27 article-title: The arterial Windkessel publication-title: Med. Biol. Eng. Comput. doi: 10.1007/s11517-008-0359-2 – volume: 348 start-page: 683 year: 2017 ident: 10.1016/j.cma.2019.112623_b32 article-title: Machine learning of linear differential equations using gaussian processes publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2017.07.050 – year: 2016 ident: 10.1016/j.cma.2019.112623_b46 – volume: 15 start-page: 20180546 issue: 149 year: 2018 ident: 10.1016/j.cma.2019.112623_b62 article-title: Optimization of topological complexity for one-dimensional arterial blood flow models publication-title: J. R. Soc. Interface doi: 10.1098/rsif.2018.0546 – volume: 25 start-page: 751 issue: 4 year: 1980 ident: 10.1016/j.cma.2019.112623_b13 article-title: Spin warp NMR imaging and applications to human whole-body imaging publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/25/4/017 – year: 2018 ident: 10.1016/j.cma.2019.112623_b68 – volume: 26 start-page: 2 issue: 1 year: 1995 ident: 10.1016/j.cma.2019.112623_b10 article-title: Mechanical principles in arterial disease publication-title: Hypertension doi: 10.1161/01.HYP.26.1.2 – volume: 56 start-page: 1061 issue: 8 year: 2008 ident: 10.1016/j.cma.2019.112623_b54 article-title: Reduced modelling of blood flow in the cerebral circulation: Coupling 1-D, 0-D and cerebral auto-regulation models publication-title: Int. J. Numer. Methods Fluids doi: 10.1002/fld.1606 – year: 2018 ident: 10.1016/j.cma.2019.112623_b64 |
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| SubjectTerms | Aorta Artificial intelligence Artificial neural networks Bifurcations Blood flow modeling Blood pressure Calibration Computer simulation Computing costs Data-driven modeling Deep neural networks Machine learning Measurement techniques Neural networks Non-invasive diagnostics One dimensional models Parameter estimation Post-processing Prediction models Principles Propagation Pulse propagation Pulse wave propagation Simulators Stress propagation Topology Wall shear stresses Wave propagation |
| Title | Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks |
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