pith. sign in

arxiv: 2509.07971 · v2 · submitted 2025-09-09 · ⚛️ physics.med-ph

Cardiac mechanics modeling: recent developments and current challenges

Aaron L. Brown , Ju Liu , Daniel B. Ennis , Alison L. Marsden This is my paper

Pith reviewed 2026-05-18 17:53 UTC · model grok-4.3

classification ⚛️ physics.med-ph
keywords cardiac mechanicscomputational modelingpatient-specific modelsclinical translationheart simulationmyocardial mechanicsbiomechanicsnumerical methods
0
0 comments X

The pith

Clarifying which modeling complexities are essential versus safely omittable will enable clinical translation of patient-specific heart models.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This review surveys recent advances in building patient-specific computational models of the heart, with a focus on cardiac tissue mechanics. It examines the steps required to create these models, including image-based anatomy reconstruction, representation of myocardial mesostructure, material behavior, geometry and boundary conditions, multi-physics coupling, and numerical methods. Each step involves choices that trade physiological detail against computational effort. The central claim is that progress toward medical use of these models hinges on determining which of these details must be retained and which can be reduced without losing usefulness for applications like treatment planning.

Core claim

Patient-specific models of cardiac mechanics are constructed through anatomy reconstruction from medical images, representation of myocardial mesostructure, capture of material behavior, definition of model geometry and boundary conditions, coupling of multiple physics, and selection of numerical methods. Many of these choices reflect a tradeoff between how closely the model matches real physiology and how complex the model becomes to build and solve. The review summarizes recent developments and open questions in these areas and concludes that distinguishing essential complexities from those that can be safely simplified is the key step toward clinical translation.

What carries the argument

The fidelity-complexity tradeoff that governs decisions across anatomy reconstruction, mesostructure, material properties, boundary conditions, multi-physics coupling, and numerical methods in patient-specific cardiac models.

If this is right

  • Models that retain only essential features can run faster and support real-time decision support in surgery or device placement.
  • Prioritization of research can shift toward the remaining unresolved questions in areas such as mesostructure and multi-physics coupling.
  • Guidelines for safe simplification can emerge, reducing the time and expertise needed to build usable patient-specific models.
  • Clinical adoption can accelerate once models are shown to deliver reliable predictions with manageable complexity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same fidelity-complexity lens could be applied to computational models of other organs or organ systems facing similar translation barriers.
  • Direct head-to-head validation studies on identical clinical datasets would provide concrete data on which simplifications preserve accuracy.
  • Machine-learning techniques might be used to systematically test the impact of omitting individual modeling components across large patient cohorts.

Load-bearing premise

The papers selected for the review are representative enough of the field to reliably separate modeling features that are required for clinical utility from those that can be omitted.

What would settle it

A controlled comparison on the same patient data showing that a model simplified according to current guidance produces clinically unacceptable errors in outcome prediction while a more complex version does not.

Figures

Figures reproduced from arXiv: 2509.07971 by Aaron L. Brown, Alison L. Marsden, Daniel B. Ennis, Ju Liu.

Figure 1
Figure 1. Figure 1: Summary of the major modeling considerations required when developing a personalized computational heart [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: A) Major structures of the heart. B) Layers of the heart wall. Created in [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Using a Laplace-Dirichlet rule-based method (LDRBM) [ [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Representative cardiac geometries commonly used in the literature. Four-chamber models, sometimes [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustrations showing the anatomic structures surrounding the heart, on the superior, inferior, anterior, posterior, [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 1
Figure 1. Figure 1: Electrical activity in the heart is commonly modeled using a reaction-diffusion partial differential equation [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
read the original abstract

Patient-specific computational models of the heart are powerful tools for cardiovascular research and medicine, with demonstrated applications in treatment planning, device evaluation, and surgical decision-making. Yet constructing such models is inherently difficult, reflecting the extraordinary complexity of the heart itself. Numerous considerations are required, including reconstructing the anatomy from medical images, representing myocardial mesostructure, capturing material behavior, defining model geometry and boundary conditions, coupling multiple physics, and selecting numerical methods. Many of these choices involve a tradeoff between physiological fidelity and modeling complexity. In this review, we summarize recent advances and unresolved questions in each of these areas, with particular emphasis on cardiac tissue mechanics. We argue that clarifying which complexities are essential, and which can be safely simplified, will be key to enabling clinical translation of these models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. This review summarizes recent advances in patient-specific computational models of cardiac mechanics, addressing anatomy reconstruction from medical images, myocardial mesostructure representation, material constitutive behavior, geometry and boundary conditions, multiphysics coupling, and numerical methods. It highlights tradeoffs between physiological fidelity and modeling complexity, and argues that distinguishing essential features from those that can be safely simplified is key to clinical translation.

Significance. A balanced and representative survey of this type could help researchers prioritize modeling decisions and accelerate translation of cardiac models into treatment planning and device evaluation. The paper's value rests on its ability to synthesize evidence on which complexities have been shown dispensable in validation studies rather than merely enumerating open questions.

major comments (2)
  1. [Abstract and Introduction] Abstract and opening paragraphs: the central claim that 'clarifying which complexities are essential, and which can be safely simplified, will be key to enabling clinical translation' is not supported by synthesis of head-to-head validation studies; the manuscript catalogs recent papers and unresolved questions but does not evaluate evidence that specific choices (e.g., mesostructure inclusion or multiphysics coupling) can be omitted without loss of accuracy against clinical endpoints.
  2. [Material behavior] Section on material behavior and constitutive laws: no quantitative references or cited benchmarks are provided showing which simplifications (isotropic vs. transversely isotropic models, or reduced-order representations) preserve predictive power in patient-specific settings; this leaves the tradeoff discussion descriptive rather than evidence-based.
minor comments (2)
  1. [Figures] Figure captions could more explicitly annotate the fidelity-complexity tradeoffs illustrated in the modeling pipelines.
  2. A small number of references predate 2020; updating with 2023-2024 clinical validation studies would strengthen currency.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and insightful comments, which help us better align the review with its goal of supporting clinical translation. We address each major comment below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract and Introduction] Abstract and opening paragraphs: the central claim that 'clarifying which complexities are essential, and which can be safely simplified, will be key to enabling clinical translation' is not supported by synthesis of head-to-head validation studies; the manuscript catalogs recent papers and unresolved questions but does not evaluate evidence that specific choices (e.g., mesostructure inclusion or multiphysics coupling) can be omitted without loss of accuracy against clinical endpoints.

    Authors: We appreciate the referee's observation that stronger linkage to comparative validation evidence would better substantiate the central claim. The manuscript is structured as a broad survey of recent advances and open challenges across multiple modeling aspects rather than a systematic meta-analysis. We agree that explicitly referencing head-to-head studies would strengthen the argument. In the revised manuscript we will expand the introduction and add a dedicated paragraph synthesizing available comparative validation results (e.g., studies showing limited impact of certain mesostructure details or boundary-condition simplifications on clinical endpoints such as strain or pressure-volume loops). Where direct head-to-head evidence remains sparse, we will note this limitation and its implications for future work. revision: yes

  2. Referee: [Material behavior] Section on material behavior and constitutive laws: no quantitative references or cited benchmarks are provided showing which simplifications (isotropic vs. transversely isotropic models, or reduced-order representations) preserve predictive power in patient-specific settings; this leaves the tradeoff discussion descriptive rather than evidence-based.

    Authors: We acknowledge that the material-behavior section would be more useful if it included quantitative benchmarks. We will revise this section to incorporate specific citations to patient-specific studies that directly compare isotropic versus anisotropic constitutive models (and reduced-order approximations) against experimental or clinical metrics such as myocardial strain, wall stress, or ejection fraction. This will allow the tradeoff discussion to rest on reported predictive differences rather than remaining purely descriptive. revision: yes

Circularity Check

0 steps flagged

Review catalogs literature without derivations or self-referential predictions

full rationale

This is a survey paper summarizing recent advances and open questions in cardiac mechanics modeling. The central claim—that clarifying essential vs. safely omittable complexities will enable clinical translation—is presented as a perspective on existing literature rather than a quantity derived from equations, fitted parameters, or self-referential steps. No derivations, predictions, or load-bearing self-citations that reduce the argument to its own inputs are present. The paper is self-contained as a review against external benchmarks in the cited studies, warranting only a minimal score for any incidental self-references.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

As a review the central claim rests on the representativeness of the surveyed literature and the premise that tradeoffs between fidelity and complexity can be clarified through synthesis; no new free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5660 in / 1120 out tokens · 37526 ms · 2026-05-18T17:53:21.879805+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

300 extracted references · 300 canonical work pages

  1. [1]

    Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European Heart Journal, 45(45):4808–4821, December 2024

    Phyllis M Thangaraj, Sean H Benson, Evangelos K Oikonomou, Folkert W Asselbergs, and Rohan Khera. Cardiovascular care with digital twin technology in the era of generative artificial intelligence.European Heart Journal, 45(45):4808–4821, December 2024. ISSN 0195-668X. doi:10.1093/eurheartj/ehae619. URL https://doi.org/10.1093/eurheartj/ehae619

    work page doi:10.1093/eurheartj/ehae619 2024

  2. [2]

    Figtree, David F

    Genevieve Coorey, Gemma A. Figtree, David F. Fletcher, Victoria J. Snelson, Stephen Thomas Vernon, David Winlaw, Stuart M. Grieve, Alistair McEwan, Jean Yee Hwa Yang, Pierre Qian, Kieran O’Brien, Jessica Orchard, Jinman Kim, Sanjay Patel, and Julie Redfern. The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary ...

    work page doi:10.1038/s41746-022-00640-7 2022

  3. [3]

    Inan, Ramakrishna Mukkamala, Jeffrey Palmer, David Paydarfar, Roderic I

    Kaan Sel, Deen Osman, Fatemeh Zare, Sina Masoumi Shahrbabak, Laura Brattain, Jin-Oh Hahn, Omer T. Inan, Ramakrishna Mukkamala, Jeffrey Palmer, David Paydarfar, Roderic I. Pettigrew, Arshed A. Quyyumi, Brian Telfer, and Roozbeh Jafari. Building Digital Twins for Cardiovascular Health: From Principles to Clinical Impact.Journal of the American Heart Associa...

    work page doi:10.1161/jaha.123.031981 2024

  4. [4]

    Comparison of novel ventricular pacing strategies using an electro-mechanical simulation platform.EP Europace, 25(6):euad144, June 2023

    Roel Meiburg, Jesse H J Rijks, Ahmed S Beela, Edoardo Bressi, Domenico Grieco, Tammo Delhaas, Justin G LM Luermans, Frits W Prinzen, Kevin Vernooy, and Joost Lumens. Comparison of novel ventricular pacing strategies using an electro-mechanical simulation platform.EP Europace, 25(6):euad144, June 2023. ISSN 1099-5129. doi:10.1093/europace/euad144. URLhttps...

    work page doi:10.1093/europace/euad144 2023

  5. [5]

    Personalized computational electro-mechanics simulations to optimize cardiac resynchronization therapy.Biomechanics and Modeling in Mechanobiology, August 2024

    Emilia Capuano, Francesco Regazzoni, Massimiliano Maines, Silvia Fornara, Vanessa Locatelli, Domenico Catanzariti, Simone Stella, Fabio Nobile, Maurizio Del Greco, and Christian Vergara. Personalized computational electro-mechanics simulations to optimize cardiac resynchronization therapy.Biomechanics and Modeling in Mechanobiology, August 2024. ISSN 1617...

    work page doi:10.1007/s10237-024-01878-8 2024

  6. [6]

    Nikolic, Rakesh Mishra, Mark B

    Lik Chuan Lee, Liang Ge, Zhihong Zhang, Matthew Pease, Serjan D. Nikolic, Rakesh Mishra, Mark B. Ratcliffe, and Julius M. Guccione. Patient-specific finite element modeling of the Cardiokinetix Parachute® device: effects on left ventricular wall stress and function.Medical & Biological Engineering & Computing, 52(6): 557–566, June 2014. ISSN 1741-0444. do...

    work page doi:10.1007/s11517-014-1159-5 2014

  7. [7]

    A Hybrid Experimental-Computational Modeling Frame- work for Cardiovascular Device Testing.Journal of Biomechanical Engineering, 141(051012), March 2019

    Ethan Kung, Masoud Farahmand, and Akash Gupta. A Hybrid Experimental-Computational Modeling Frame- work for Cardiovascular Device Testing.Journal of Biomechanical Engineering, 141(051012), March 2019. ISSN 0148-0731. doi:10.1115/1.4042665. URLhttps://doi.org/10.1115/1.4042665

    work page doi:10.1115/1.4042665 2019

  8. [8]

    Ramachandra, Jack Boyd, Alison L

    Jongmin Seo, Abhay B. Ramachandra, Jack Boyd, Alison L. Marsden, and Andrew M. Kahn. Computational evaluation of venous graft geometries in coronary artery bypass surgery.Seminars in thoracic and cardiovascular surgery, 34(2):521–532, 2022. ISSN 1043-0679. doi:10.1053/j.semtcvs.2021.03.007. URL https://www. ncbi.nlm.nih.gov/pmc/articles/PMC8429518/

    work page doi:10.1053/j.semtcvs.2021.03.007 2022

  9. [9]

    Cameron Dowling, Sami Firoozi, and Stephen J. Brecker. First-in-Human Experience With Patient-Specific Computer Simulation of TA VR in Bicuspid Aortic Valve Morphology.JACC: Cardiovascular Interven- tions, 13(2):184–192, January 2020. ISSN 1936-8798. doi:10.1016/j.jcin.2019.07.032. URL https: //www.sciencedirect.com/science/article/pii/S1936879819316115

    work page doi:10.1016/j.jcin.2019.07.032 2020

  10. [10]

    Nam, Ashraful Abid, Christian Herz, Christopher E

    Chad Vigil, Andras Lasso, Reena Ghosh, Csaba Pinter, Alana Cianciulli, Hannah H. Nam, Ashraful Abid, Christian Herz, Christopher E. Mascio, Jonathan Chen, Stephanie Fuller, Kevin Whitehead, and Matthew A. Jolley. Modeling Tool for Rapid Virtual Planning of the Intracardiac Baffle in Double Outlet Right Ventricle.The Annals of thoracic surgery, 111(6):2078...

    work page doi:10.1016/j.athoracsur.2021.02.058 2078

  11. [11]

    Ryan P O’Hara, Edem Binka, Adityo Prakosa, Stefan L Zimmerman, Mark J Cartoski, M Roselle Abraham, Dai- Yin Lu, Patrick M Boyle, and Natalia A Trayanova. Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy.eLife, 11:e73325, 23 Cardiac mechanics modeling: recent d...

    work page doi:10.7554/elife.73325 2022

  12. [12]

    Causes of altered ventricular mechanics in hypertrophic cardiomyopathy: an in-silico study.BioMedical Engineering OnLine, 20(1):69, July 2021

    Ekaterina Kovacheva, Tobias Gerach, Steffen Schuler, Marco Ochs, Olaf Dössel, and Axel Loewe. Causes of altered ventricular mechanics in hypertrophic cardiomyopathy: an in-silico study.BioMedical Engineering OnLine, 20(1):69, July 2021. ISSN 1475-925X. doi:10.1186/s12938-021-00900-9. URL https://doi.org/ 10.1186/s12938-021-00900-9

    work page doi:10.1186/s12938-021-00900-9 2021

  13. [13]

    Teitgen, Marcus T

    Abigail E. Teitgen, Marcus T. Hock, Kimberly J. McCabe, Matthew C. Childers, Gary A. Huber, Bahador Marzban, Daniel A. Beard, J. Andrew McCammon, Michael Regnier, and Andrew D. McCulloch. Multiscale modeling shows how 2’-deoxy-ATP rescues ventricular function in heart failure.Proceedings of the National Academy of Sciences, 121(35):e2322077121, August 202...

    work page doi:10.1073/pnas.2322077121 2024

  14. [14]

    Augustin, Savannah F

    Alejandro Gonzalo, Christoph M. Augustin, Savannah F. Bifulco, Åshild Telle, Yaacoub Chahine, Ahmad Kassar, Manuel Guerrero-Hurtado, Eduardo Durán, Pablo Martínez-Legazpi, Oscar Flores, Javier Bermejo, Gernot Plank, Nazem Akoum, Patrick M. Boyle, and Juan C. del Alamo. Multiphysics simulations reveal haemodynamic impacts of patient-derived fibrosis-relate...

    work page doi:10.1113/jp287011 2024

  15. [15]

    Role of tissue biomechanics in the formation and function of myocardial tra- beculae in zebrafish embryos.The Journal of Physiology, 602(4):597–617, February 2024

    Adriana Gaia Cairelli, Alex Gendernalik, Wei Xuan Chan, Phuc Nguyen, Julien Vermot, Juhyun Lee, David Bark, and Choon Hwai Yap. Role of tissue biomechanics in the formation and function of myocardial tra- beculae in zebrafish embryos.The Journal of Physiology, 602(4):597–617, February 2024. ISSN 1469-7793. doi:10.1113/JP285490

    work page doi:10.1113/jp285490 2024

  16. [16]

    Classifying Drugs by their Arrhythmo- genic Risk Using Machine Learning.Biophysical Journal, 118(5):1165–1176, March 2020

    Francisco Sahli-Costabal, Kinya Seo, Euan Ashley, and Ellen Kuhl. Classifying Drugs by their Arrhythmo- genic Risk Using Machine Learning.Biophysical Journal, 118(5):1165–1176, March 2020. ISSN 0006-

    work page 2020

  17. [17]

    URL https://www.sciencedirect.com/science/article/pii/ S0006349520300382

    doi:10.1016/j.bpj.2020.01.012. URL https://www.sciencedirect.com/science/article/pii/ S0006349520300382

    work page doi:10.1016/j.bpj.2020.01.012 2020

  18. [18]

    Peirlinck, J

    M. Peirlinck, J. Yao, F. Sahli Costabal, and E. Kuhl. How drugs modulate the performance of the human heart. Computational Mechanics, 69(6):1397–1411, June 2022. ISSN 1432-0924. doi:10.1007/s00466-022-02146-1. URLhttps://doi.org/10.1007/s00466-022-02146-1

    work page doi:10.1007/s00466-022-02146-1 2022

  19. [19]

    A deep-learning approach for direct whole- heart mesh reconstruction.Medical Image Analysis, 74:102222, December 2021

    Fanwei Kong, Nathan Wilson, and Shawn Shadden. A deep-learning approach for direct whole- heart mesh reconstruction.Medical Image Analysis, 74:102222, December 2021. ISSN 1361-8415. doi:10.1016/j.media.2021.102222. URL https://www.sciencedirect.com/science/article/pii/ S136184152100267X

    work page doi:10.1016/j.media.2021.102222 2021

  20. [20]

    Advancements in cardiac structures segmentation: a comprehen- sive systematic review of deep learning in CT imaging.Frontiers in Cardiovascular Medicine, 11, January 2024

    Turki Nasser Alnasser, Lojain Abdulaal, Ahmed Maiter, Michael Sharkey, Krit Dwivedi, Mahan Salehi, Pankaj Garg, Andrew James Swift, and Samer Alabed. Advancements in cardiac structures segmentation: a comprehen- sive systematic review of deep learning in CT imaging.Frontiers in Cardiovascular Medicine, 11, January 2024. ISSN 2297-055X. doi:10.3389/fcvm.20...

    work page doi:10.3389/fcvm.2024.1323461 2024

  21. [21]

    Chen, and Vijay Vedula

    Lei Shi, Ian Y . Chen, and Vijay Vedula. Personalized Multiscale Modeling of Left Atrial Mechanics and Blood Flow, May 2025. URL https://www.biorxiv.org/content/10.1101/2025.04.26.650771v1. Pages: 2025.04.26.650771 Section: New Results

    work page doi:10.1101/2025.04.26.650771v1 2025

  22. [22]

    Marco Fedele and Alfio Quarteroni. Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function.International Journal for Numerical Methods in Biomedical Engineering, 37(4):e3435, 2021. ISSN 2040-7947. doi:10.1002/cnm.3435. URL https://onlinelibrary.wiley.com/ doi/abs/10.1002/cnm.3435. _eprint: https://online...

    work page doi:10.1002/cnm.3435 2021

  23. [23]

    Wilson, Gregory B

    Alexander J. Wilson, Gregory B. Sands, Ian J. LeGrice, Alistair A. Young, and Daniel B. Ennis. Myocardial mesostructure and mesofunction.American Journal of Physiology-Heart and Circulatory Physiology, 323(2): H257–H275, August 2022. ISSN 0363-6135. doi:10.1152/ajpheart.00059.2022. URL https://journals. physiology.org/doi/full/10.1152/ajpheart.00059.2022....

    work page doi:10.1152/ajpheart.00059.2022 2022

  24. [24]

    Ennis, Leon Axel, Pierre Croisille, Pedro F

    Erica Dall’Armellina, Daniel B. Ennis, Leon Axel, Pierre Croisille, Pedro F. Ferreira, Alexander Gotschy, David Lohr, Kevin Moulin, Christopher T. Nguyen, Sonja Nielles-Vallespin, William Romero, Andrew D. Scott, Christian Stoeck, Irvin Teh, Elizabeth M. Tunnicliffe, Magalie Viallon, Victoria Wang, Alistair A. Young, Jürgen E. Schneider, and David E. Sosn...

    work page doi:10.1016/j.jocmr.2024.101109 2025

  25. [25]

    Africa, Marco Fedele, Christian Vergara, Luca Dedè, Antonio F

    Roberto Piersanti, Pasquale C. Africa, Marco Fedele, Christian Vergara, Luca Dedè, Antonio F. Corno, and Alfio Quarteroni. Modeling cardiac muscle fibers in ventricular and atrial electrophysiology simu- lations.Computer Methods in Applied Mechanics and Engineering, 373:113468, January 2021. ISSN 00457825. doi:10.1016/j.cma.2020.113468. URL https://linkin...

    work page doi:10.1016/j.cma.2020.113468 2021

  26. [26]

    Hurtado, and Ellen Kuhl

    Francisco Sahli Costabal, Daniel E. Hurtado, and Ellen Kuhl. Generating Purkinje networks in the human heart.Journal of Biomechanics, 49(12):2455–2465, August 2016. ISSN 0021-9290. doi:10.1016/j.jbiomech.2015.12.025. URL https://www.sciencedirect.com/science/article/pii/ S0021929015007332

    work page doi:10.1016/j.jbiomech.2015.12.025 2016

  27. [27]

    Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps.IEEE Transactions on Medical Imaging, 40(8): 2182–2194, August 2021

    Fernando Barber, Peter Langfield, Miguel Lozano, Ignacio García-Fernández, Josselin Duchateau, Mélèze Hocini, Michel Haïssaguerre, Edward Vigmond, and Rafael Sebastian. Estimation of Personalized Minimal Purkinje Systems From Human Electro-Anatomical Maps.IEEE Transactions on Medical Imaging, 40(8): 2182–2194, August 2021. ISSN 1558-254X. doi:10.1109/TMI....

    work page doi:10.1109/tmi.2021.3073499 2021

  28. [28]

    Julia Camps, Lucas Arantes Berg, Zhinuo Jenny Wang, Rafael Sebastian, Leto Luana Riebel, Ruben Doste, Xin Zhou, Rafael Sachetto, James Coleman, Brodie Lawson, Vicente Grau, Kevin Burrage, Alfonso Bueno-Orovio, Rodrigo Weber dos Santos, and Blanca Rodriguez. Digital twinning of the human ventricular activation sequence to Clinical 12-lead ECGs and magnetic...

    work page doi:10.1016/j.media.2024.103108 2024

  29. [29]

    Ghosh, Matteo Bianchi, Filippo Piatti, Francesca R

    Monica Emendi, Francesco Sturla, Ram P. Ghosh, Matteo Bianchi, Filippo Piatti, Francesca R. Pluchinotta, Daniel Giese, Massimo Lombardi, Alberto Redaelli, and Danny Bluestein. Patient-Specific Bicuspid Aortic Valve Biomechanics: A Magnetic Resonance Imaging Integrated Fluid–Structure Interaction Approach.Annals of Biomedical Engineering, 49(2):627–641, Fe...

    work page doi:10.1007/s10439-020-02571- 2021

  30. [30]

    Company: Springer Distributor: Springer Institution: Springer Label: Springer Number: 2 Publisher: Springer International Publishing

    URL https://link.springer.com/article/10.1007/s10439-020-02571-4 . Company: Springer Distributor: Springer Institution: Springer Label: Springer Number: 2 Publisher: Springer International Publishing

    work page doi:10.1007/s10439-020-02571-4

  31. [31]

    A general three-dimensional parametric geometry of the native aortic valve and root for biomechanical modeling.Journal of Biomechanics, 45(14):2392–2397, September 2012

    Rami Haj-Ali, Gil Marom, Sagit Ben Zekry, Moshe Rosenfeld, and Ehud Raanani. A general three-dimensional parametric geometry of the native aortic valve and root for biomechanical modeling.Journal of Biomechanics, 45(14):2392–2397, September 2012. ISSN 0021-9290. doi:10.1016/j.jbiomech.2012.07.017. URL https: //www.sciencedirect.com/science/article/pii/S00...

    work page doi:10.1016/j.jbiomech.2012.07.017 2012

  32. [32]

    Kaiser, Rohan Shad, William Hiesinger, and Alison L

    Alexander D. Kaiser, Rohan Shad, William Hiesinger, and Alison L. Marsden. A design-based model of the aortic valve for fluid-structure interaction.Biomechanics and Modeling in Mechanobiology, 20(6):2413–2435, December 2021. ISSN 1617-7940. doi:10.1007/s10237-021-01516-7. URL https://link.springer.com/ article/10.1007/s10237-021-01516-7 . Company: Springe...

    work page doi:10.1007/s10237-021-01516-7 2021

  33. [33]

    Data integration for the numerical simulation of cardiac electrophysiology.Pacing and Clinical Electrophysiology, 44(4):726–736, 2021

    Stefano Pagani, Luca Dede’, Andrea Manzoni, and Alfio Quarteroni. Data integration for the numerical simulation of cardiac electrophysiology.Pacing and Clinical Electrophysiology, 44(4):726–736, 2021. ISSN 1540-8159. doi:10.1111/pace.14198. URL https://onlinelibrary.wiley.com/doi/abs/10.1111/pace. 14198. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10...

    work page doi:10.1111/pace.14198 2021

  34. [34]

    lifex-ep: a robust and efficient software for cardiac electrophysiology simulations.BMC Bioinformatics, 24(1):389, October 2023

    Pasquale Claudio Africa, Roberto Piersanti, Francesco Regazzoni, Michele Bucelli, Matteo Salvador, Marco Fedele, Stefano Pagani, Luca Dede’, and Alfio Quarteroni. lifex-ep: a robust and efficient software for cardiac electrophysiology simulations.BMC Bioinformatics, 24(1):389, October 2023. ISSN 1471-2105. doi:10.1186/s12859-023-05513-8. URLhttps://doi.or...

    work page doi:10.1186/s12859-023-05513-8 2023

  35. [35]

    Sack, Pieter De Backer, Pedro Morais, Patrick Segers, Thomas Franz, and Matthieu De Beule

    Mathias Peirlinck, Kevin L. Sack, Pieter De Backer, Pedro Morais, Patrick Segers, Thomas Franz, and Matthieu De Beule. Kinematic boundary conditions substantially impact in silico ventricular function.Inter- national Journal for Numerical Methods in Biomedical Engineering, 35(1):e3151, 2019. ISSN 2040-7947. doi:10.1002/cnm.3151. URL https://onlinelibrary....

    work page doi:10.1002/cnm.3151 2019

  36. [36]

    Pfaller, Julia M

    Martin R. Pfaller, Julia M. Hörmann, Martina Weigl, Andreas Nagler, Radomir Chabiniok, Cristóbal Bertoglio, and Wolfgang A. Wall. The importance of the pericardium for cardiac biomechanics: from physiology to computational modeling.Biomechanics and Modeling in Mechanobiology, 18(2):503–529, April 2019. ISSN 1617-7940. doi:10.1007/s10237-018-1098-4. URLhtt...

    work page doi:10.1007/s10237-018-1098-4 2019

  37. [37]

    Vignon-Clementel, C

    Irene E. Vignon-Clementel, C. Alberto Figueroa, Kenneth E. Jansen, and Charles A. Taylor. Outflow boundary conditions for three-dimensional finite element modeling of blood flow and pressure in arteries. 25 Cardiac mechanics modeling: recent developments and current challenges Computer Methods in Applied Mechanics and Engineering, 195(29):3776–3796, June ...

    work page 2006

  38. [38]

    URL https://www.sciencedirect.com/science/article/pii/ S0045782505002586

    doi:10.1016/j.cma.2005.04.014. URL https://www.sciencedirect.com/science/article/pii/ S0045782505002586

    work page doi:10.1016/j.cma.2005.04.014 2005

  39. [39]

    Niestrawska, Christoph M

    Justyna A. Niestrawska, Christoph M. Augustin, and Gernot Plank. Computational modeling of cardiac growth and remodeling in pressure overloaded hearts—Linking microstructure to organ phenotype.Acta Biomaterialia, 106:34–53, April 2020. ISSN 1742-7061. doi:10.1016/j.actbio.2020.02.010. URL https: //www.sciencedirect.com/science/article/pii/S1742706120300891

    work page doi:10.1016/j.actbio.2020.02.010 2020

  40. [40]

    Michler, A

    C. Michler, A. N. Cookson, R. Chabiniok, E. Hyde, J. Lee, M. Sinclair, T. Sochi, A. Goyal, G. Vigueras, D. A. Nordsletten, and N. P Smith. A computationally efficient framework for the sim- ulation of cardiac perfusion using a multi-compartment Darcy porous-media flow model.International Journal for Numerical Methods in Biomedical Engineering, 29(2):217–2...

    work page doi:10.1002/cnm.2520 2013

  41. [41]

    Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies.Fluids, 7(3): 94, March 2022

    Trung Bao Le, Mustafa Usta, Cyrus Aidun, Ajit Yoganathan, and Fotis Sotiropoulos. Computational Methods for Fluid-Structure Interaction Simulation of Heart Valves in Patient-Specific Left Heart Anatomies.Fluids, 7(3): 94, March 2022. ISSN 2311-5521. doi:10.3390/fluids7030094. URL https://www.mdpi.com/2311-5521/ 7/3/94. Number: 3 Publisher: Multidisciplina...

    work page doi:10.3390/fluids7030094 2022

  42. [42]

    Andrea Tonini, Francesco Regazzoni, Matteo Salvador, Luca Dede’, Roberto Scrofani, Laura Fusini, Chiara Cogliati, Gianluca Pontone, Christian Vergara, and Alfio Quarteroni. Two New Calibra- tion Techniques of Lumped-Parameter Mathematical Models for the Cardiovascular System.Inter- national Journal for Numerical Methods in Engineering, 126(1):e7648, 2025....

    work page doi:10.1002/nme.7648 2025

  43. [43]

    Chen, Hiroo Takayama, and Vijay Vedula

    Lei Shi, Ian Y . Chen, Hiroo Takayama, and Vijay Vedula. An optimization framework to personalize passive cardiac mechanics.Computer Methods in Applied Mechanics and Engineering, 432:117401, December 2024. ISSN 0045-7825. doi:10.1016/j.cma.2024.117401. URL https://www.sciencedirect.com/science/ article/pii/S004578252400656X

    work page doi:10.1016/j.cma.2024.117401 2024

  44. [44]

    Integrating imaging and invasive pressure data into a multi-scale whole-heart model.Journal of Biomechanical Engineering, pages 1–52, August 2025

    Marina Strocchi, Christoph M Augustin, Matthias A F Gsell, Christopher A Rinaldi, Edward J Vigmond, Gernot Plank, Chris J Oates, Richard D Wilkinson, and Steven A Niederer. Integrating imaging and invasive pressure data into a multi-scale whole-heart model.Journal of Biomechanical Engineering, pages 1–52, August 2025. ISSN 0148-0731. doi:10.1115/1.4069497...

    work page doi:10.1115/1.4069497 2025

  45. [45]

    Fast and robust parameter estimation with uncertainty quantification for the cardiac function.Computer Methods and Programs in Biomedicine, 231: 107402, April 2023

    Matteo Salvador, Francesco Regazzoni, Luca Dede’, and Alfio Quarteroni. Fast and robust parameter estimation with uncertainty quantification for the cardiac function.Computer Methods and Programs in Biomedicine, 231: 107402, April 2023. ISSN 0169-2607. doi:10.1016/j.cmpb.2023.107402. URL https://www.sciencedirect. com/science/article/pii/S016926072300069X

    work page doi:10.1016/j.cmpb.2023.107402 2023

  46. [46]

    HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerat- ing Cardiac Mechanics Modeling, April 2025

    Lei Shi, Yurui Chen, and Vijay Vedula. HeartSimSage: Attention-Enhanced Graph Neural Networks for Accelerat- ing Cardiac Mechanics Modeling, April 2025. URLhttp://arxiv.org/abs/2504.18968. arXiv:2504.18968 [physics]

    work page arXiv 2025

  47. [47]

    Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models.Computational Mechanics, 75(2):487–513, February 2025

    Federica Caforio, Francesco Regazzoni, Stefano Pagani, Elias Karabelas, Christoph Augustin, Gundolf Haase, Gernot Plank, and Alfio Quarteroni. Physics-informed neural network estimation of material properties in soft tissue nonlinear biomechanical models.Computational Mechanics, 75(2):487–513, February 2025. ISSN 1432-0924. doi:10.1007/s00466-024-02516-x....

    work page doi:10.1007/s00466-024-02516-x 2025

  48. [48]

    Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models, May 2025

    Matthias Höfler, Francesco Regazzoni, Stefano Pagani, Elias Karabelas, Christoph Augustin, Gundolf Haase, Gernot Plank, and Federica Caforio. Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models, May 2025. URL http://arxiv.org/abs/2505.03382. arXiv:2505.03382 [cs]

    work page arXiv 2025

  49. [49]

    Levrero-Florencio, F

    F. Levrero-Florencio, F. Margara, E. Zacur, A. Bueno-Orovio, Z. J. Wang, A. Santiago, J. Aguado-Sierra, G. Houzeaux, V . Grau, D. Kay, M. Vázquez, R. Ruiz-Baier, and B. Rodriguez. Sensitivity analysis of a strongly- coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers.Computer Methods ...

    work page doi:10.1016/j.cma.2019.112762 2020

  50. [50]

    Augustin, Matthias A

    Marina Strocchi, Stefano Longobardi, Christoph M. Augustin, Matthias A. F. Gsell, Argyrios Petras, Christo- pher A. Rinaldi, Edward J. Vigmond, Gernot Plank, Chris J. Oates, Richard D. Wilkinson, and Steven A. Niederer. Cell to whole organ global sensitivity analysis on a four-chamber heart electromechanics model using Gaussian processes emulators.PLOS Co...

    work page doi:10.1371/journal.pcbi.1011257 2023

  51. [51]

    Sensitivity analysis and inverse uncertainty quantifica- tion for the left ventricular passive mechanics.Biomechanics and Modeling in Mechanobiology, 21(3):953–982, June 2022

    Alan Lazarus, David Dalton, Dirk Husmeier, and Hao Gao. Sensitivity analysis and inverse uncertainty quantifica- tion for the left ventricular passive mechanics.Biomechanics and Modeling in Mechanobiology, 21(3):953–982, June 2022. ISSN 1617-7940. doi:10.1007/s10237-022-01571-8. URL https://link.springer.com/ article/10.1007/s10237-022-01571-8 . Company: ...

    work page doi:10.1007/s10237-022-01571-8 2022

  52. [52]

    Owais Khan, Gianluca Geraci, Koen Nieman, Daniele E

    Karthik Menon, Andrea Zanoni, M. Owais Khan, Gianluca Geraci, Koen Nieman, Daniele E. Schiavazzi, and Alison L. Marsden. Personalized and uncertainty-aware coronary hemodynamics simulations: From Bayesian estimation to improved multi-fidelity uncertainty quantification.Computer Methods and Programs in Biomedicine, 271:108951, November 2025. ISSN 0169-2607...

    work page doi:10.1016/j.cmpb.2025.108951 2025

  53. [53]

    Lee, Jakob Richter, Martin R

    John D. Lee, Jakob Richter, Martin R. Pfaller, Jason M. Szafron, Karthik Menon, Andrea Zanoni, Michael R. Ma, Jeffrey A. Feinstein, Jacqueline Kreutzer, Alison L. Marsden, and Daniele E. Schi- avazzi. A probabilistic neural twin for treatment planning in peripheral pulmonary artery stenosis.Inter- national Journal for Numerical Methods in Biomedical Engin...

    work page doi:10.1002/cnm.3820 2024

  54. [54]

    de Tullio, Francesco S

    Michele Torre, Simone Morganti, Alessandro Nitti, Marco D. de Tullio, Francesco S. Pasqualini, and Alessandro Reali. Isogeometric mixed collocation of nearly-incompressible electromechanics in finite deformations for cardiac muscle simulations.Computer Methods in Applied Mechanics and Engineering, 411:116055, June 2023. ISSN 0045-7825. doi:10.1016/j.cma.2...

    work page doi:10.1016/j.cma.2023.116055 2023

  55. [55]

    Wang, Martyn P

    Èric Lluch, Oscar Camara, Rubén Doste, Bart Bijnens, Mathieu De Craene, Maxime Sermesant, Vicky Y . Wang, Martyn P. Nash, and Hernán G. Morales. Calibration of a fully coupled electromechanical meshless computational model of the heart with experimental data.Computer Methods in Applied Mechanics and Engineering, 364: 112869, June 2020. ISSN 0045-7825. doi...

    work page doi:10.1016/j.cma.2020.112869 2020

  56. [56]

    A software benchmark for cardiac elastodynamics.Computer Methods in Applied Mechanics and Engineering, 435:117485, February 2025

    Reidmen Aróstica, David Nolte, Aaron Brown, Amadeus Gebauer, Elias Karabelas, Javiera Jilberto, Matteo Salvador, Michele Bucelli, Roberto Piersanti, Kasra Osouli, Christoph Augustin, Henrik Finsberg, Lei Shi, Marc Hirschvogel, Martin Pfaller, Pasquale Claudio Africa, Matthias Gsell, Alison Marsden, David Nordsletten, Francesco Regazzoni, Gernot Plank, Joa...

    work page doi:10.1016/j.cma.2024.117485 2025

  57. [57]

    Soares, David S

    Reza Avazmohammadi, João S. Soares, David S. Li, Samarth S. Raut, Robert C. Gorman, and Michael S. Sacks. A Contemporary Look at Biomechanical Models of Myocardium.Annual Re- view of Biomedical Engineering, 21(V olume 21, 2019):417–442, June 2019. ISSN 1523-9829, 1545-

    work page 2019

  58. [58]

    URL https://www.annualreviews.org/content/ journals/10.1146/annurev-bioeng-062117-121129

    doi:10.1146/annurev-bioeng-062117-121129. URL https://www.annualreviews.org/content/ journals/10.1146/annurev-bioeng-062117-121129. Publisher: Annual Reviews

    work page doi:10.1146/annurev-bioeng-062117-121129

  59. [59]

    Niederer, Kenneth S

    Steven A. Niederer, Kenneth S. Campbell, and Stuart G. Campbell. A short history of the development of mathematical models of cardiac mechanics.Journal of Molecular and Cellular Cardiology, 127:11–19, February 2019. ISSN 0022-2828. doi:10.1016/j.yjmcc.2018.11.015. URL https://www.sciencedirect. com/science/article/pii/S0022282818309751

    work page doi:10.1016/j.yjmcc.2018.11.015 2019

  60. [60]

    Bracamonte, Sarah K

    Johane H. Bracamonte, Sarah K. Saunders, John S. Wilson, Uyen T. Truong, and Joao S. Soares. Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications.Applied Sciences, 12(8):3954, January 2022. ISSN 2076-3417. doi:10.3390/app12083954. URL https://www.mdpi...

    work page doi:10.3390/app12083954 2022

  61. [61]

    Cristobal Rodero, Tiffany M. G. Baptiste, Rosie K. Barrows, Alexandre Lewalle, Steven A. Niederer, and Marina Strocchi. Advancing clinical translation of cardiac biomechanics models: a comprehensive re- view, applications and future pathways.Frontiers in Physics, 11, November 2023. ISSN 2296-424X. 27 Cardiac mechanics modeling: recent developments and cur...

    work page doi:10.3389/fphy.2023.1306210 2023

  62. [62]

    Sacks, and Shawn C

    Amirhossein Arzani, Jian-Xun Wang, Michael S. Sacks, and Shawn C. Shadden. Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.Annals of Biomedical Engineering, 50(6): 615–627, June 2022. ISSN 1573-9686. doi:10.1007/s10439-022-02967-4. URL https://doi.org/10.1007/ s10439-022-02967-4

    work page doi:10.1007/s10439-022-02967-4 2022

  63. [63]

    What is a normal blood pressure?European Heart Journal, 39(24):2233–2240, June 2018

    Thomas F Lüscher. What is a normal blood pressure?European Heart Journal, 39(24):2233–2240, June 2018. ISSN 0195-668X. doi:10.1093/eurheartj/ehy330. URLhttps://doi.org/10.1093/eurheartj/ehy330

    work page doi:10.1093/eurheartj/ehy330 2018

  64. [64]

    Acute right ventricular failure—from pathophysiology to new treatments.Intensive Care Medicine, 30(2):185–196, February 2004

    Alexandre Mebazaa, Peter Karpati, Estelle Renaud, and Lars Algotsson. Acute right ventricular failure—from pathophysiology to new treatments.Intensive Care Medicine, 30(2):185–196, February 2004. ISSN 1432-

    work page 2004

  65. [65]

    URL https://link-springer-com.laneproxy.stanford.edu/ article/10.1007/s00134-003-2025-3

    doi:10.1007/s00134-003-2025-3. URL https://link-springer-com.laneproxy.stanford.edu/ article/10.1007/s00134-003-2025-3 . Company: Springer Distributor: Springer Institution: Springer Label: Springer Number: 2 Publisher: Springer Berlin Heidelberg

    work page doi:10.1007/s00134-003-2025-3 2025

  66. [66]

    Echocardiographic measurement of right ven- tricular wall thickness

    Haruo Matsukubo, Tokru Matsuura, Naoto Endo, Jun Asayama, Toshimitsu Watanabe, Keizo Furukawa, Hiroshi Kunishige, Hiroshi Katsume, and Hamao Ijichi. Echocardiographic measurement of right ven- tricular wall thickness. A new application of subxiphoid echocardiography., August 1977. URL https: //www.ahajournals.org/doi/epdf/10.1161/01.CIR.56.2.278

    work page doi:10.1161/01.cir.56.2.278 1977

  67. [67]

    A comprehensive and biophysically detailed computational model of the whole human heart electromechanics.Computer Methods in Applied Mechanics and Engineering, 410:115983, May 2023

    Marco Fedele, Roberto Piersanti, Francesco Regazzoni, Matteo Salvador, Pasquale Claudio Africa, Michele Bucelli, Alberto Zingaro, Luca Dede’, and Alfio Quarteroni. A comprehensive and biophysically detailed computational model of the whole human heart electromechanics.Computer Methods in Applied Mechanics and Engineering, 410:115983, May 2023. ISSN 0045-7...

    work page doi:10.1016/j.cma.2023.115983 2023

  68. [68]

    Peirlinck, F

    M. Peirlinck, F. Sahli Costabal, J. Yao, J. M. Guccione, S. Tripathy, Y . Wang, D. Ozturk, P. Segars, T. M. Morrison, S. Levine, and E. Kuhl. Precision medicine in human heart modeling.Biomechanics and Modeling in Mechanobiology, 20(3):803–831, June 2021. ISSN 1617-7940. doi:10.1007/s10237-021-01421-z. URL https://doi.org/10.1007/s10237-021-01421-z

    work page doi:10.1007/s10237-021-01421-z 2021

  69. [69]

    Zygote Solid 3D Heart Generations I & II Development Report

    Zygote Media Group Inc. Zygote Solid 3D Heart Generations I & II Development Report. Technical report, 2014

    work page 2014

  70. [70]

    Fox, Robert L

    Brian Baillargeon, Nuno Rebelo, David D. Fox, Robert L. Taylor, and Ellen Kuhl. The Living Heart Project: A robust and integrative simulator for human heart function.European journal of mechanics. A, Solids, 48:38–47,

    work page

  71. [71]

    doi:10.1016/j.euromechsol.2014.04.001

    ISSN 0997-7538. doi:10.1016/j.euromechsol.2014.04.001. URL https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC4175454/

    work page doi:10.1016/j.euromechsol.2014.04.001 2014

  72. [72]

    Cajas, Eva Casoni, Eva Casoni, Mariano Vázquez, and Mariano Vázquez

    Alfonso Santiago, Alfonso Santiago, Jazmín Aguado-Sierra, Jazmin Aguado-Sierra, Miguel Zavala-Aké, Miguel Zavala-Aké, Ruben Doste, Rubén Doste, Ruben Doste-Beltran, Samuel Gómez, Samuel Gómez, Ruth Arís, Ruth Arís, Juan Carlos Cajas, J.C. Cajas, Eva Casoni, Eva Casoni, Mariano Vázquez, and Mariano Vázquez. Fully coupled fluid-electro-mechanical model of t...

    work page doi:10.1002/cnm.3140 2018

  73. [73]

    Augustin, Matthias A

    Marina Strocchi, Christoph M. Augustin, Matthias A. F. Gsell, Elias Karabelas, Aurel Neic, Karli Gillette, Orod Razeghi, Anton J. Prassl, Edward J. Vigmond, Jonathan M. Behar, Justin Gould, Baldeep Sidhu, Christopher A. Rinaldi, Martin J. Bishop, Gernot Plank, and Steven A. Niederer. A publicly available virtual cohort of four- chamber heart meshes for ca...

    work page doi:10.1371/journal.pone.0235145 2020

  74. [74]

    Theo Arts, Tammo Delhaas, Peter Bovendeerd, Xander Verbeek, and Frits W. Prinzen. Adaptation to me- chanical load determines shape and properties of heart and circulation: the CircAdapt model.American Journal of Physiology-Heart and Circulatory Physiology, 288(4):H1943–H1954, April 2005. ISSN 0363-

    work page 2005

  75. [75]

    URL https://journals.physiology.org/doi/full/10.1152/ ajpheart.00444.2004

    doi:10.1152/ajpheart.00444.2004. URL https://journals.physiology.org/doi/full/10.1152/ ajpheart.00444.2004. Publisher: American Physiological Society

    work page doi:10.1152/ajpheart.00444.2004 2004

  76. [76]

    Roney, Caroline Mendonca Costa, Gernot Plank, Pablo Lamata, and Steven A

    Marina Strocchi, Cristobal Rodero, Caroline H. Roney, Caroline Mendonca Costa, Gernot Plank, Pablo Lamata, and Steven A. Niederer. A Semi-automatic Pipeline for Generation of Large Cohorts of Four-Chamber Heart Meshes. In Michael Regnier and Matthew Childers, editors,Familial Cardiomyopathies: Methods and Protocols, pages 117–127. Springer US, New York, N...

    work page doi:10.1007/978-1-0716-3527- 2024

  77. [77]

    TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator.ACM Trans

    Hang Si. TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator.ACM Trans. Math. Softw., 41(2): 11:1–11:36, February 2015. ISSN 0098-3500. doi:10.1145/2629697. URL https://dl.acm.org/doi/10. 1145/2629697

    work page doi:10.1145/2629697 2015

  78. [78]

    {CGAL Editorial Board}, {6.0.1} edition, 2024

    {The CGAL Project}.{CGAL} User and Reference Manual. {CGAL Editorial Board}, {6.0.1} edition, 2024. URLhttps://doc.cgal.org/6.0.1/Manual/packages.html

    work page 2024

  79. [79]

    Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities.International Journal for Numerical Methods in Engineering, 79(11):1309–1331,

    Christophe Geuzaine and Jean-François Remacle. Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities.International Journal for Numerical Methods in Engineering, 79(11):1309–1331,

    work page

  80. [80]

    Geuzaine and J.F

    ISSN 1097-0207. doi:10.1002/nme.2579. URL https://onlinelibrary.wiley.com/doi/abs/10. 1002/nme.2579. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/nme.2579

    work page doi:10.1002/nme.2579

Showing first 80 references.