1
|
Jayathungage Don TD, Safaei S, Maso Talou GD, Russell PS, Phillips ARJ, Reynolds HM. Computational fluid dynamic modeling of the lymphatic system: a review of existing models and future directions. Biomech Model Mechanobiol 2024; 23:3-22. [PMID: 37902894 PMCID: PMC10901951 DOI: 10.1007/s10237-023-01780-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/02/2023] [Indexed: 11/01/2023]
Abstract
Historically, research into the lymphatic system has been overlooked due to both a lack of knowledge and limited recognition of its importance. In the last decade however, lymphatic research has gained substantial momentum and has included the development of a variety of computational models to aid understanding of this complex system. This article reviews existing computational fluid dynamic models of the lymphatics covering each structural component including the initial lymphatics, pre-collecting and collecting vessels, and lymph nodes. This is followed by a summary of limitations and gaps in existing computational models and reasons that development in this field has been hindered to date. Over the next decade, efforts to further characterize lymphatic anatomy and physiology are anticipated to provide key data to further inform and validate lymphatic fluid dynamic models. Development of more comprehensive multiscale- and multi-physics computational models has the potential to significantly enhance the understanding of lymphatic function in both health and disease.
Collapse
Affiliation(s)
| | - Soroush Safaei
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Gonzalo D Maso Talou
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Peter S Russell
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Surgical and Translational Research Centre, Department of Surgery, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Anthony R J Phillips
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Surgical and Translational Research Centre, Department of Surgery, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand
| | - Hayley M Reynolds
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| |
Collapse
|
2
|
Korte J, Klopp ES, Berg P. Multi-Dimensional Modeling of Cerebral Hemodynamics: A Systematic Review. Bioengineering (Basel) 2024; 11:72. [PMID: 38247949 PMCID: PMC10813503 DOI: 10.3390/bioengineering11010072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 12/23/2023] [Accepted: 01/06/2024] [Indexed: 01/23/2024] Open
Abstract
The Circle of Willis (CoW) describes the arterial system in the human brain enabling the neurovascular blood supply. Neurovascular diseases like intracranial aneurysms (IAs) can occur within the CoW and carry the risk of rupture, which can lead to subarachnoid hemorrhage. The assessment of hemodynamic information in these pathologies is crucial for their understanding regarding detection, diagnosis and treatment. Multi-dimensional in silico approaches exist to evaluate these hemodynamics based on patient-specific input data. The approaches comprise low-scale (zero-dimensional, one-dimensional) and high-scale (three-dimensional) models as well as multi-scale coupled models. The input data can be derived from medical imaging, numerical models, literature-based assumptions or from measurements within healthy subjects. Thus, the most realistic description of neurovascular hemodynamics is still controversial. Within this systematic review, first, the models of the three scales (0D, 1D, 3D) and second, the multi-scale models, which are coupled versions of the three scales, were discussed. Current best practices in describing neurovascular hemodynamics most realistically and their clinical applicablility were elucidated. The performance of 3D simulation entails high computational expenses, which could be reduced by analyzing solely the region of interest in detail. Medical imaging to establish patient-specific boundary conditions is usually rare, and thus, lower dimensional models provide a realistic mimicking of the surrounding hemodynamics. Multi-scale coupling, however, is computationally expensive as well, especially when taking all dimensions into account. In conclusion, the 0D-1D-3D multi-scale approach provides the most realistic outcome; nevertheless, it is least applicable. A 1D-3D multi-scale model can be considered regarding a beneficial trade-off between realistic results and applicable performance.
Collapse
Affiliation(s)
- Jana Korte
- Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
- Department of Fluid Dynamics and Technical Flows, University of Magdeburg, 39106 Magdeburg, Germany
| | - Ehlar Sophie Klopp
- Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
- Department of Medical Engineering, University of Magdeburg, 39106 Magdeburg, Germany
| | - Philipp Berg
- Research Campus STIMULATE, University of Magdeburg, 39106 Magdeburg, Germany
- Department of Medical Engineering, University of Magdeburg, 39106 Magdeburg, Germany
| |
Collapse
|
3
|
Pegolotti L, Pfaller MR, Rubio NL, Ding K, Brugarolas Brufau R, Darve E, Marsden AL. Learning reduced-order models for cardiovascular simulations with graph neural networks. Comput Biol Med 2024; 168:107676. [PMID: 38039892 PMCID: PMC10886437 DOI: 10.1016/j.compbiomed.2023.107676] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 10/23/2023] [Accepted: 11/06/2023] [Indexed: 12/03/2023]
Abstract
Reduced-order models based on physics are a popular choice in cardiovascular modeling due to their efficiency, but they may experience loss in accuracy when working with anatomies that contain numerous junctions or pathological conditions. We develop one-dimensional reduced-order models that simulate blood flow dynamics using a graph neural network trained on three-dimensional hemodynamic simulation data. Given the initial condition of the system, the network iteratively predicts the pressure and flow rate at the vessel centerline nodes. Our numerical results demonstrate the accuracy and generalizability of our method in physiological geometries comprising a variety of anatomies and boundary conditions. Our findings demonstrate that our approach can achieve errors below 3% for pressure and flow rate, provided there is adequate training data. As a result, our method exhibits superior performance compared to physics-based one-dimensional models while maintaining high efficiency at inference time.
Collapse
Affiliation(s)
- Luca Pegolotti
- Department of Pediatrics, Stanford University, United States of America; Institute for Computational and Mathematical Engineering, Stanford University, United States of America.
| | - Martin R Pfaller
- Department of Pediatrics, Stanford University, United States of America; Institute for Computational and Mathematical Engineering, Stanford University, United States of America
| | - Natalia L Rubio
- Department of Mechanical Engineering, Stanford University, United States of America
| | - Ke Ding
- Intel Corporation, United States of America
| | | | - Eric Darve
- Institute for Computational and Mathematical Engineering, Stanford University, United States of America; Department of Mechanical Engineering, Stanford University, United States of America
| | - Alison L Marsden
- Department of Pediatrics, Stanford University, United States of America; Institute for Computational and Mathematical Engineering, Stanford University, United States of America; Department of Mechanical Engineering, Stanford University, United States of America; Department of Bioengineering, Stanford University, United States of America
| |
Collapse
|
4
|
Schwarz EL, Pegolotti L, Pfaller MR, Marsden AL. Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. BIOPHYSICS REVIEWS 2023; 4:011301. [PMID: 36686891 PMCID: PMC9846834 DOI: 10.1063/5.0109400] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/12/2022] [Indexed: 01/15/2023]
Abstract
Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.
Collapse
Affiliation(s)
- Erica L. Schwarz
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Luca Pegolotti
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Martin R. Pfaller
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Alison L. Marsden
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
5
|
Pfaller MR, Pham J, Verma A, Pegolotti L, Wilson NM, Parker DW, Yang W, Marsden AL. Automated generation of 0D and 1D reduced-order models of patient-specific blood flow. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3639. [PMID: 35875875 PMCID: PMC9561079 DOI: 10.1002/cnm.3639] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 05/24/2022] [Accepted: 07/19/2022] [Indexed: 06/13/2023]
Abstract
Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.
Collapse
Affiliation(s)
- Martin R. Pfaller
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
- Cardiovascular Institute, Stanford University, CA, USA
| | - Jonathan Pham
- Mechanical Engineering, Stanford University, CA, USA
| | | | - Luca Pegolotti
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
| | | | | | | | - Alison L. Marsden
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
- Cardiovascular Institute, Stanford University, CA, USA
- Bioengineering, Stanford University, CA, USA
| |
Collapse
|
6
|
Zhou J, Li J, Qin S, Liu J, Lin Z, Xie J, Zhang Z, Chen R. High-resolution cerebral blood flow simulation with a domain decomposition method and verified by the TCD measurement. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 224:107004. [PMID: 35841853 DOI: 10.1016/j.cmpb.2022.107004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/24/2022] [Accepted: 07/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND An efficient and accurate blood flow simulation can be useful for understanding many vascular diseases. Accurately resolving the blood flow velocity based on patient-specific geometries and model parameters is still a major challenge because of complex geomerty and turbulence issues. In addition, obtaining results in a short amount of computing time is important so that the simulation can be used in the clinical environment. In this work, we present a parallel scalable method for the patient-specific blood flow simulation with focuses on its parallel performance study and clinical verification. METHODS We adopt a fully implicit unstructured finite element method for a patient-specific simulation of blood flow in a full precerebral artery. The 3D artery is constructed from MRI images, and a parallel Newton-Krylov method preconditioned with a two-level domain decomposition method is adopted to solve the large nonlinear system discretized from the time-dependent 3D Navier-Stokes equations in the artery with an integral outlet boundary condition. The simulated results are verified using the clinical data measured by transcranial Doppler ultrasound, and the parallel performance of the algorithm is studied on a supercomputer. RESULTS The simulated velocity matches the clinical measured data well. Other simulated blood flow parameters, such as pressure and wall shear stress, are within reasonable ranges. The results show that the parallel algorithm scales up to 2160 processors with a 49% parallel efficiency for solving a problem with over 20 million unstructured elements on a supercomputer. For a standard cerebral blood flow simulation case with approximately 4 million finite elements, the calculation of one cardiac cycle can be finished within one hour with 1000 processors. CONCLUSION The proposed method is able to perform high-resolution 3D blood flow simulations in a patient-specific full precerebral artery within an acceptable time, and the simulated results are comparable with the clinical measured data, which demonstrates its high potential for clinical applications.
Collapse
Affiliation(s)
- Jie Zhou
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, China
| | - Jing Li
- School of Mathematics and Statistics, Changsha University of Science and Technology, Changsha, China
| | - Shanlin Qin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
| | - Jia Liu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Zeng Lin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jian Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Rongliang Chen
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing, Shenzhen, China.
| |
Collapse
|
7
|
Baselli G, Laganà MM. The intracranial Windkessel implies arteriovenous pulsatile coupling increased by venous resistances. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103092] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
|
8
|
Qin S, Chen R, Wu B, Shiu WS, Cai XC. Numerical Simulation of Blood Flows in Patient-specific Abdominal Aorta with Primary Organs. Biomech Model Mechanobiol 2021; 20:909-924. [PMID: 33582934 DOI: 10.1007/s10237-021-01419-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/06/2021] [Indexed: 11/29/2022]
Abstract
The abdominal aorta is the largest artery in the abdominal cavity that supplies blood flows to vital organs through the complex visceral arterial branches, including the celiac trunk (the liver, stomach, spleen, etc.), the renal arteries (the kidneys) and the superior and inferior mesenteric arteries (the small and large intestine, pancreas, etc.). An accurate simulation of blood flows in this network of arteries is important for the understanding of the hemodynamics in various organs of healthy and diseased patients, but the computational cost is very high. As a result, most researchers choose to focus on a portion of the artery or use a low-dimensional approximation of the artery. In the present work, we introduce a parallel algorithm for the modeling of pulsatile flows in the abdominal aorta with branches to the primary organs, and an organ-based two-level method for calculating the resistances for the outflow boundary conditions. With this highly parallel approach, the simulation of the blood flow for a cardiac cycle of the anatomically detailed aorta can be obtained within a few hours, and the blood distribution to organs including liver, spleen and kidneys are also computed with certain accuracy. Moreover, we discuss the significant hemodynamic differences resulted from the influence of the peripheral branches. In addition, we examine the accuracy of the results with respect to the mesh size and time-step size and show the high parallel scalability of the proposed algorithm with up to 3000 processor cores.
Collapse
Affiliation(s)
- Shanlin Qin
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Rongliang Chen
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Laboratory for Exascale Engineering and Scientific Computing, Shenzhen, China
| | - Bokai Wu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wen-Shin Shiu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiao-Chuan Cai
- Department of Mathematics, University of Macau, Macau, China.
| |
Collapse
|