1
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Schäfer F, Schiavazzi DE, Hellevik LR, Sturdy J. Global sensitivity analysis with multifidelity Monte Carlo and polynomial chaos expansion for vascular haemodynamics. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2024:e3836. [PMID: 38837871 DOI: 10.1002/cnm.3836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/04/2024] [Accepted: 05/12/2024] [Indexed: 06/07/2024]
Abstract
Computational models of the cardiovascular system are increasingly used for the diagnosis, treatment, and prevention of cardiovascular disease. Before being used for translational applications, the predictive abilities of these models need to be thoroughly demonstrated through verification, validation, and uncertainty quantification. When results depend on multiple uncertain inputs, sensitivity analysis is typically the first step required to separate relevant from unimportant inputs, and is key to determine an initial reduction on the problem dimensionality that will significantly affect the cost of all downstream analysis tasks. For computationally expensive models with numerous uncertain inputs, sample-based sensitivity analysis may become impractical due to the substantial number of model evaluations it typically necessitates. To overcome this limitation, we consider recently proposed Multifidelity Monte Carlo estimators for Sobol' sensitivity indices, and demonstrate their applicability to an idealized model of the common carotid artery. Variance reduction is achieved combining a small number of three-dimensional fluid-structure interaction simulations with affordable one- and zero-dimensional reduced-order models. These multifidelity Monte Carlo estimators are compared with traditional Monte Carlo and polynomial chaos expansion estimates. Specifically, we show consistent sensitivity ranks for both bi- (1D/0D) and tri-fidelity (3D/1D/0D) estimators, and superior variance reduction compared to traditional single-fidelity Monte Carlo estimators for the same computational budget. As the computational burden of Monte Carlo estimators for Sobol' indices is significantly affected by the problem dimensionality, polynomial chaos expansion is found to have lower computational cost for idealized models with smooth stochastic response.
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Affiliation(s)
- Friederike Schäfer
- Division of Biomechanics, Norwegian University of Science and Technology (NTNU), Norway
| | - Daniele E Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana, USA
| | - Leif Rune Hellevik
- Division of Biomechanics, Norwegian University of Science and Technology (NTNU), Norway
| | - Jacob Sturdy
- Division of Biomechanics, Norwegian University of Science and Technology (NTNU), Norway
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2
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Berggren CC, Jiang D, Jack Wang YF, Bergquist JA, Rupp LC, Liu Z, MacLeod RS, Narayan A, Timmins LH. Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification. Biomech Model Mechanobiol 2024; 23:927-940. [PMID: 38361087 PMCID: PMC11102342 DOI: 10.1007/s10237-023-01814-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 12/30/2023] [Indexed: 02/17/2024]
Abstract
Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.
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Affiliation(s)
- Caleb C Berggren
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - David Jiang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Y F Jack Wang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Jake A Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Lindsay C Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Rob S MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Lucas H Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
- School of Engineering Medicine, Texas A&M University, 1020 Holcombe Blvd., Houston, TX, USA.
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA.
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3
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Menon K, Khan MO, Sexton ZA, Richter J, Nguyen PK, Malik SB, Boyd J, Nieman K, Marsden AL. Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees. NPJ IMAGING 2024; 2:9. [PMID: 38706558 PMCID: PMC11062925 DOI: 10.1038/s44303-024-00014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 02/25/2024] [Indexed: 05/07/2024]
Abstract
Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges - incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary anatomies combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. We propose an optimization-based method to personalize multiscale coronary flow simulations by assimilating clinical CT myocardial perfusion imaging and cardiac function measurements to yield patient-specific flow distributions and model parameters. Using this proof-of-concept study on a cohort of six patients, we reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based purely on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.
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Affiliation(s)
- Karthik Menon
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA
| | - Muhammed Owais Khan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON Canada
| | | | - Jakob Richter
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA USA
| | - Patricia K. Nguyen
- VA Palo Alto Healthcare System, Palo Alto, CA USA
- Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA USA
| | | | - Jack Boyd
- Department of Cardiothoracic Surgery, Stanford School of Medicine, Stanford, CA USA
| | - Koen Nieman
- Division of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA USA
- Department of Radiology, Stanford School of Medicine, Stanford, CA USA
| | - Alison L. Marsden
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA USA
- Department of Bioengineering, Stanford University, Stanford, CA USA
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4
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Berggren CC, Jiang D, Jack Wang Y, Bergquist JA, Rupp LC, Liu Z, MacLeod RS, Narayan A, Timmins LH. Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification. ARXIV 2024:arXiv:2401.15047v1. [PMID: 38344225 PMCID: PMC10854278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.
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Affiliation(s)
- Caleb C. Berggren
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - David Jiang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Y.F. Jack Wang
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
| | - Jake A. Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Lindsay C. Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Zexin Liu
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Rob S. MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Nora Eccles Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT, USA
| | - Akil Narayan
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- Department of Mathematics, University of Utah, Salt Lake City, UT, USA
| | - Lucas H. Timmins
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA
- Department of Biomedical Engineering, Texas A&M University, College Station, TX, USA
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5
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Lu L, Zhu T, Morelli D, Creagh A, Liu Z, Yang J, Liu F, Zhang YT, Clifton DA. Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review. IEEE Rev Biomed Eng 2024; 17:180-196. [PMID: 37186539 DOI: 10.1109/rbme.2023.3271595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.
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6
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Salvador M, Marsden AL. Branched Latent Neural Maps. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2024; 418:116499. [PMID: 37872974 PMCID: PMC10588816 DOI: 10.1016/j.cma.2023.116499] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 hours on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10 - 4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.
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Affiliation(s)
- Matteo Salvador
- Institute for Computational and Mathematical Engineering, Stanford University, California, USA
- Cardiovascular Institute, Stanford University, California, USA
- Pediatric Cardiology, Stanford University, California, USA
| | - Alison Lesley Marsden
- Department of Bioengineering, Stanford University, California, USA
- Institute for Computational and Mathematical Engineering, Stanford University, California, USA
- Cardiovascular Institute, Stanford University, California, USA
- Pediatric Cardiology, Stanford University, California, USA
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7
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Yan Q, Xiao D, Jia Y, Ai D, Fan J, Song H, Xu C, Wang Y, Yang J. A multi-dimensional CFD framework for fast patient-specific fractional flow reserve prediction. Comput Biol Med 2024; 168:107718. [PMID: 37988787 DOI: 10.1016/j.compbiomed.2023.107718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/01/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023]
Abstract
Fractional flow reserve (FFR) is considered as the gold standard for diagnosing coronary myocardial ischemia. Existing 3D computational fluid dynamics (CFD) methods attempt to predict FFR noninvasively using coronary computed tomography angiography (CTA). However, the accuracy and efficiency of the 3D CFD methods in coronary arteries are considerably limited. In this work, we introduce a multi-dimensional CFD framework that improves the accuracy of FFR prediction by estimating 0D patient-specific boundary conditions, and increases the efficiency by generating 3D initial conditions. The multi-dimensional CFD models contain the 3D vascular model for coronary simulation, the 1D vascular model for iterative optimization, and the 0D vascular model for boundary conditions expression. To improve the accuracy, we utilize clinical parameters to derive 0D patient-specific boundary conditions with an optimization algorithm. To improve the efficiency, we evaluate the convergence state using the 1D vascular model and obtain the convergence parameters to generate appropriate 3D initial conditions. The 0D patient-specific boundary conditions and the 3D initial conditions are used to predict FFR (FFRC). We conducted a retrospective study involving 40 patients (61 diseased vessels) with invasive FFR and their corresponding CTA images. The results demonstrate that the FFRC and the invasive FFR have a strong linear correlation (r = 0.80, p < 0.001) and high consistency (mean difference: 0.014 ±0.071). After applying the cut-off value of FFR (0.8), the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of FFRC were 88.5%, 93.3%, 83.9%, 84.8%, and 92.9%, respectively. Compared with the conventional zero initial conditions method, our method improves prediction efficiency by 71.3% per case. Therefore, our multi-dimensional CFD framework is capable of improving the accuracy and efficiency of FFR prediction significantly.
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Affiliation(s)
- Qing Yan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Deqiang Xiao
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
| | - Yaosong Jia
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Danni Ai
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Jingfan Fan
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China
| | - Hong Song
- School of Computer Science, Beijing Institute of Technology, Beijing 100081, China
| | - Cheng Xu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Yining Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
| | - Jian Yang
- School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China.
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8
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Schwarz EL, Pfaller MR, Szafron JM, Latorre M, Lindsey SE, Breuer CK, Humphrey JD, Marsden AL. A Fluid-Solid-Growth Solver for Cardiovascular Modeling. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2023; 417:116312. [PMID: 38044957 PMCID: PMC10691594 DOI: 10.1016/j.cma.2023.116312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
We implement full, three-dimensional constrained mixture theory for vascular growth and remodeling into a finite element fluid-structure interaction (FSI) solver. The resulting "fluid-solid-growth" (FSG) solver allows long term, patient-specific predictions of changing hemodynamics, vessel wall morphology, tissue composition, and material properties. This extension from short term (FSI) to long term (FSG) simulations increases clinical relevance by enabling mechanobioloigcally-dependent studies of disease progression in complex domains.
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Affiliation(s)
- Erica L Schwarz
- Department of Bioengineering, Stanford Univeristy, Stanford, CA 94306, USA
| | - Martin R Pfaller
- Department of Pediatrics - Cardiology, Stanford Univeristy, Stanford, CA 94306, USA
| | - Jason M Szafron
- Department of Pediatrics - Cardiology, Stanford Univeristy, Stanford, CA 94306, USA
| | - Marcos Latorre
- Center for Research and Innovation in Bioengineering, Universitat Politècnica de València, València 46022, Spain
| | - Stephanie E Lindsey
- Department of Pediatrics - Cardiology, Stanford Univeristy, Stanford, CA 94306, USA
| | - Christopher K Breuer
- Department of Surgery, Nationwide Children's Hospital, Columbus, OH 43210, USA
- Center for Regenerative Medicine, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH 43215, USA
| | - Jay D Humphrey
- Department of Biomedical Engineering, Yale Univeristy, New Haven, CT 06520, USA
| | - Alison L Marsden
- Department of Bioengineering, Stanford Univeristy, Stanford, CA 94306, USA
- Department of Pediatrics - Cardiology, Stanford Univeristy, Stanford, CA 94306, USA
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9
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Chiastra C, Zuin M, Rigatelli G, D’Ascenzo F, De Ferrari GM, Collet C, Chatzizisis YS, Gallo D, Morbiducci U. Computational fluid dynamics as supporting technology for coronary artery disease diagnosis and treatment: an international survey. Front Cardiovasc Med 2023; 10:1216796. [PMID: 37719972 PMCID: PMC10501454 DOI: 10.3389/fcvm.2023.1216796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 08/14/2023] [Indexed: 09/19/2023] Open
Abstract
Background Computational fluid dynamics (CFD) is emerging as an effective technology able to improve procedural outcomes and enhance clinical decision-making in patients with coronary artery disease (CAD). The present study aims to assess the state of knowledge, use and clinical acceptability of CFD in the diagnosis and treatment of CAD. Methods We realized a 20-questions international, anonymous, cross-sectional survey to cardiologists to test their knowledge and confidence on CFD as a technology applied to patients suffering from CAD. Responses were recorded between May 18, 2022, and June 12, 2022. Results A total of 466 interventional cardiologists (mean age 48.4 ± 8.3 years, males 362), from 42 different countries completed the survey, for a response rate of 45.9%. Of these, 66.6% declared to be familiar with the term CFD, especially for optimization of existing interventional techniques (16.1%) and assessment of hemodynamic quantities related with CAD (13.7%). About 30% of respondents correctly answered to the questions exploring their knowledge on the pathophysiological role of some CFD-derived quantities such as wall shear stress and helical flow in coronary arteries. Among respondents, 85.9% would consider patient-specific CFD-based analysis in daily interventional practice while 94.2% declared to be interested in receiving a brief foundation course on the basic CFD principles. Finally, 87.7% of respondents declared to be interested in a cath-lab software able to conduct affordable CFD-based analyses at the point-of-care. Conclusions Interventional cardiologists reported to be profoundly interested in adopting CFD simulations as a technology supporting decision making in the treatment of CAD in daily practice.
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Affiliation(s)
- Claudio Chiastra
- PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Marco Zuin
- Department of Translational Medicine, University of Ferrara, Ferrara, Italy
| | - Gianluca Rigatelli
- Interventional Cardiology Unit, Department of Cardiology, Madre Teresa Hospital, Padova, Italy
| | - Fabrizio D’Ascenzo
- Division of Cardiology, Department of Medical Sciences, Città Della Salute e Della Scienza Hospital, Turin, Italy
| | - Gaetano Maria De Ferrari
- Division of Cardiology, Department of Medical Sciences, Città Della Salute e Della Scienza Hospital, Turin, Italy
| | | | - Yiannis S. Chatzizisis
- Division of Cardiovascular Medicine, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Diego Gallo
- PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
| | - Umberto Morbiducci
- PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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10
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Menon K, Khan MO, Sexton ZA, Richter J, Nieman K, Marsden AL. Personalized coronary and myocardial blood flow models incorporating CT perfusion imaging and synthetic vascular trees. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.17.23294242. [PMID: 37645850 PMCID: PMC10462196 DOI: 10.1101/2023.08.17.23294242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Computational simulations of coronary artery blood flow, using anatomical models based on clinical imaging, are an emerging non-invasive tool for personalized treatment planning. However, current simulations contend with two related challenges - incomplete anatomies in image-based models due to the exclusion of arteries smaller than the imaging resolution, and the lack of personalized flow distributions informed by patient-specific imaging. We introduce a data-enabled, personalized and multi-scale flow simulation framework spanning large coronary arteries to myocardial microvasculature. It includes image-based coronary models combined with synthetic vasculature for arteries below the imaging resolution, myocardial blood flow simulated using Darcy models, and systemic circulation represented as lumped-parameter networks. Personalized flow distributions and model parameters are informed by clinical CT myocardial perfusion imaging and cardiac function using surrogate-based optimization. We reveal substantial differences in flow distributions and clinical diagnosis metrics between the proposed personalized framework and empirical methods based on anatomy; these errors cannot be predicted a priori. This suggests virtual treatment planning tools would benefit from increased personalization informed by emerging imaging methods.
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Affiliation(s)
- Karthik Menon
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Muhammed Owais Khan
- Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, Ontario, Canada
| | - Zachary A Sexton
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Jakob Richter
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA, USA
| | - Koen Nieman
- Departments of Radiology and Medicine (Cardiovascular Medicine), Stanford School of Medicine, Stanford, CA, USA
| | - Alison L Marsden
- Department of Pediatrics (Cardiology), Stanford School of Medicine, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
- Department of Bioengineering, Stanford University, Stanford, CA, USA
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11
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Uncertainty Quantification in the In Vivo Image-Based Estimation of Local Elastic Properties of Vascular Walls. J Cardiovasc Dev Dis 2023; 10:jcdd10030109. [PMID: 36975873 PMCID: PMC10058982 DOI: 10.3390/jcdd10030109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/15/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023] Open
Abstract
Introduction: Patient-specific computational models are a powerful tool for planning cardiovascular interventions. However, the in vivo patient-specific mechanical properties of vessels represent a major source of uncertainty. In this study, we investigated the effect of uncertainty in the elastic module (E) on a Fluid–Structure Interaction (FSI) model of a patient-specific aorta. Methods: The image-based χ-method was used to compute the initial E value of the vascular wall. The uncertainty quantification was carried out using the generalized Polynomial Chaos (gPC) expansion technique. The stochastic analysis was based on four deterministic simulations considering four quadrature points. A deviation of about ±20% on the estimation of the E value was assumed. Results: The influence of the uncertain E parameter was evaluated along the cardiac cycle on area and flow variations extracted from five cross-sections of the aortic FSI model. Results of stochastic analysis showed the impact of E in the ascending aorta while an insignificant effect was observed in the descending tract. Conclusions: This study demonstrated the importance of the image-based methodology for inferring E, highlighting the feasibility of retrieving useful additional data and enhancing the reliability of in silico models in clinical practice.
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12
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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.
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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
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13
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Du P, Wang JX. Reducing Geometric Uncertainty in Computational Hemodynamics by Deep Learning-Assisted Parallel-Chain MCMC. J Biomech Eng 2022; 144:121009. [PMID: 36166284 PMCID: PMC9632478 DOI: 10.1115/1.4055809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 09/21/2022] [Indexed: 11/08/2022]
Abstract
Computational hemodynamic modeling has been widely used in cardiovascular research and healthcare. However, the reliability of model predictions is largely dependent on the uncertainties of modeling parameters and boundary conditions, which should be carefully quantified and further reduced with available measurements. In this work, we focus on propagating and reducing the uncertainty of vascular geometries within a Bayesian framework. A novel deep learning (DL)-assisted parallel Markov chain Monte Carlo (MCMC) method is presented to enable efficient Bayesian posterior sampling and geometric uncertainty reduction. A DL model is built to approximate the geometry-to-hemodynamic map, which is trained actively using online data collected from parallel MCMC chains and utilized for early rejection of unlikely proposals to facilitate convergence with less expensive full-order model evaluations. Numerical studies on two-dimensional aortic flows are conducted to demonstrate the effectiveness and merit of the proposed method.
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Affiliation(s)
- Pan Du
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
| | - Jian-Xun Wang
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556
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14
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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: 2] [Impact Index Per Article: 1.0] [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.
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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
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15
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Anbazhakan S, Rios Coronado PE, Sy-Quia ANL, Seow LW, Hands AM, Zhao M, Dong ML, Pfaller MR, Amir ZA, Raftrey BC, Cook CK, D’Amato G, Fan X, Williams IM, Jha SK, Bernstein D, Nieman K, Pașca AM, Marsden AL, Horse KR. Blood flow modeling reveals improved collateral artery performance during the regenerative period in mammalian hearts. NATURE CARDIOVASCULAR RESEARCH 2022; 1:775-790. [PMID: 37305211 PMCID: PMC10256232 DOI: 10.1038/s44161-022-00114-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 07/07/2022] [Indexed: 06/13/2023]
Abstract
Collateral arteries bridge opposing artery branches, forming a natural bypass that can deliver blood flow downstream of an occlusion. Inducing coronary collateral arteries could treat cardiac ischemia, but more knowledge on their developmental mechanisms and functional capabilities is required. Here we used whole-organ imaging and three-dimensional computational fluid dynamics modeling to define spatial architecture and predict blood flow through collaterals in neonate and adult mouse hearts. Neonate collaterals were more numerous, larger in diameter and more effective at restoring blood flow. Decreased blood flow restoration in adults arose because during postnatal growth coronary arteries expanded by adding branches rather than increasing diameters, altering pressure distributions. In humans, adult hearts with total coronary occlusions averaged 2 large collaterals, with predicted moderate function, while normal fetal hearts showed over 40 collaterals, likely too small to be functionally relevant. Thus, we quantify the functional impact of collateral arteries during heart regeneration and repair-a critical step toward realizing their therapeutic potential.
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Affiliation(s)
- Suhaas Anbazhakan
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- These authors contributed equally
| | - Pamela E. Rios Coronado
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- These authors contributed equally
| | | | - Lek Wei Seow
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Aubrey M. Hands
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Mingming Zhao
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Melody L. Dong
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Martin R. Pfaller
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305
| | - Zhainib A. Amir
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Brian C. Raftrey
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | | | - Gaetano D’Amato
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Xiaochen Fan
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Ian M. Williams
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Sawan K. Jha
- Department of Biology, Stanford University, Stanford, CA 94305, USA
| | - Daniel Bernstein
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Koen Nieman
- Departments of Cardiovascular Medicine and Radiology, School of Medicine, Stanford University, Stanford, CA, 94305, USA
| | - Anca M. Pașca
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305
| | - Alison L. Marsden
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kristy Red Horse
- Department of Biology, Stanford University, Stanford, CA 94305, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford, CA, 94305, USA
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16
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Uncertainty Quantification for Numerical Solutions of the Nonlinear Partial Differential Equations by Using the Multi-Fidelity Monte Carlo Method. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12147045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The Monte Carlo simulation is a popular statistical method to estimate the effect of uncertainties on the solutions of nonlinear partial differential equations, but it requires a huge computational cost of the deterministic model, and the convergence may become slow. For this reason, we developed the multi-fidelity Monte Carlo (MFMC) methods based on data-driven low-fidelity models for uncertainty analysis of nonlinear partial differential equations. Firstly, the nonlinear partial differential equations are transformed into ordinary differential equations (ODEs) by using finite difference discretization or Fourier transformation. Then, the reduced dimension model and discrete empirical interpolation method (DEIM) are coupled to construct effective nonlinear low-fidelity models in ODEs system. Finally, the MFMC method is used to combine the output information of the high-fidelity model and the low-fidelity models to give the optimal estimation of the statistics. Experimental results of the nonlinear Schrodinger equation and the Burgers’ equation show that, compared with the standard Monte Carlo method, the MFMC method based on the data-driven low-fidelity model in this paper can improve the calculation efficiency significantly.
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17
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Mirramezani M, Shadden SC. Distributed lumped parameter modeling of blood flow in compliant vessels. J Biomech 2022; 140:111161. [DOI: 10.1016/j.jbiomech.2022.111161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 05/05/2022] [Accepted: 05/23/2022] [Indexed: 10/18/2022]
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18
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Latorre M, Szafron JM, Ramachandra AB, Humphrey JD. In vivo development of tissue engineered vascular grafts: a fluid-solid-growth model. Biomech Model Mechanobiol 2022; 21:827-848. [PMID: 35179675 PMCID: PMC9133046 DOI: 10.1007/s10237-022-01562-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 01/24/2022] [Indexed: 11/02/2022]
Abstract
Methods of tissue engineering continue to advance, and multiple clinical trials are underway evaluating tissue engineered vascular grafts (TEVGs). Whereas initial concerns focused on suture retention and burst pressure, there is now a pressing need to design grafts to have optimal performance, including an ability to grow and remodel in response to changing hemodynamic loads. Toward this end, there is similarly a need for computational methods that can describe and predict the evolution of TEVG geometry, composition, and material properties while accounting for changes in hemodynamics. Although the ultimate goal is a fluid-solid-growth (FSG) model incorporating fully 3D growth and remodeling and 3D hemodynamics, lower fidelity models having high computational efficiency promise to play important roles, especially in the design of candidate grafts. We introduce here an efficient FSG model of in vivo development of a TEVG based on two simplifying concepts: mechanobiologically equilibrated growth and remodeling of the graft and an embedded control volume analysis of the hemodynamics. Illustrative simulations for a model Fontan conduit reveal the utility of this approach, which promises to be particularly useful in initial design considerations involving formal methods of optimization which otherwise add considerably to the computational expense.
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Affiliation(s)
- Marcos Latorre
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA.
- Center for Research and Innovation in Bioengineering, Universitat Politècnica de València, València, 46022, Spain.
| | - Jason M Szafron
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, CA, 94305, USA
| | - Abhay B Ramachandra
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Jay D Humphrey
- Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, 06520, USA
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19
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Esmailie F, Razavi A, Yeats B, Sivakumar SK, Chen H, Samaee M, Shah IA, Veneziani A, Yadav P, Thourani VH, Dasi LP. Biomechanics of Transcatheter Aortic Valve Replacement Complications and Computational Predictive Modeling. STRUCTURAL HEART : THE JOURNAL OF THE HEART TEAM 2022; 6:100032. [PMID: 37273734 PMCID: PMC10236878 DOI: 10.1016/j.shj.2022.100032] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/09/2021] [Accepted: 11/03/2021] [Indexed: 06/06/2023]
Abstract
Transcatheter aortic valve replacement (TAVR) is a rapidly growing field enabling replacement of diseased aortic valves without the need for open heart surgery. However, due to the nature of the procedure and nonremoval of the diseased tissue, there are rates of complications ranging from tissue rupture and coronary obstruction to paravalvular leak, valve thrombosis, and permanent pacemaker implantation. In recent years, computational modeling has shown a great deal of promise in its capabilities to understand the biomechanical implications of TAVR as well as help preoperatively predict risks inherent to device-patient-specific anatomy biomechanical interaction. This includes intricate replication of stent and leaflet designs and tested and validated simulated deployments with structural and fluid mechanical simulations. This review outlines current biomechanical understanding of device-related complications from TAVR and related predictive strategies using computational modeling. An outlook on future modeling strategies highlighting reduced order modeling which could significantly reduce the high time and cost that are required for computational prediction of TAVR outcomes is presented in this review paper. A summary of current commercial/in-development software is presented in the final section.
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Affiliation(s)
- Fateme Esmailie
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Atefeh Razavi
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Breandan Yeats
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Sri Krishna Sivakumar
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Huang Chen
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Milad Samaee
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Imran A. Shah
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
| | - Alessandro Veneziani
- Department of Mathematics, Department of Computer Science, Emory University, Atlanta, Georgia, USA
| | - Pradeep Yadav
- Department of Cardiology, Marcus Valve Center, Piedmont Heart Institute, Atlanta, Georgia, USA
| | - Vinod H. Thourani
- Department of Cardiovascular Surgery, Marcus Valve Center, Piedmont Heart Institute, Atlanta, Georgia, USA
| | - Lakshmi Prasad Dasi
- The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology/Emory University School of Medicine, Atlanta, Georgia, USA
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20
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Lodi Rizzini M, Candreva A, Chiastra C, Gallinoro E, Calò K, D'Ascenzo F, De Bruyne B, Mizukami T, Collet C, Gallo D, Morbiducci U. Modelling coronary flows: impact of differently measured inflow boundary conditions on vessel-specific computational hemodynamic profiles. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 221:106882. [PMID: 35597205 DOI: 10.1016/j.cmpb.2022.106882] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/27/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES The translation of hemodynamic quantities based on wall shear stress (WSS) or intravascular helical flow into clinical biomarkers of coronary atherosclerotic disease is still hampered by the assumptions/idealizations required by the computational fluid dynamics (CFD) simulations of the coronary hemodynamics. In the resulting budget of uncertainty, inflow boundary conditions (BCs) play a primary role. Accordingly, in this study we investigated the impact of the approach adopted for in vivo coronary artery blood flow rate assessment on personalized CFD simulations where blood flow rate is used as inflow BC. METHODS CFD simulations were carried out on coronary angiograms by applying personalized inflow BCs derived from four different techniques assessing in vivo surrogates of flow rate: continuous thermodilution, intravascular Doppler, frame count-based 3D contrast velocity, and diameter-based scaling law. The impact of inflow BCs on coronary hemodynamics was evaluated in terms of WSS- and helicity-based quantities. RESULTS As main findings, we report that: (i) coronary flow rate values may differ based on the applied flow derivation technique, as continuous thermodilution provided higher flow rate values than intravascular Doppler and diameter-based scaling law (p = 0.0014 and p = 0.0023, respectively); (ii) such intrasubject differences in flow rate values lead to different surface-averaged values of WSS magnitude and helical blood flow intensity (p<0.0020); (iii) luminal surface areas exposed to low WSS and helical flow topological features showed robustness to the flow rate values. CONCLUSIONS Although the absence of a clinically applicable gold standard approach prevents a general recommendation for one coronary blood flow rate derivation technique, our findings indicate that the inflow BC may impact computational hemodynamic results, suggesting that a standardization would be desirable to provide comparable results among personalized CFD simulations of the coronary hemodynamics.
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Affiliation(s)
- Maurizio Lodi Rizzini
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Alessandro Candreva
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy; Department of Cardiology, Zurich University Hospital, Zurich, Switzerland
| | - Claudio Chiastra
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | | | - Karol Calò
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
| | - Fabrizio D'Ascenzo
- Hemodynamic Laboratory, Department of Medical Sciences, University of Turin, Turin, Italy
| | | | | | - Carlos Collet
- Cardiovascular Center Aalst, OLV-Clinic, Aalst, Belgium
| | - Diego Gallo
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy.
| | - Umberto Morbiducci
- Polito(BIO)Med Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Torino, Italy
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21
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Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond. Ann Biomed Eng 2022; 50:615-627. [PMID: 35445297 DOI: 10.1007/s10439-022-02967-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/07/2022] [Indexed: 12/13/2022]
Abstract
Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown significant promising results, ML for the prediction of biomechanics such as blood flow or tissue dynamics is in its infancy. This perspective article discusses some of the challenges in using ML for replacing well-established physics-based models in cardiovascular biomechanics. Specifically, we discuss the large landscape of input features in 3D patient-specific modeling as well as the high-dimensional output space of field variables that vary in space and time. We argue that the end purpose of such ML models needs to be clearly defined and the tradeoff between the loss in accuracy and the gained speedup carefully interpreted in the context of translational modeling. We also discuss several exciting venues where ML could be strategically used to augment traditional physics-based modeling in cardiovascular biomechanics. In these applications, ML is not replacing physics-based modeling, but providing opportunities to solve ill-defined problems, improve measurement data quality, enable a solution to computationally expensive problems, and interpret complex spatiotemporal data by extracting hidden patterns. In summary, we suggest a strategic integration of ML in cardiovascular biomechanics modeling where the ML model is not the end goal but rather a tool to facilitate enhanced modeling.
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22
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Lan IS, Yang W, Feinstein JA, Kreutzer J, Collins RT, Ma M, Adamson GT, Marsden AL. Virtual Transcatheter Interventions for Peripheral Pulmonary Artery Stenosis in Williams and Alagille Syndromes. J Am Heart Assoc 2022; 11:e023532. [PMID: 35253446 PMCID: PMC9075299 DOI: 10.1161/jaha.121.023532] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background
Despite favorable outcomes of surgical pulmonary artery (PA) reconstruction, isolated proximal stenting of the central PAs is common clinical practice for patients with peripheral PA stenosis in association with Williams and Alagille syndromes. Given the technical challenges of PA reconstruction and the morbidities associated with transcatheter interventions, the hemodynamic consequences of all treatment strategies must be rigorously assessed. Our study aims to model, assess, and predict hemodynamic outcomes of transcatheter interventions in these patients.
Methods and Results
Isolated proximal and “extensive” interventions (stenting and/or balloon angioplasty of proximal and lobar vessels) were performed in silico on 6 patient‐specific PA models. Autoregulatory adaptation of the cardiac output and downstream arterial resistance was modeled in response to intervention‐induced hemodynamic perturbations. Postintervention computational fluid dynamics predictions were validated in 2 stented patients and quantitatively assessed in 4 surgical patients. Our computational methods accurately predicted postinterventional PA pressures, the primary indicators of success for treatment of peripheral PA stenosis. Proximal and extensive treatment achieved median reductions of 14% and 40% in main PA systolic pressure, 27% and 56% in pulmonary vascular resistance, and 10% and 45% in right ventricular stroke work, respectively.
Conclusions
In patients with Williams and Alagille syndromes, extensive transcatheter intervention is required to sufficiently reduce PA pressures and right ventricular stroke work. Transcatheter therapy was shown to be ineffective for long‐segment stenosis and pales hemodynamically in comparison with published outcomes of surgical reconstruction. Regardless of the chosen strategy, a virtual treatment planning platform could identify lesions most critical for optimizing right ventricular afterload.
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Affiliation(s)
- Ingrid S. Lan
- Department of Bioengineering Stanford University Stanford CA
| | - Weiguang Yang
- Department of Pediatrics (Cardiology) Stanford University Stanford CA
| | - Jeffrey A. Feinstein
- Department of Bioengineering Stanford University Stanford CA
- Department of Pediatrics (Cardiology) Stanford University Stanford CA
| | - Jacqueline Kreutzer
- Department of Pediatrics (Cardiology) University of Pittsburgh Pittsburgh PA
| | - R. Thomas Collins
- Department of Pediatrics (Cardiology) Stanford University Stanford CA
- Department of Medicine (Cardiovascular Medicine) Stanford University Stanford CA
| | - Michael Ma
- Department of Cardiothoracic Surgery Stanford University Stanford CA
| | | | - Alison L. Marsden
- Department of Bioengineering Stanford University Stanford CA
- Department of Pediatrics (Cardiology) Stanford University Stanford CA
- Institute for Computational and Mathematical Engineering Stanford University Stanford CA
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23
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Characterization of the Ejector Pump Performance for the Assisted Bidirectional Glenn Procedure. FLUIDS 2022. [DOI: 10.3390/fluids7010031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
This study introduces an algebraic model informed by computational fluid dynamics (CFD) simulations to investigate the performance of the assisted bidirectional Glenn (ABG) operation on a broad range of conditions. The performance of this operation, as measured by the superior vena cava (SVC) pressure, depends on the nozzle area in its ejector pump and the patient’s pulmonary vascular resistance (PVR). Using the developed algebraic model to explore this two-dimensional parameter space shows that the ejector pump can create a pressure difference between the pulmonary artery and the SVC as high as 5 mmHg. The lowest SVC pressure is produced at a nozzle area that decreases linearly with the PVR such that, at PVR =4.2 (Wood units-m2), there is no added benefit in utilizing the ejector pump effect (optimal nozzle area is zero, corresponding to the bidirectional Glenn circulation). At PVR =2 (Wood units-m2), the SVC pressure can be lowered to less than 4 mmHg by using an optimal nozzle area of ≈2.5 mm2. Regardless of the PVR, adding a 2 mm2 nozzle to the baseline bidirectional Glenn boosts the oxygen saturation and delivery by at least 15%. The SVC pressure for that 2 mm2 nozzle remains below 14 mmHg for all PVRs less than 7 Wood units-m2. The mechanical efficiency of the optimal designs consistently remains below 30%, indicating the potential for improvement in the future. A good agreement is observed between the algebraic model and high-fidelity CFD simulations.
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24
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A Mechanistic Lumped Parameter Model of the Berlin Heart EXCOR to Analyze Device Performance and Physiologic Interactions. Cardiovasc Eng Technol 2022; 13:603-623. [PMID: 34997556 DOI: 10.1007/s13239-021-00603-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 12/09/2021] [Indexed: 11/02/2022]
Abstract
PURPOSE The Berlin Heart EXCOR (BH) is the only FDA-approved, extracorporeal pulsatile ventricular assist device (VAD) for infants and children with heart failure. Clinicians control four settings on the device-systolic and diastolic drive pressures, device pump rate, and systolic time as a percentage of the pump cycle. However, interactions between BH pneumatics and the native circulation remain poorly understood. Thus, establishing appropriate device size and settings can be challenging on a patient-to-patient basis. METHODS In this study we develop a novel lumped parameter network based on simplified device mechanics. We perform parametric studies to characterize device behavior, study interactions between the left ventricle (LV) and BH across different device settings, and develop patient-specific simulations. We then simulate the impact of changing device parameters for each of three patients. RESULTS Increasing systolic pressure and systolic time increased device output. We identified previously unobserved cycle-to-cycle variations in LV-BH interactions that may impact patient health. Patient-specific simulations demonstrated the model's ability to replicate BH performance, captured trends in LV behavior after device implantation, and emphasized the importance of device rate and volume in optimizing BH efficiency. CONCLUSION We present a novel, mechanistic model that can be readily adjusted to study a wide range of device settings and clinical scenarios. Physiologic interactions between the BH and the native LV produced large variability in cardiac loading. Our findings showed that operating the BH at a device rate greater than the patient's native heart decreases variability in physiological interactions between the BH and LV, increasing cardiac offloading while maintaining cardiac output. Device rates that are close to the resting heart rate may result in unfavorable cardiac loading conditions. Our work demonstrates the utility of our model to investigate BH performance for patient-specific physiologies.
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Pfaller MR, Pham J, Wilson NM, Parker DW, Marsden AL. On the Periodicity of Cardiovascular Fluid Dynamics Simulations. Ann Biomed Eng 2021; 49:3574-3592. [PMID: 34169398 PMCID: PMC9831274 DOI: 10.1007/s10439-021-02796-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 05/12/2021] [Indexed: 01/12/2023]
Abstract
Three-dimensional cardiovascular fluid dynamics simulations typically require computation of several cardiac cycles before they reach a periodic solution, rendering them computationally expensive. Furthermore, there is currently no standardized method to determine whether a simulation has yet reached that periodic state. In this work, we propose the use of an asymptotic error measurement to quantify the difference between simulation results and their ideal periodic state using open-loop lumped-parameter modeling. We further show that initial conditions are crucial in reducing computational time and develop an automated framework to generate appropriate initial conditions from a one-dimensional model of blood flow. We demonstrate the performance of our initialization method using six patient-specific models from the Vascular Model Repository. In our examples, our initialization protocol achieves periodic convergence within one or two cardiac cycles, leading to a significant reduction in computational cost compared to standard methods. All computational tools used in this work are implemented in the open-source software platform SimVascular. Automatically generated initial conditions have the potential to significantly reduce computation time in cardiovascular fluid dynamics simulations.
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Affiliation(s)
- Martin R Pfaller
- Department of Pediatrics (Cardiology), Institute for Computational and Mathematical Engineering, Bioengineering, Stanford University, Stanford, USA.
| | - Jonathan Pham
- Department of Mechanical Engineering, Stanford University, Stanford, USA
| | | | - David W Parker
- Stanford Research Computing, Stanford University, Stanford, USA
| | - Alison L Marsden
- Department of Pediatrics (Cardiology), Institute for Computational and Mathematical Engineering, Bioengineering, Stanford University, Stanford, USA
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Maher GD, Fleeter CM, Schiavazzi DE, Marsden AL. Geometric Uncertainty in Patient-Specific Cardiovascular Modeling with Convolutional Dropout Networks. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 386:114038. [PMID: 34737480 PMCID: PMC8562598 DOI: 10.1016/j.cma.2021.114038] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
We propose a novel approach to generate samples from the conditional distribution of patient-specific cardiovascular models given a clinically aquired image volume. A convolutional neural network architecture with dropout layers is first trained for vessel lumen segmentation using a regression approach, to enable Bayesian estimation of vessel lumen surfaces. This network is then integrated into a path-planning patient-specific modeling pipeline to generate families of cardiovascular models. We demonstrate our approach by quantifying the effect of geometric uncertainty on the hemodynamics for three patient-specific anatomies, an aorto-iliac bifurcation, an abdominal aortic aneurysm and a sub-model of the left coronary arteries. A key innovation introduced in the proposed approach is the ability to learn geometric uncertainty directly from training data. The results show how geometric uncertainty produces coefficients of variation comparable to or larger than other sources of uncertainty for wall shear stress and velocity magnitude, but has limited impact on pressure. Specifically, this is true for anatomies characterized by small vessel sizes, and for local vessel lesions seen infrequently during network training.
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Affiliation(s)
- Gabriel D. Maher
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Casey M. Fleeter
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Daniele E. Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Alison L. Marsden
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, CA, USA
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Corti A, Colombo M, Migliavacca F, Rodriguez Matas JF, Casarin S, Chiastra C. Multiscale Computational Modeling of Vascular Adaptation: A Systems Biology Approach Using Agent-Based Models. Front Bioeng Biotechnol 2021; 9:744560. [PMID: 34796166 PMCID: PMC8593007 DOI: 10.3389/fbioe.2021.744560] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/04/2021] [Indexed: 12/20/2022] Open
Abstract
The widespread incidence of cardiovascular diseases and associated mortality and morbidity, along with the advent of powerful computational resources, have fostered an extensive research in computational modeling of vascular pathophysiology field and promoted in-silico models as a support for biomedical research. Given the multiscale nature of biological systems, the integration of phenomena at different spatial and temporal scales has emerged to be essential in capturing mechanobiological mechanisms underlying vascular adaptation processes. In this regard, agent-based models have demonstrated to successfully embed the systems biology principles and capture the emergent behavior of cellular systems under different pathophysiological conditions. Furthermore, through their modular structure, agent-based models are suitable to be integrated with continuum-based models within a multiscale framework that can link the molecular pathways to the cell and tissue levels. This can allow improving existing therapies and/or developing new therapeutic strategies. The present review examines the multiscale computational frameworks of vascular adaptation with an emphasis on the integration of agent-based approaches with continuum models to describe vascular pathophysiology in a systems biology perspective. The state-of-the-art highlights the current gaps and limitations in the field, thus shedding light on new areas to be explored that may become the future research focus. The inclusion of molecular intracellular pathways (e.g., genomics or proteomics) within the multiscale agent-based modeling frameworks will certainly provide a great contribution to the promising personalized medicine. Efforts will be also needed to address the challenges encountered for the verification, uncertainty quantification, calibration and validation of these multiscale frameworks.
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Affiliation(s)
- Anna Corti
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Monika Colombo
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,Institute for Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Switzerland
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Jose Felix Rodriguez Matas
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy
| | - Stefano Casarin
- Department of Surgery, Houston Methodist Hospital, Houston, TX, United States.,Center for Computational Surgery, Houston Methodist Research Institute, Houston, TX, United States.,Houston Methodist Academic Institute, Houston, TX, United States
| | - Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Milan, Italy.,PoliToMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin, Italy
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Grande Gutiérrez N, Sinno T, Diamond SL. A 1D-3D Hybrid Model of Patient-Specific Coronary Hemodynamics. Cardiovasc Eng Technol 2021; 13:331-342. [PMID: 34591275 DOI: 10.1007/s13239-021-00580-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 09/07/2021] [Indexed: 11/29/2022]
Abstract
PURPOSE Coronary flow is affected by evolving events such as atherosclerotic plaque formation, rupture, and thrombosis, resulting in myocardial ischemia and infarction. Highly resolved 3D hemodynamic data at the stenosis is essential to model shear-sensitive thrombotic events in coronary artery disease. METHODS We developed a hybrid 1D-3D simulation framework to compute patient-specific coronary hemodynamics efficiently. A 1D model of the coronary flow is coupled to an image-based 3D model of the region of interest. This framework affords the advantages of reduced-order modeling, decreasing the global computational cost, without sacrificing the accuracy of the quantities of interest. RESULTS We validated our 1D-3D model against full 3D coronary simulations in healthy and diseased conditions. Our results showed good agreement between the 3D and the 1D-3D models while reducing the computational cost by 40-fold compared to the 3D simulation. The 1D-3D model predicted left/right coronary flow distribution within 3% and provided an accurate estimation of fractional flow reserve and wall shear stress distribution at the stenosis comparable to the 3D simulation. CONCLUSION Savings in computational cost may be significant in situations with changing geometry, such as growing thrombosis. Also, this approach would allow quantifying the time-dependent effect of thrombotic growth and occlusion on the global coronary circulation.
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Affiliation(s)
- Noelia Grande Gutiérrez
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, USA
| | - Talid Sinno
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, USA
| | - Scott L Diamond
- Department of Chemical and Biomolecular Engineering, University of Pennsylvania, Philadelphia, USA. .,Department of Bioengineering, University of Pennsylvania, Philadelphia, USA.
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Antonuccio MN, Mariotti A, Fanni BM, Capellini K, Capelli C, Sauvage E, Celi S. Effects of Uncertainty of Outlet Boundary Conditions in a Patient-Specific Case of Aortic Coarctation. Ann Biomed Eng 2021; 49:3494-3507. [PMID: 34431017 PMCID: PMC8671284 DOI: 10.1007/s10439-021-02841-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/20/2021] [Indexed: 12/22/2022]
Abstract
Computational Fluid Dynamics (CFD) simulations of blood flow are widely used to compute a variety of hemodynamic indicators such as velocity, time-varying wall shear stress, pressure drop, and energy losses. One of the major advances of this approach is that it is non-invasive. The accuracy of the cardiovascular simulations depends directly on the level of certainty on input parameters due to the modelling assumptions or computational settings. Physiologically suitable boundary conditions at the inlet and outlet of the computational domain are needed to perform a patient-specific CFD analysis. These conditions are often affected by uncertainties, whose impact can be quantified through a stochastic approach. A methodology based on a full propagation of the uncertainty from clinical data to model results is proposed here. It was possible to estimate the confidence associated with model predictions, differently than by deterministic simulations. We evaluated the effect of using three-element Windkessel models as the outflow boundary conditions of a patient-specific aortic coarctation model. A parameter was introduced to calibrate the resistances of the Windkessel model at the outlets. The generalized Polynomial Chaos method was adopted to perform the stochastic analysis, starting from a few deterministic simulations. Our results show that the uncertainty of the input parameter gave a remarkable variability on the volume flow rate waveform at the systolic peak simulating the conditions before the treatment. The same uncertain parameter had a slighter effect on other quantities of interest, such as the pressure gradient. Furthermore, the results highlight that the fine-tuning of Windkessel resistances is not necessary to simulate the post-stenting scenario.
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Affiliation(s)
- Maria Nicole Antonuccio
- BioCardioLab, Bioengineering Unit - Heart Hospital, Fondazione Toscana "G. Monasterio", Massa, Italy
| | - Alessandro Mariotti
- Civil and Industrial Engineering Department, University of Pisa, Pisa, Italy
| | - Benigno Marco Fanni
- BioCardioLab, Bioengineering Unit - Heart Hospital, Fondazione Toscana "G. Monasterio", Massa, Italy
- Information Engineering Department, University of Pisa, Pisa, Italy
| | - Katia Capellini
- BioCardioLab, Bioengineering Unit - Heart Hospital, Fondazione Toscana "G. Monasterio", Massa, Italy
- Information Engineering Department, University of Pisa, Pisa, Italy
| | - Claudio Capelli
- Institute of Cardiovascular Science, University College of London, London, UK
| | - Emilie Sauvage
- Institute of Cardiovascular Science, University College of London, London, UK
| | - Simona Celi
- BioCardioLab, Bioengineering Unit - Heart Hospital, Fondazione Toscana "G. Monasterio", Massa, Italy.
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Ninos G, Bartzis V, Merlemis N, Sarris IE. Uncertainty quantification implementations in human hemodynamic flows. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106021. [PMID: 33721602 DOI: 10.1016/j.cmpb.2021.106021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 02/19/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Human hemodynamic modeling is usually influenced by uncertainties occurring from a considerable unavailability of information linked to the boundary conditions and the physical properties used in the numerical models. Calculating the effect of these uncertainties on the numerical findings along the cardiovascular system is a demanding process due to the complexity of the morphology of the body and the area dynamics. To cope with all these difficulties, Uncertainty Quantification (UQ) methods seem to be an ideal tool. RESULTS This study focuses on analyzing and summarizing some of the recent research efforts and directions of implementing UQ in human hemodynamic flows by analyzing 139 research papers. Initially, the suitability of applying this approach is analyzed and demonstrated. Then, an overview of the most significant research work in various fields of biomedical hemodynamic engineering is presented. Finally, it is attempted to identify any possible forthcoming directions for research and methodological progress of UQ in biomedical sciences. CONCLUSION This review concludes that by finding the best statistical methods and parameters to represent the propagated uncertainties, while achieving a good interpretation of the interaction between input-output, is crucial for implementing UQ in biomedical sciences.
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Affiliation(s)
- G Ninos
- Department of Biomedical Sciences, University of West Attica, 12243, Athens, Greece; Department of Mechanical Engineering, University of West Attica, 12244, Athens, Greece.
| | - V Bartzis
- Department of Food Science & Technology, University of West Attica, 12243, Athens, Greece
| | - N Merlemis
- Deptartment of Surveying and Geoinformatics Engineering, University of West Attica, 12243 Athens, Greece
| | - I E Sarris
- Department of Mechanical Engineering, University of West Attica, 12244, Athens, Greece
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Prud'homme C, Sala L, Szopos M. Uncertainty propagation and sensitivity analysis: results from the Ocular Mathematical Virtual Simulator. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:2010-2032. [PMID: 33892535 DOI: 10.3934/mbe.2021105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
We propose an uncertainty propagation study and a sensitivity analysis with the Ocular Mathematical Virtual Simulator, a computational and mathematical model that predicts the hemodynamics and biomechanics within the human eye. In this contribution, we focus on the effect of intraocular pressure, retrolaminar tissue pressure and systemic blood pressure on the ocular posterior tissue vasculature. The combination of a physically-based model with experiments-based stochastic input allows us to gain a better understanding of the physiological system, accounting both for the driving mechanisms and the data variability.
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Affiliation(s)
| | - Lorenzo Sala
- Centre de recherche INRIA de Paris, Paris 75012, France
| | - Marcela Szopos
- MAP5 UMR CNRS 8145, Université de Paris, Paris 75006, France
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Vardhan M, Randles A. Application of physics-based flow models in cardiovascular medicine: Current practices and challenges. BIOPHYSICS REVIEWS 2021; 2:011302. [PMID: 38505399 PMCID: PMC10903374 DOI: 10.1063/5.0040315] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Accepted: 02/18/2021] [Indexed: 03/21/2024]
Abstract
Personalized physics-based flow models are becoming increasingly important in cardiovascular medicine. They are a powerful complement to traditional methods of clinical decision-making and offer a wealth of physiological information beyond conventional anatomic viewing using medical imaging data. These models have been used to identify key hemodynamic biomarkers, such as pressure gradient and wall shear stress, which are associated with determining the functional severity of cardiovascular diseases. Importantly, simulation-driven diagnostics can help researchers understand the complex interplay between geometric and fluid dynamic parameters, which can ultimately improve patient outcomes and treatment planning. The possibility to compute and predict diagnostic variables and hemodynamics biomarkers can therefore play a pivotal role in reducing adverse treatment outcomes and accelerate development of novel strategies for cardiovascular disease management.
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Affiliation(s)
- M. Vardhan
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
| | - A. Randles
- Department of Biomedical Engineering, Duke University, Durham, North Carolina 27708, USA
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Seo J, Schiavazzi DE, Kahn AM, Marsden AL. The effects of clinically-derived parametric data uncertainty in patient-specific coronary simulations with deformable walls. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2020; 36:e3351. [PMID: 32419369 PMCID: PMC8211426 DOI: 10.1002/cnm.3351] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2019] [Revised: 02/20/2020] [Accepted: 05/09/2020] [Indexed: 05/31/2023]
Abstract
Cardiovascular simulations are increasingly used for noninvasive diagnosis of cardiovascular disease, to guide treatment decisions, and in the design of medical devices. Quantitative assessment of the variability of simulation outputs due to input uncertainty is a key step toward further integration of cardiovascular simulations in the clinical workflow. In this study, we present uncertainty quantification in computational models of the coronary circulation to investigate the effect of uncertain parameters, including coronary pressure waveform, intramyocardial pressure, morphometry exponent, and the vascular wall Young's modulus. We employ a left coronary artery model with deformable vessel walls, simulated via an Arbitrary-Lagrangian-Eulerian framework for fluid-structure interaction, with a prescribed inlet pressure and open-loop lumped parameter network outlet boundary conditions. Stochastic modeling of the uncertain inputs is determined from intra-coronary catheterization data or gathered from the literature. Uncertainty propagation is performed using several approaches including Monte Carlo, Quasi Monte Carlo sampling, stochastic collocation, and multi-wavelet stochastic expansion. Variabilities in the quantities of interest, including branch pressure, flow, wall shear stress, and wall deformation are assessed. We find that uncertainty in inlet pressures and intramyocardial pressures significantly affect all resulting QoIs, while uncertainty in elastic modulus only affects the mechanical response of the vascular wall. Variability in the morphometry exponent used to distribute the total downstream vascular resistance to the single outlets, has little effect on coronary hemodynamics or wall mechanics. Finally, we compare convergence behaviors of statistics of QoIs using several uncertainty propagation methods on three model benchmark problems and the left coronary simulations. From the simulation results, we conclude that the multi-wavelet stochastic expansion shows superior accuracy and performance against Quasi Monte Carlo and stochastic collocation methods.
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Affiliation(s)
- Jongmin Seo
- Department of Pediatrics (Cardiology), Bioengineering and ICME, Stanford University, Stanford, California
| | - Daniele E. Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Indiana
| | - Andrew M. Kahn
- Department of Medicine, University of California San Diego, La Jolla, California
| | - Alison L. Marsden
- Department of Pediatrics (Cardiology), Bioengineering and ICME, Stanford University, Stanford, California
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