1
|
Kozitza CJ, Colebank MJ, Gonzalez-Pereira JP, Chesler NC, Lamers L, Roldán-Alzate A, Witzenburg CM. Estimating pulmonary arterial remodeling via an animal-specific computational model of pulmonary artery stenosis. Biomech Model Mechanobiol 2024:10.1007/s10237-024-01850-6. [PMID: 38918266 DOI: 10.1007/s10237-024-01850-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 04/17/2024] [Indexed: 06/27/2024]
Abstract
Pulmonary artery stenosis (PAS) often presents in children with congenital heart disease, altering blood flow and pressure during critical periods of growth and development. Variability in stenosis onset, duration, and severity result in variable growth and remodeling of the pulmonary vasculature. Computational fluid dynamics (CFD) models enable investigation into the hemodynamic impact and altered mechanics associated with PAS. In this study, a one-dimensional (1D) fluid dynamics model was used to simulate hemodynamics throughout the pulmonary arteries of individual animals. The geometry of the large pulmonary arteries was prescribed by animal-specific imaging, whereas the distal vasculature was simulated by a three-element Windkessel model at each terminal vessel outlet. Remodeling of the pulmonary vasculature, which cannot be measured in vivo, was estimated via model-fitted parameters. The large artery stiffness was significantly higher on the left side of the vasculature in the left pulmonary artery (LPA) stenosis group, but neither side differed from the sham group. The sham group exhibited a balanced distribution of total distal vascular resistance, whereas the left side was generally larger in the LPA stenosis group, with no significant differences between groups. In contrast, the peripheral compliance on the right side of the LPA stenosis group was significantly greater than the corresponding side of the sham group. Further analysis indicated the underperfused distal vasculature likely moderately decreased in radius with little change in stiffness given the increase in thickness observed with histology. Ultimately, our model enables greater understanding of pulmonary arterial adaptation due to LPA stenosis and has potential for use as a tool to noninvasively estimate remodeling of the pulmonary vasculature.
Collapse
Affiliation(s)
- Callyn J Kozitza
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
| | - Mitchel J Colebank
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, and Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | | | - Naomi C Chesler
- Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, and Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, USA
| | - Luke Lamers
- Pediatrics, Division of Cardiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Alejandro Roldán-Alzate
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Colleen M Witzenburg
- Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Brown AL, Sexton ZA, Hu Z, Yang W, Marsden AL. Computational approaches for mechanobiology in cardiovascular development and diseases. Curr Top Dev Biol 2024; 156:19-50. [PMID: 38556423 DOI: 10.1016/bs.ctdb.2024.01.006] [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] [Indexed: 04/02/2024]
Abstract
The cardiovascular development in vertebrates evolves in response to genetic and mechanical cues. The dynamic interplay among mechanics, cell biology, and anatomy continually shapes the hydraulic networks, characterized by complex, non-linear changes in anatomical structure and blood flow dynamics. To better understand this interplay, a diverse set of molecular and computational tools has been used to comprehensively study cardiovascular mechanobiology. With the continual advancement of computational capacity and numerical techniques, cardiovascular simulation is increasingly vital in both basic science research for understanding developmental mechanisms and disease etiologies, as well as in clinical studies aimed at enhancing treatment outcomes. This review provides an overview of computational cardiovascular modeling. Beginning with the fundamental concepts of computational cardiovascular modeling, it navigates through the applications of computational modeling in investigating mechanobiology during cardiac development. Second, the article illustrates the utility of computational hemodynamic modeling in the context of treatment planning for congenital heart diseases. It then delves into the predictive potential of computational models for elucidating tissue growth and remodeling processes. In closing, we outline prevailing challenges and future prospects, underscoring the transformative impact of computational cardiovascular modeling in reshaping cardiovascular science and clinical practice.
Collapse
Affiliation(s)
- Aaron L Brown
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Zachary A Sexton
- Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Zinan Hu
- Department of Mechanical Engineering, Stanford University, Stanford, CA, United States
| | - Weiguang Yang
- Department of Pediatrics, Stanford University, Stanford, CA, United States
| | - Alison L Marsden
- Department of Bioengineering, Stanford University, Stanford, CA, United States; Department of Pediatrics, Stanford University, Stanford, CA, United States.
| |
Collapse
|
4
|
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.
Collapse
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
| |
Collapse
|
5
|
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.
Collapse
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.
| |
Collapse
|
6
|
Shi Y, Zheng J, Zhang Y, Sun Q, Shen J, Gao Y, Sun J, Yang N, Zhou X, Li S, Weir-McCall JR, Xia P, Teng Z. The influence of flow distribution strategy for the quantification of pressure- and wall shear stress-derived parameters in the coronary artery: A CTA-based computational fluid dynamics analysis. J Biomech 2023; 161:111857. [PMID: 37939424 DOI: 10.1016/j.jbiomech.2023.111857] [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: 04/13/2023] [Revised: 10/15/2023] [Accepted: 11/01/2023] [Indexed: 11/10/2023]
Abstract
For image-based computational fluid dynamics (CFD) analysis to characterize the local coronary hemodynamic environment, the accuracy depends on the flow rate which is in turn associated with outlet branches' morphology. A good flow distribution strategy is important to mitigate the effect when certain branches cannot be considered. In this study, stenotic coronary arteries from 13 patients were used to analyze the effect of missing branches and different flow distribution strategies. Pressure- and wall shear stress (WSS)-derived parameters around the stenotic region (ROI) were compared, including fractional flow reserve (CT-FFR), instantaneous wave-free ratio (CT-iFR), resting distal to aortic coronary pressure (CT-Pd/Pa), time-averaged WSS, oscillatory shear index (OSI) and relative residence time (RRT). Three flow distribution strategies were the Huo-Kassab model at distal outlets (Type I), flow distribution based on outlet resistances (Type II), and a developed algorithm distributing flow at each bifurcation until the final outlets (Type III). Results showed that Type III strategy for models with truncated branch(es) had a good agreement in both pressure- and WSS-related results (interquatile range less than 0.12% and 4.02%, respectively) with the baseline model around the ROI. The relative difference of pressure- and WSS-related results were correlated with the flow differences in the ROI to the baseline mode. Type III strategy had the best performance in maintaining the flow in intermediate branches. It is recommended for CFD analysis. Removal of branches distal to a stenosis can be undertaken with an improved performance and maintained accuracy, while those proximal to the ROI should be kept.
Collapse
Affiliation(s)
- Yibing Shi
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Jin Zheng
- Department of Radiology, University of Cambridge, UK
| | - Ying Zhang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Quanlin Sun
- Department of Radiology, University of Cambridge, UK; Nanjing Jingsan Medical Science and Technology, Ltd., Jiangsu, China
| | - Jinhua Shen
- Nanjing Jingsan Medical Science and Technology, Ltd., Jiangsu, China
| | - Yongguang Gao
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Jingxi Sun
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Ning Yang
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Xuanxuan Zhou
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Suqing Li
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China
| | - Jonathan R Weir-McCall
- Department of Radiology, University of Cambridge, UK; Department of Radiology, Royal Papworth Hospital, Cambridge, UK
| | - Ping Xia
- Department of Radiology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Jiangsu, China.
| | - Zhongzhao Teng
- Department of Radiology, University of Cambridge, UK; Nanjing Jingsan Medical Science and Technology, Ltd., Jiangsu, China.
| |
Collapse
|
7
|
Menon K, Seo J, Fukazawa R, Ogawa S, Kahn AM, Burns JC, Marsden AL. Predictors of Myocardial Ischemia in Patients with Kawasaki Disease: Insights from Patient-Specific Simulations of Coronary Hemodynamics. J Cardiovasc Transl Res 2023; 16:1099-1109. [PMID: 36939959 DOI: 10.1007/s12265-023-10374-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/08/2023] [Indexed: 03/21/2023]
Abstract
Current treatments for patients with coronary aneurysms caused by Kawasaki disease (KD) are based primarily on aneurysm size. This ignores hemodynamic factors influencing myocardial ischemic risk. We performed patient-specific computational hemodynamics simulations for 15 KD patients, with parameters tuned to patients' arterial pressure and cardiac function. Ischemic risk was evaluated in 153 coronary arteries from simulated fractional flow reserve (FFR), wall shear stress, and residence time. FFR correlated weakly with aneurysm [Formula: see text]-scores (correlation coefficient, [Formula: see text]) but correlated better with the ratio of maximum-to-minimum aneurysmal lumen diameter ([Formula: see text]). FFR dropped more rapidly distal to aneurysms, and this correlated more with the lumen diameter ratio ([Formula: see text]) than [Formula: see text]-score ([Formula: see text]). Wall shear stress correlated better with the diameter ratio ([Formula: see text]), while residence time correlated more with [Formula: see text]-score ([Formula: see text]). Overall, the maximum-to-minimum diameter ratio predicted ischemic risk better than [Formula: see text]-score. Although FFR immediately distal to aneurysms was nonsignificant, its rapid rate of decrease suggests elevated risk.
Collapse
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
| | - Jongmin Seo
- Department of Mechanical Engineering, Kyung Hee University, Yongin-Si, Gyeonggi-Do, South Korea
| | - Ryuji Fukazawa
- Department of Pediatrics, Nippon Medical School Hospital, Tokyo, Japan
| | - Shunichi Ogawa
- Department of Pediatrics, Nippon Medical School Hospital, Tokyo, Japan
| | - Andrew M Kahn
- Division of Cardiovascular Medicine, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Jane C Burns
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, 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.
| |
Collapse
|
8
|
Poorbahrami K, Allshouse MR, Oakes JM. Dosimetry Sensitivity in a Lower Dimensional Model of Patient-Specific Asthma Subjects. IEEE Trans Biomed Eng 2023; 70:2581-2591. [PMID: 37030850 DOI: 10.1109/tbme.2023.3255784] [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: 03/19/2023]
Abstract
OBJECTIVE Experimental uncertainty will impact in silico model calculations of aerosol delivery and deposition. Patient-specific dosimetry models are often parameterized based on medical imaging data, which contain inherent experimental variability. METHODS Here, we created and parameterized 1D models of three subject-specific asthmatic subjects and randomly assigned perturbations of up to 15 % on airway diameter, segmental volume, and defected volume. Sensitivity of imaging data experimental variability on dosimetry metrics were quantified. RESULTS Lobar particle delivery primarily depended on the distal segmental volumes; 15 % range of noise resulted in delivery to the upper right lobe to vary at most from 15.2 and 18.2 % for one of the severe subjects. Particle deposition was most sensitive to airway diameter; 95 % confidence intervals spanned from 8 to 10.6 % in the mild/moderate subject for 15 % variation on input metrics for 5 [Formula: see text] diameter particles. While these results provide possible ranges of dosimetry calculations for a specific subject, the perturbations were not sufficient to model the large observed inter-subject variability (8.9, 19, and 14.5 % deposition, subjects 1--3, respectively, 5 [Formula: see text] diameter particles). CONCLUSION This study highlights that in silico model predictions are robust in the presence of experimental uncertainty and that it continues to be necessary to perform subject-specific simulations, especially within the presence of heterogeneous airway disease. SIGNIFICANCE Sensitivity analysis provides confidence in calculating deposition in the airways of asthmatic subjects within the presence of experimental uncertainty.
Collapse
|
9
|
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.
Collapse
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
| |
Collapse
|
10
|
Garber L, Khodaei S, Maftoon N, Keshavarz-Motamed Z. Impact of TAVR on coronary artery hemodynamics using clinical measurements and image-based patient-specific in silico modeling. Sci Rep 2023; 13:8948. [PMID: 37268642 DOI: 10.1038/s41598-023-31987-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/21/2023] [Indexed: 06/04/2023] Open
Abstract
In recent years, transcatheter aortic valve replacement (TAVR) has become the leading method for treating aortic stenosis. While the procedure has improved dramatically in the past decade, there are still uncertainties about the impact of TAVR on coronary blood flow. Recent research has indicated that negative coronary events after TAVR may be partially driven by impaired coronary blood flow dynamics. Furthermore, the current technologies to rapidly obtain non-invasive coronary blood flow data are relatively limited. Herein, we present a lumped parameter computational model to simulate coronary blood flow in the main arteries as well as a series of cardiovascular hemodynamic metrics. The model was designed to only use a few inputs parameters from echocardiography, computed tomography and a sphygmomanometer. The novel computational model was then validated and applied to 19 patients undergoing TAVR to examine the impact of the procedure on coronary blood flow in the left anterior descending (LAD) artery, left circumflex (LCX) artery and right coronary artery (RCA) and various global hemodynamics metrics. Based on our findings, the changes in coronary blood flow after TAVR varied and were subject specific (37% had increased flow in all three coronary arteries, 32% had decreased flow in all coronary arteries, and 31% had both increased and decreased flow in different coronary arteries). Additionally, valvular pressure gradient, left ventricle (LV) workload and maximum LV pressure decreased by 61.5%, 4.5% and 13.0% respectively, while mean arterial pressure and cardiac output increased by 6.9% and 9.9% after TAVR. By applying this proof-of-concept computational model, a series of hemodynamic metrics were generated non-invasively which can help to better understand the individual relationships between TAVR and mean and peak coronary flow rates. In the future, tools such as these may play a vital role by providing clinicians with rapid insight into various cardiac and coronary metrics, rendering the planning for TAVR and other cardiovascular procedures more personalized.
Collapse
Affiliation(s)
- Louis Garber
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada
| | - Seyedvahid Khodaei
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada
| | - Nima Maftoon
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada
| | - Zahra Keshavarz-Motamed
- School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada.
- Department of Mechanical Engineering (Mail to JHE-310), McMaster University, Hamilton, ON, L8S 4L7, Canada.
- School of Computational Science and Engineering, McMaster University, Hamilton, ON, Canada.
| |
Collapse
|
11
|
Schwarz EL, Pegolotti L, Pfaller MR, Marsden AL. Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. BIOPHYSICS REVIEWS 2023; 4:011301. [PMID: 36686891 PMCID: PMC9846834 DOI: 10.1063/5.0109400] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 12/12/2022] [Indexed: 01/15/2023]
Abstract
Physics-based computational models of the cardiovascular system are increasingly used to simulate hemodynamics, tissue mechanics, and physiology in evolving healthy and diseased states. While predictive models using computational fluid dynamics (CFD) originated primarily for use in surgical planning, their application now extends well beyond this purpose. In this review, we describe an increasingly wide range of modeling applications aimed at uncovering fundamental mechanisms of disease progression and development, performing model-guided design, and generating testable hypotheses to drive targeted experiments. Increasingly, models are incorporating multiple physical processes spanning a wide range of time and length scales in the heart and vasculature. With these expanded capabilities, clinical adoption of patient-specific modeling in congenital and acquired cardiovascular disease is also increasing, impacting clinical care and treatment decisions in complex congenital heart disease, coronary artery disease, vascular surgery, pulmonary artery disease, and medical device design. In support of these efforts, we discuss recent advances in modeling methodology, which are most impactful when driven by clinical needs. We describe pivotal recent developments in image processing, fluid-structure interaction, modeling under uncertainty, and reduced order modeling to enable simulations in clinically relevant timeframes. In all these areas, we argue that traditional CFD alone is insufficient to tackle increasingly complex clinical and biological problems across scales and systems. Rather, CFD should be coupled with appropriate multiscale biological, physical, and physiological models needed to produce comprehensive, impactful models of mechanobiological systems and complex clinical scenarios. With this perspective, we finally outline open problems and future challenges in the field.
Collapse
Affiliation(s)
- Erica L. Schwarz
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Luca Pegolotti
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Martin R. Pfaller
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| | - Alison L. Marsden
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, California 94305, USA
| |
Collapse
|
12
|
Yin M, Zhang E, Yu Y, Karniadakis GE. Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022; 402:115027. [PMID: 37384215 PMCID: PMC10300559 DOI: 10.1016/j.cma.2022.115027] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/30/2023]
Abstract
Multiscale modeling is an effective approach for investigating multiphysics systems with largely disparate size features, where models with different resolutions or heterogeneous descriptions are coupled together for predicting the system's response. The solver with lower fidelity (coarse) is responsible for simulating domains with homogeneous features, whereas the expensive high-fidelity (fine) model describes microscopic features with refined discretization, often making the overall cost prohibitively high, especially for time-dependent problems. In this work, we explore the idea of multiscale modeling with machine learning and employ DeepONet, a neural operator, as an efficient surrogate of the expensive solver. DeepONet is trained offline using data acquired from the fine solver for learning the underlying and possibly unknown fine-scale dynamics. It is then coupled with standard PDE solvers for predicting the multiscale systems with new boundary/initial conditions in the coupling stage. The proposed framework significantly reduces the computational cost of multiscale simulations since the DeepONet inference cost is negligible, facilitating readily the incorporation of a plurality of interface conditions and coupling schemes. We present various benchmarks to assess the accuracy and efficiency, including static and time-dependent problems. We also demonstrate the feasibility of coupling of a continuum model (finite element methods, FEM) with a neural operator, serving as a surrogate of a particle system (Smoothed Particle Hydrodynamics, SPH), for predicting mechanical responses of anisotropic and hyperelastic materials. What makes this approach unique is that a well-trained over-parametrized DeepONet can generalize well and make predictions at a negligible cost.
Collapse
Affiliation(s)
- Minglang Yin
- Center for Biomedical Engineering, Brown University, Providence, RI, United States of America
- School of Engineering, Brown University, Providence, RI, United States of America
| | - Enrui Zhang
- Division of Applied Mathematics, Brown University, Providence, RI, United States of America
| | - Yue Yu
- Department of Mathematics, Lehigh University, Bethlehem, PA, United States of America
| | - George Em Karniadakis
- School of Engineering, Brown University, Providence, RI, United States of America
- Division of Applied Mathematics, Brown University, Providence, RI, United States of America
| |
Collapse
|
13
|
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.
Collapse
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
| |
Collapse
|
14
|
Pfaller MR, Pham J, Verma A, Pegolotti L, Wilson NM, Parker DW, Yang W, Marsden AL. Automated generation of 0D and 1D reduced-order models of patient-specific blood flow. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3639. [PMID: 35875875 PMCID: PMC9561079 DOI: 10.1002/cnm.3639] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 05/24/2022] [Accepted: 07/19/2022] [Indexed: 06/13/2023]
Abstract
Three-dimensional (3D) cardiovascular fluid dynamics simulations typically require hours to days of computing time on a high-performance computing cluster. One-dimensional (1D) and lumped-parameter zero-dimensional (0D) models show great promise for accurately predicting blood bulk flow and pressure waveforms with only a fraction of the cost. They can also accelerate uncertainty quantification, optimization, and design parameterization studies. Despite several prior studies generating 1D and 0D models and comparing them to 3D solutions, these were typically limited to either 1D or 0D and a singular category of vascular anatomies. This work proposes a fully automated and openly available framework to generate and simulate 1D and 0D models from 3D patient-specific geometries, automatically detecting vessel junctions and stenosis segments. Our only input is the 3D geometry; we do not use any prior knowledge from 3D simulations. All computational tools presented in this work are implemented in the open-source software platform SimVascular. We demonstrate the reduced-order approximation quality against rigid-wall 3D solutions in a comprehensive comparison with N = 72 publicly available models from various anatomies, vessel types, and disease conditions. Relative average approximation errors of flows and pressures typically ranged from 1% to 10% for both 1D and 0D models, measured at the outlets of terminal vessel branches. In general, 0D model errors were only slightly higher than 1D model errors despite requiring only a third of the 1D runtime. Automatically generated ROMs can significantly speed up model development and shift the computational load from high-performance machines to personal computers.
Collapse
Affiliation(s)
- Martin R. Pfaller
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
- Cardiovascular Institute, Stanford University, CA, USA
| | - Jonathan Pham
- Mechanical Engineering, Stanford University, CA, USA
| | | | - Luca Pegolotti
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
| | | | | | | | - Alison L. Marsden
- Pediatric Cardiology, Stanford University, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, CA, USA
- Cardiovascular Institute, Stanford University, CA, USA
- Bioengineering, Stanford University, CA, USA
| |
Collapse
|
15
|
Tossas-Betancourt C, Li NY, Shavik SM, Afton K, Beckman B, Whiteside W, Olive MK, Lim HM, Lu JC, Phelps CM, Gajarski RJ, Lee S, Nordsletten DA, Grifka RG, Dorfman AL, Baek S, Lee LC, Figueroa CA. Data-driven computational models of ventricular-arterial hemodynamics in pediatric pulmonary arterial hypertension. Front Physiol 2022; 13:958734. [PMID: 36160862 PMCID: PMC9490558 DOI: 10.3389/fphys.2022.958734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Pulmonary arterial hypertension (PAH) is a complex disease involving increased resistance in the pulmonary arteries and subsequent right ventricular (RV) remodeling. Ventricular-arterial interactions are fundamental to PAH pathophysiology but are rarely captured in computational models. It is important to identify metrics that capture and quantify these interactions to inform our understanding of this disease as well as potentially facilitate patient stratification. Towards this end, we developed and calibrated two multi-scale high-resolution closed-loop computational models using open-source software: a high-resolution arterial model implemented using CRIMSON, and a high-resolution ventricular model implemented using FEniCS. Models were constructed with clinical data including non-invasive imaging and invasive hemodynamic measurements from a cohort of pediatric PAH patients. A contribution of this work is the discussion of inconsistencies in anatomical and hemodynamic data routinely acquired in PAH patients. We proposed and implemented strategies to mitigate these inconsistencies, and subsequently use this data to inform and calibrate computational models of the ventricles and large arteries. Computational models based on adjusted clinical data were calibrated until the simulated results for the high-resolution arterial models matched within 10% of adjusted data consisting of pressure and flow, whereas the high-resolution ventricular models were calibrated until simulation results matched adjusted data of volume and pressure waveforms within 10%. A statistical analysis was performed to correlate numerous data-derived and model-derived metrics with clinically assessed disease severity. Several model-derived metrics were strongly correlated with clinically assessed disease severity, suggesting that computational models may aid in assessing PAH severity.
Collapse
Affiliation(s)
| | - Nathan Y. Li
- Department of Pharmaceutical Sciences, University of Michigan, Ann Arbor, MI, United States
| | - Sheikh M. Shavik
- Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh
| | - Katherine Afton
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Brian Beckman
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Wendy Whiteside
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Mary K. Olive
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Heang M. Lim
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Jimmy C. Lu
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Christina M. Phelps
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Robert J. Gajarski
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Simon Lee
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - David A. Nordsletten
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
- School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Ronald G. Grifka
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Adam L. Dorfman
- Department of Pediatrics, Division of Pediatric Cardiology, University of Michigan, Ann Arbor, MI, United States
| | - Seungik Baek
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, United States
| | - Lik Chuan Lee
- Department of Mechanical Engineering, Michigan State University, East Lansing, MI, United States
| | - C. Alberto Figueroa
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
- Department of Surgery, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
16
|
Nolte D, Bertoglio C. Inverse problems in blood flow modeling: A review. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3613. [PMID: 35526113 PMCID: PMC9541505 DOI: 10.1002/cnm.3613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/17/2021] [Revised: 12/29/2021] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Mathematical and computational modeling of the cardiovascular system is increasingly providing non-invasive alternatives to traditional invasive clinical procedures. Moreover, it has the potential for generating additional diagnostic markers. In blood flow computations, the personalization of spatially distributed (i.e., 3D) models is a key step which relies on the formulation and numerical solution of inverse problems using clinical data, typically medical images for measuring both anatomy and function of the vasculature. In the last years, the development and application of inverse methods has rapidly expanded most likely due to the increased availability of data in clinical centers and the growing interest of modelers and clinicians in collaborating. Therefore, this work aims to provide a wide and comparative overview of literature within the last decade. We review the current state of the art of inverse problems in blood flows, focusing on studies considering fully dimensional fluid and fluid-solid models. The relevant physical models and hemodynamic measurement techniques are introduced, followed by a survey of mathematical data assimilation approaches used to solve different kinds of inverse problems, namely state and parameter estimation. An exhaustive discussion of the literature of the last decade is presented, structured by types of problems, models and available data.
Collapse
Affiliation(s)
- David Nolte
- Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands
- Center for Mathematical ModelingUniversidad de ChileSantiagoChile
- Department of Fluid DynamicsTechnische Universität BerlinBerlinGermany
| | | |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
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]
|
19
|
Perivascular pumping in the mouse brain: Improved boundary conditions reconcile theory, simulation, and experiment. J Theor Biol 2022; 542:111103. [PMID: 35339513 DOI: 10.1016/j.jtbi.2022.111103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 02/16/2022] [Accepted: 03/17/2022] [Indexed: 01/24/2023]
Abstract
Cerebrospinal fluid (CSF) flows through the perivascular spaces (PVSs) surrounding cerebral arteries. Revealing the mechanisms driving that flow could bring improved understanding of brain waste transport and insights for disorders including Alzheimer's disease and stroke. In vivo velocity measurements of CSF in surface PVSs in mice have been used to argue that flow is driven primarily by the pulsatile motion of artery walls - perivascular pumping. However, fluid dynamics theory and simulation have predicted that perivascular pumping produces flows differing from in vivo observations starkly, particularly in the phase and relative amplitude of flow oscillation. We show that coupling theoretical and simulated flows to more realistic end boundary conditions, using resistance and compliance values measured in mice instead of using periodic boundaries, results in velocities that match observations more closely in phase and relative amplitude of oscillation, while preserving the existing agreement in mean flow speed. This quantitative agreement among theory, simulation, and in vivo measurement further supports the idea that perivascular pumping is an important CSF driver in physiological conditions.
Collapse
|
20
|
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: 13] [Impact Index Per Article: 6.5] [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.
Collapse
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
| |
Collapse
|
21
|
Patient-Specific Fluid-Structure Simulations of Anomalous Aortic Origin of Right Coronary Arteries. JTCVS Tech 2022; 13:144-162. [PMID: 35711199 PMCID: PMC9196314 DOI: 10.1016/j.xjtc.2022.02.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 02/16/2022] [Indexed: 01/10/2023] Open
Abstract
Objectives Anomalous aortic origin of the right coronary artery (AAORCA) may cause ischemia and sudden death. However, the specific anatomic indications for surgery are unclear, so dobutamine-stress instantaneous wave-free ratio (iFR) is increasingly used. Meanwhile, advances in fluid–structure interaction (FSI) modeling can simulate the pulsatile hemodynamics and tissue deformation. We sought to evaluate the feasibility of simulating the resting and dobutamine-stress iFR in AAORCA using patient-specific FSI models and to visualize the mechanism of ischemia within the intramural geometry and associated lumen narrowing. Methods We developed 6 patient-specific FSI models of AAORCA using SimVascular software. Three-dimensional geometries were segmented from coronary computed tomography angiography. Vascular outlets were coupled to lumped-parameter networks that included dynamic compression of the coronary microvasculature and were tuned to each patient's vitals and cardiac output. Results All cases were interarterial, and 5 of 6 had an intramural course. Measured iFRs ranged from 0.95 to 0.98 at rest and 0.80 to 0.95 under dobutamine stress. After we tuned the distal coronary resistances to achieve a stress flow rate triple that at rest, the simulations adequately matched the measured iFRs (r = 0.85, root-mean-square error = 0.04). The intramural lumen remained narrowed with simulated stress and resulted in lower iFRs without needing external compression from the pulmonary root. Conclusions Patient-specific FSI modeling of AAORCA is a promising, noninvasive method to assess the iFR reduction caused by intramural geometries and inform surgical intervention. However, the models’ sensitivity to distal coronary resistance suggests that quantitative stress-perfusion imaging may augment virtual and invasive iFR studies.
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
Jones G, Parr J, Nithiarasu P, Pant S. A physiologically realistic virtual patient database for the study of arterial haemodynamics. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3497. [PMID: 33973397 DOI: 10.1002/cnm.3497] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/30/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
This study creates a physiologically realistic virtual patient database (VPD), representing the human arterial system, for the primary purpose of studying the effects of arterial disease on haemodynamics. A low dimensional representation of an anatomically detailed arterial network is outlined, and a physiologically realistic posterior distribution for its parameters constructed through the use of a Bayesian approach. This approach combines both physiological/geometrical constraints and the available measurements reported in the literature. A key contribution of this work is to present a framework for including all such available information for the creation of virtual patients (VPs). The Markov Chain Monte Carlo (MCMC) method is used to sample random VPs from this posterior distribution, and the pressure and flow-rate profiles associated with each VP computed through a physics based model of pulse wave propagation. This combination of the arterial network parameters (representing a virtual patient) and the haemodynamics waveforms of pressure and flow-rates at various locations (representing functional response and potential measurements that can be acquired in the virtual patient) makes up the VPD. While 75,000 VPs are sampled from the posterior distribution, 10,000 are discarded as the initial burn-in period of the MCMC sampler. A further 12,857 VPs are subsequently removed due to the presence of negative average flow-rate, reducing the VPD to 52,143. Due to undesirable behaviour observed in some VPs-asymmetric under- and over-damped pressure and flow-rate profiles in left and right sides of the arterial system-a filter is proposed to remove VPs showing such behaviour. Post application of the filter, the VPD has 28,868 subjects. It is shown that the methodology is appropriate by comparing the VPD statistics to those reported in literature across real populations. Generally, a good agreement between the two is found while respecting physiological/geometrical constraints.
Collapse
Affiliation(s)
- Gareth Jones
- College of Engineering, Swansea University, Swansea, UK
| | - Jim Parr
- Applied Technologies, McLaren Technology Centre, Woking, UK
| | | | - Sanjay Pant
- College of Engineering, Swansea University, Swansea, UK
| |
Collapse
|
24
|
Fevola E, Ballarin F, Jiménez‐Juan L, Fremes S, Grivet‐Talocia S, Rozza G, Triverio P. An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3516. [PMID: 34337877 PMCID: PMC9285750 DOI: 10.1002/cnm.3516] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/26/2021] [Indexed: 06/01/2023]
Abstract
The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress (WSS), which are of clinical interest. Devising automated procedures for the selection of boundary conditions is vital to achieve repeatable simulations. However, the most common techniques do not automatically assimilate patient-specific data, relying instead on expensive and time-consuming manual tuning procedures. In this work, we propose a technique for the automated estimation of outlet boundary conditions based on optimal control. The values of resistive boundary conditions are set as control variables and optimized to match available patient-specific data. Experimental results on four aortic arches demonstrate that the proposed framework can assimilate 4D-Flow MRI data more accurately than two other common techniques based on Murray's law and Ohm's law.
Collapse
Affiliation(s)
- Elisa Fevola
- Department of Electronics and TelecommunicationsPolitecnico di TorinoTorinoItaly
| | - Francesco Ballarin
- MathLab, Mathematics areaSISSA ‐ International School for Advanced StudiesTriesteItaly
- Department of Mathematics and PhysicsCatholic University of the Sacred HeartBresciaItaly
| | - Laura Jiménez‐Juan
- Department of Medical ImagingSt Michael's Hospital and Sunnybrook Research Institute, University of TorontoTorontoCanada
| | - Stephen Fremes
- Schulich Heart CentreSunnybrook Health Sciences Center and Sunnybrook Research Institute, University of TorontoTorontoCanada
| | | | - Gianluigi Rozza
- MathLab, Mathematics areaSISSA ‐ International School for Advanced StudiesTriesteItaly
| | - Piero Triverio
- Department of Electrical & Computer EngineeringInstitute of Biomedical Engineering, University of TorontoTorontoCanada
| |
Collapse
|
25
|
Dong ML, Lan IS, Yang W, Rabinovitch M, Feinstein JA, Marsden AL. Computational simulation-derived hemodynamic and biomechanical properties of the pulmonary arterial tree early in the course of ventricular septal defects. Biomech Model Mechanobiol 2021; 20:2471-2489. [PMID: 34585299 DOI: 10.1007/s10237-021-01519-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 09/12/2021] [Indexed: 01/15/2023]
Abstract
Untreated ventricular septal defects (VSDs) can lead to pulmonary arterial hypertension (PAH) characterized by elevated pulmonary artery (PA) pressure and vascular remodeling, known as PAH associated with congenital heart disease (PAH-CHD). Though previous studies have investigated hemodynamic effects on vascular mechanobiology in late-stage PAH, hemodynamics leading to PAH-CHD initiation have not been fully quantified. We hypothesize that abnormal hemodynamics from left-to-right shunting in early stage VSDs affects PA biomechanical properties leading to PAH initiation. To model PA hemodynamics in healthy, small, moderate, and large VSD conditions prior to the onset of vascular remodeling, computational fluid dynamics simulations were performed using a 3D finite element model of a healthy 1-year-old's proximal PAs and a body-surface-area-scaled 0D distal PA tree. VSD conditions were modeled with increased pulmonary blood flow to represent degrees of left-to-right shunting. In the proximal PAs, pressure, flow, strain, and wall shear stress (WSS) increased with increasing VSD size; oscillatory shear index decreased with increasing VSD size in the larger PA vessels. WSS was higher in smaller diameter vessels and increased with VSD size, with the large VSD condition exhibiting WSS >100 dyn/cm[Formula: see text], well above values typically used to study dysfunctional mechanotransduction pathways in PAH. This study is the first to estimate hemodynamic and biomechanical metrics in the entire pediatric PA tree with VSD severity at the stage leading to PAH initiation and has implications for future studies assessing effects of abnormal mechanical stimuli on endothelial cells and vascular wall mechanics that occur during PAH-CHD initiation and progression.
Collapse
Affiliation(s)
- Melody L Dong
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Ingrid S Lan
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Weiguang Yang
- Department of Pediatrics, Stanford University, Stanford, CA, USA
| | | | - Jeffrey A Feinstein
- Department of Pediatrics and Bioengineering, Stanford University, Stanford, CA, USA
| | - Alison L Marsden
- Department of Pediatrics and Bioengineering, Stanford University, Stanford, CA, USA.
| |
Collapse
|
26
|
Carpenter HJ, Gholipour A, Ghayesh MH, Zander AC, Psaltis PJ. In Vivo Based Fluid-Structure Interaction Biomechanics of the Left Anterior Descending Coronary Artery. J Biomech Eng 2021; 143:081001. [PMID: 33729476 DOI: 10.1115/1.4050540] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Indexed: 12/25/2022]
Abstract
A fluid-structure interaction-based biomechanical model of the entire left anterior descending coronary artery is developed from in vivo imaging via the finite element method in this paper. Included in this investigation is ventricle contraction, three-dimensional motion, all angiographically visible side branches, hyper/viscoelastic artery layers, non-Newtonian and pulsatile blood flow, and the out-of-phase nature of blood velocity and pressure. The fluid-structure interaction model is based on in vivo angiography of an elite athlete's entire left anterior descending coronary artery where the influence of including all alternating side branches and the dynamical contraction of the ventricle is investigated for the first time. Results show the omission of side branches result in a 350% increase in peak wall shear stress and a 54% decrease in von Mises stress. Peak von Mises stress is underestimated by up to 80% when excluding ventricle contraction and further alterations in oscillatory shear indices are seen, which provide an indication of flow reversal and has been linked to atherosclerosis localization. Animations of key results are also provided within a video abstract. We anticipate that this model and results can be used as a basis for our understanding of the interaction between coronary and myocardium biomechanics. It is hoped that further investigations could include the passive and active components of the myocardium to further replicate in vivo mechanics and lead to an understanding of the influence of cardiac abnormalities, such as arrythmia, on coronary biomechanical responses.
Collapse
Affiliation(s)
- Harry J Carpenter
- School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Alireza Gholipour
- School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Mergen H Ghayesh
- School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Anthony C Zander
- School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Peter J Psaltis
- Vascular Research Centre, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia 5000, Australia; Adelaide Medical School, University of Adelaide, Adelaide, South Australia 5005, Australia; Department of Cardiology, Central Adelaide Local Health Network, Adelaide, South Australia 5000, Australia
| |
Collapse
|
27
|
Khan MO, Tran JS, Zhu H, Boyd J, Packard RRS, Karlsberg RP, Kahn AM, Marsden AL. Low Wall Shear Stress Is Associated with Saphenous Vein Graft Stenosis in Patients with Coronary Artery Bypass Grafting. J Cardiovasc Transl Res 2021; 14:770-781. [PMID: 32240496 PMCID: PMC7529767 DOI: 10.1007/s12265-020-09982-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Accepted: 02/28/2020] [Indexed: 12/21/2022]
Abstract
Biomechanical forces may play a key role in saphenous vein graft (SVG) disease after coronary artery bypass graft (CABG) surgery. Computed tomography angiography (CTA) of 430 post-CABG patients were evaluated and 15 patients were identified with both stenosed and healthy SVGs for paired analysis. The stenosis was virtually removed, and detailed 3D models were reconstructed to perform patient-specific computational fluid dynamic (CFD) simulations. Models were processed to compute anatomic parameters, and hemodynamic parameters such as local and vessel-averaged wall shear stress (WSS), normalized WSS (WSS*), low shear area (LSA), oscillatory shear index (OSI), and flow rate. WSS* was significantly lower in pre-diseased SVG segments compared to corresponding control segments without disease (1.22 vs. 1.73, p = 0.012) and the area under the ROC curve was 0.71. No differences were observed in vessel-averaged anatomic or hemodynamic parameters between pre-stenosed and control whole SVGs. There are currently no clinically available tools to predict SVG failure post-CABG. CFD modeling has the potential to identify high-risk CABG patients who may benefit from more aggressive medical therapy and closer surveillance. Graphical Abstract.
Collapse
Affiliation(s)
- Muhammad Owais Khan
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Justin S Tran
- Department of Mechanical Engineering, California State University Fullerton, Fullerton, CA, USA
| | - Han Zhu
- Department of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jack Boyd
- Department of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - René R Sevag Packard
- Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Ronald P Karlsberg
- Cardiovascular Medical Group of Southern California, Beverly Hills, CA, USA
| | - Andrew M Kahn
- Division of Cardiovascular Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Alison L Marsden
- Department of Pediatrics, Stanford University 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.
- Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA.
| |
Collapse
|
28
|
Harrod KK, Rogers JL, Feinstein JA, Marsden AL, Schiavazzi DE. Predictive Modeling of Secondary Pulmonary Hypertension in Left Ventricular Diastolic Dysfunction. Front Physiol 2021; 12:666915. [PMID: 34276397 PMCID: PMC8281259 DOI: 10.3389/fphys.2021.666915] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 04/16/2021] [Indexed: 12/03/2022] Open
Abstract
Diastolic dysfunction is a common pathology occurring in about one third of patients affected by heart failure. This condition may not be associated with a marked decrease in cardiac output or systemic pressure and therefore is more difficult to diagnose than its systolic counterpart. Compromised relaxation or increased stiffness of the left ventricle induces an increase in the upstream pulmonary pressures, and is classified as secondary or group II pulmonary hypertension (2018 Nice classification). This may result in an increase in the right ventricular afterload leading to right ventricular failure. Elevated pulmonary pressures are therefore an important clinical indicator of diastolic heart failure (sometimes referred to as heart failure with preserved ejection fraction, HFpEF), showing significant correlation with associated mortality. However, accurate measurements of this quantity are typically obtained through invasive catheterization and after the onset of symptoms. In this study, we use the hemodynamic consistency of a differential-algebraic circulation model to predict pulmonary pressures in adult patients from other, possibly non-invasive, clinical data. We investigate several aspects of the problem, including the ability of model outputs to represent a sufficiently wide pathologic spectrum, the identifiability of the model's parameters, and the accuracy of the predicted pulmonary pressures. We also find that a classifier using the assimilated model parameters as features is free from the problem of missing data and is able to detect pulmonary hypertension with sufficiently high accuracy. For a cohort of 82 patients suffering from various degrees of heart failure severity, we show that systolic, diastolic, and wedge pulmonary pressures can be estimated on average within 8, 6, and 6 mmHg, respectively. We also show that, in general, increased data availability leads to improved predictions.
Collapse
Affiliation(s)
- Karlyn K Harrod
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, United States
| | - Jeffrey L Rogers
- Department of Digital Health, T.J. Watson Research Center, International Business Machines Corporation, Yorktown Heights, NY, United States
| | - Jeffrey A Feinstein
- Department of Pediatrics and Bioengineering, Stanford University, Stanford, CA, United States
| | - Alison L Marsden
- Department of Pediatrics, Bioengineering and Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, United States
| | - Daniele E Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, United States
| |
Collapse
|
29
|
Integrating multi-fidelity blood flow data with reduced-order data assimilation. Comput Biol Med 2021; 135:104566. [PMID: 34157468 DOI: 10.1016/j.compbiomed.2021.104566] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Revised: 05/30/2021] [Accepted: 06/08/2021] [Indexed: 11/20/2022]
Abstract
High-fidelity patient-specific modeling of cardiovascular flows and hemodynamics is challenging. Direct blood flow measurement inside the body with in-vivo measurement modalities such as 4D flow magnetic resonance imaging (4D flow MRI) suffer from low resolution and acquisition noise. In-vitro experimental modeling and patient-specific computational fluid dynamics (CFD) models are subject to uncertainty in patient-specific boundary conditions and model parameters. Furthermore, collecting blood flow data in the near-wall region (e.g., wall shear stress) with experimental measurement modalities poses additional challenges. In this study, a computationally efficient data assimilation method called reduced-order modeling Kalman filter (ROM-KF) was proposed, which combined a sequential Kalman filter with reduced-order modeling using a linear model provided by dynamic mode decomposition (DMD). The goal of ROM-KF was to overcome low resolution and noise in experimental and uncertainty in CFD modeling of cardiovascular flows. The accuracy of the method was assessed with 1D Womersley flow, 2D idealized aneurysm, and 3D patient-specific cerebral aneurysm models. Synthetic experimental data were used to enable direct quantification of errors using benchmark datasets. The accuracy of ROM-KF in reconstructing near-wall hemodynamics was assessed by applying the method to problems where near-wall blood flow data were missing in the experimental dataset. The ROM-KF method provided blood flow data that were more accurate than the computational and synthetic experimental datasets and improved near-wall hemodynamics quantification.
Collapse
|
30
|
Khosravi R, Ramachandra AB, Szafron JM, Schiavazzi DE, Breuer CK, Humphrey JD. A computational bio-chemo-mechanical model of in vivo tissue-engineered vascular graft development. Integr Biol (Camb) 2021; 12:47-63. [PMID: 32222759 DOI: 10.1093/intbio/zyaa004] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 01/26/2020] [Accepted: 02/04/2020] [Indexed: 12/15/2022]
Abstract
Stenosis is the primary complication of current tissue-engineered vascular grafts used in pediatric congenital cardiac surgery. Murine models provide considerable insight into the possible mechanisms underlying this situation, but they are not efficient for identifying optimal changes in scaffold design or therapeutic strategies to prevent narrowing. In contrast, computational modeling promises to enable time- and cost-efficient examinations of factors leading to narrowing. Whereas past models have been limited by their phenomenological basis, we present a new mechanistic model that integrates molecular- and cellular-driven immuno- and mechano-mediated contributions to in vivo neotissue development within implanted polymeric scaffolds. Model parameters are inferred directly from in vivo measurements for an inferior vena cava interposition graft model in the mouse that are augmented by data from the literature. By complementing Bayesian estimation with identifiability analysis and simplex optimization, we found optimal parameter values that match model outputs with experimental targets and quantify variability due to measurement uncertainty. Utility is illustrated by parametrically exploring possible graft narrowing as a function of scaffold pore size, macrophage activity, and the immunomodulatory cytokine transforming growth factor beta 1 (TGF-β1). The model captures salient temporal profiles of infiltrating immune and synthetic cells and associated secretion of cytokines, proteases, and matrix constituents throughout neovessel evolution, and parametric studies suggest that modulating scaffold immunogenicity with early immunomodulatory therapies may reduce graft narrowing without compromising compliance.
Collapse
Affiliation(s)
- Ramak Khosravi
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | | | - Jason M Szafron
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Daniele E Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Christopher K Breuer
- Center for Regenerative Medicine, Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Jay D Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.,Vascular Biology and Therapeutics Program, Yale School of Medicine, New Haven, CT, USA
| |
Collapse
|
31
|
Paun LM, Husmeier D. Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3421. [PMID: 33249755 PMCID: PMC7901000 DOI: 10.1002/cnm.3421] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 11/07/2020] [Accepted: 11/18/2020] [Indexed: 06/12/2023]
Abstract
The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid-dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient-specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed-form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state-of-the-art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long-term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system.
Collapse
Affiliation(s)
- L. Mihaela Paun
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| | - Dirk Husmeier
- School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK
| |
Collapse
|
32
|
Computational Modeling of Blood Flow Hemodynamics for Biomechanical Investigation of Cardiac Development and Disease. J Cardiovasc Dev Dis 2021; 8:jcdd8020014. [PMID: 33572675 PMCID: PMC7912127 DOI: 10.3390/jcdd8020014] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 01/16/2021] [Accepted: 01/21/2021] [Indexed: 12/11/2022] Open
Abstract
The heart is the first functional organ in a developing embryo. Cardiac development continues throughout developmental stages while the heart goes through a serious of drastic morphological changes. Previous animal experiments as well as clinical observations showed that disturbed hemodynamics interfere with the development of the heart and leads to the formation of a variety of defects in heart valves, heart chambers, and blood vessels, suggesting that hemodynamics is a governing factor for cardiogenesis, and disturbed hemodynamics is an important source of congenital heart defects. Therefore, there is an interest to image and quantify the flowing blood through a developing heart. Flow measurement in embryonic fetal heart can be performed using advanced techniques such as magnetic resonance imaging (MRI) or echocardiography. Computational fluid dynamics (CFD) modeling is another approach especially useful when the other imaging modalities are not available and in-depth flow assessment is needed. The approach is based on numerically solving relevant physical equations to approximate the flow hemodynamics and tissue behavior. This approach is becoming widely adapted to simulate cardiac flows during the embryonic development. While there are few studies for human fetal cardiac flows, many groups used zebrafish and chicken embryos as useful models for elucidating normal and diseased cardiogenesis. In this paper, we explain the major steps to generate CFD models for simulating cardiac hemodynamics in vivo and summarize the latest findings on chicken and zebrafish embryos as well as human fetal hearts.
Collapse
|
33
|
Mirramezani M, Shadden SC. A Distributed Lumped Parameter Model of Blood Flow. Ann Biomed Eng 2020; 48:2870-2886. [PMID: 32613457 PMCID: PMC7725998 DOI: 10.1007/s10439-020-02545-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 06/03/2020] [Indexed: 01/02/2023]
Abstract
We propose a distributed lumped parameter (DLP) modeling framework to efficiently compute blood flow and pressure in vascular domains. This is achieved by developing analytical expressions describing expected energy losses along vascular segments, including from viscous dissipation, unsteadiness, flow separation, vessel curvature and vessel bifurcations. We apply this methodology to solve for unsteady blood flow and pressure in a variety of complex 3D image-based vascular geometries, which are typically approached using computational fluid dynamics (CFD) simulations. The proposed DLP framework demonstrated consistent agreement with CFD simulations in terms of flow rate and pressure distribution, with mean errors less than 7% over a broad range of hemodynamic conditions and vascular geometries. The computational cost of the DLP framework is orders of magnitude lower than the computational cost of CFD, which opens new possibilities for hemodynamics modeling in timely decision support scenarios, and a multitude of applications of imaged-based modeling that require ensembles of numerical simulations.
Collapse
Affiliation(s)
- Mehran Mirramezani
- Mechanical Engineering, University of California, Berkeley, CA, 94720, USA
- Mathematics, University of California, Berkeley, CA, 94720, USA
| | - Shawn C Shadden
- Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
| |
Collapse
|
34
|
Eslami P, Thondapu V, Karady J, Hartman EMJ, Jin Z, Albaghdadi M, Lu M, Wentzel JJ, Hoffmann U. Physiology and coronary artery disease: emerging insights from computed tomography imaging based computational modeling. Int J Cardiovasc Imaging 2020; 36:2319-2333. [PMID: 32779078 PMCID: PMC8323761 DOI: 10.1007/s10554-020-01954-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 07/23/2020] [Indexed: 12/15/2022]
Abstract
Improvements in spatial and temporal resolution now permit robust high quality characterization of presence, morphology and composition of coronary atherosclerosis in computed tomography (CT). These characteristics include high risk features such as large plaque volume, low CT attenuation, napkin-ring sign, spotty calcification and positive remodeling. Because of the high image quality, principles of patient-specific computational fluid dynamics modeling of blood flow through the coronary arteries can now be applied to CT and allow the calculation of local lesion-specific hemodynamics such as endothelial shear stress, fractional flow reserve and axial plaque stress. This review examines recent advances in coronary CT image-based computational modeling and discusses the opportunity to identify lesions at risk for rupture much earlier than today through the combination of anatomic and hemodynamic information.
Collapse
Affiliation(s)
- Parastou Eslami
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Vikas Thondapu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Julia Karady
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Eline M J Hartman
- Department of Cardiology, Biomedical Engineering, Erasmus MC, Rotterdam, The Netherlands
| | - Zexi Jin
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mazen Albaghdadi
- Department of Cardiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Lu
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jolanda J Wentzel
- Department of Cardiology, Biomedical Engineering, Erasmus MC, Rotterdam, The Netherlands
| | - Udo Hoffmann
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
35
|
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.
Collapse
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
| |
Collapse
|
36
|
Fleeter CM, Geraci G, Schiavazzi DE, Kahn AM, Marsden AL. Multilevel and multifidelity uncertainty quantification for cardiovascular hemodynamics. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020; 365:113030. [PMID: 32336811 PMCID: PMC7182133 DOI: 10.1016/j.cma.2020.113030] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Standard approaches for uncertainty quantification in cardiovascular modeling pose challenges due to the large number of uncertain inputs and the significant computational cost of realistic three-dimensional simulations. We propose an efficient uncertainty quantification framework utilizing a multilevel multifidelity Monte Carlo (MLMF) estimator to improve the accuracy of hemodynamic quantities of interest while maintaining reasonable computational cost. This is achieved by leveraging three cardiovascular model fidelities, each with varying spatial resolution to rigorously quantify the variability in hemodynamic outputs. We employ two low-fidelity models (zero- and one-dimensional) to construct several different estimators. Our goal is to investigate and compare the efficiency of estimators built from combinations of these two low-fidelity model alternatives and our high-fidelity three-dimensional models. We demonstrate this framework on healthy and diseased models of aortic and coronary anatomy, including uncertainties in material property and boundary condition parameters. Our goal is to demonstrate that for this application it is possible to accelerate the convergence of the estimators by utilizing a MLMF paradigm. Therefore, we compare our approach to single fidelity Monte Carlo estimators and to a multilevel Monte Carlo approach based only on three-dimensional simulations, but leveraging multiple spatial resolutions. We demonstrate significant, on the order of 10 to 100 times, reduction in total computational cost with the MLMF estimators. We also examine the differing properties of the MLMF estimators in healthy versus diseased models, as well as global versus local quantities of interest. As expected, global quantities such as outlet pressure and flow show larger reductions than local quantities, such as those relating to wall shear stress, as the latter rely more heavily on the highest fidelity model evaluations. Similarly, healthy models show larger reductions than diseased models. In all cases, our workflow coupling Dakota's MLMF estimators with the SimVascular cardiovascular modeling framework makes uncertainty quantification feasible for constrained computational budgets.
Collapse
Affiliation(s)
- Casey M. Fleeter
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Gianluca Geraci
- Center for Computing Research, Sandia National Laboratories, Albuquerque, NM, USA
| | - Daniele E. Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA
| | - Andrew M. Kahn
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Alison L. Marsden
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
- Departments of Pediatrics and Bioengineering, Stanford University, Stanford, CA, USA
| |
Collapse
|
37
|
Seo J, Schiavazzi DE, Marsden AL. Performance of preconditioned iterative linear solvers for cardiovascular simulations in rigid and deformable vessels. COMPUTATIONAL MECHANICS 2019; 64:717-739. [PMID: 31827310 PMCID: PMC6905469 DOI: 10.1007/s00466-019-01678-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 01/21/2019] [Indexed: 05/31/2023]
Abstract
Computing the solution of linear systems of equations is invariably the most time consuming task in the numerical solutions of PDEs in many fields of computational science. In this study, we focus on the numerical simulation of cardiovascular hemodynamics with rigid and deformable walls, discretized in space and time through the variational multiscale finite element method. We focus on three approaches: the problem agnostic generalized minimum residual (GMRES) and stabilized bi-conjugate gradient (BICGS) methods, and a recently proposed, problem specific, bi-partitioned (BIPN) method. We also perform a comparative analysis of several preconditioners, including diagonal, block-diagonal, incomplete factorization, multigrid, and resistance based methods. Solver performance and matrix characteristics (diagonal dominance, symmetry, sparsity, bandwidth and spectral properties) are first examined for an idealized cylindrical geometry with physiologic boundary conditions and then successively tested on several patient-specific anatomies representative of realistic cardiovascular simulation problems. Incomplete factorization preconditioners provide the best performance and results in terms of both strong and weak scalability. The BIPN method was found to outperform other methods in patient-specific models with rigid walls. In models with deformable walls, BIPN was outperformed by BICG with diagonal and Incomplete LU preconditioners.
Collapse
Affiliation(s)
- Jongmin Seo
- Department of Pediatrics and Institute for Computational and Mathematical Engineering(ICME), Stanford University, Stanford, CA, USA,
| | - Daniele E Schiavazzi
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, IN, USA,
| | - Alison L Marsden
- Department of Pediatrics, Bioengineering and ICME, Stanford University, Stanford, CA, USA,
| |
Collapse
|
38
|
|
39
|
Lan H, Updegrove A, Wilson NM, Maher GD, Shadden SC, Marsden AL. A Re-Engineered Software Interface and Workflow for the Open-Source SimVascular Cardiovascular Modeling Package. J Biomech Eng 2019; 140:2666622. [PMID: 29238826 DOI: 10.1115/1.4038751] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Indexed: 11/08/2022]
Abstract
Patient-specific simulation plays an important role in cardiovascular disease research, diagnosis, surgical planning and medical device design, as well as education in cardiovascular biomechanics. simvascular is an open-source software package encompassing an entire cardiovascular modeling and simulation pipeline from image segmentation, three-dimensional (3D) solid modeling, and mesh generation, to patient-specific simulation and analysis. SimVascular is widely used for cardiovascular basic science and clinical research as well as education, following increased adoption by users and development of a GATEWAY web portal to facilitate educational access. Initial efforts of the project focused on replacing commercial packages with open-source alternatives and adding increased functionality for multiscale modeling, fluid-structure interaction (FSI), and solid modeling operations. In this paper, we introduce a major SimVascular (SV) release that includes a new graphical user interface (GUI) designed to improve user experience. Additional improvements include enhanced data/project management, interactive tools to facilitate user interaction, new boundary condition (BC) functionality, plug-in mechanism to increase modularity, a new 3D segmentation tool, and new computer-aided design (CAD)-based solid modeling capabilities. Here, we focus on major changes to the software platform and outline features added in this new release. We also briefly describe our recent experiences using SimVascular in the classroom for bioengineering education.
Collapse
Affiliation(s)
- Hongzhi Lan
- Department of Pediatrics, Stanford University, Stanford, CA 94305
| | - Adam Updegrove
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720
| | - Nathan M Wilson
- Open Source Medical Software Corporation, Santa Monica, CA 90403
| | | | - Shawn C Shadden
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, CA 94720
| | - Alison L Marsden
- Department of Pediatrics, Stanford University, , Stanford, CA 94305-5428.,ICME, Stanford University, Stanford, CA 94305.,Department of Bioengineering, Stanford University, Stanford, CA 94305 e-mail:
| |
Collapse
|
40
|
Tran JS, Schiavazzi DE, Kahn AM, Marsden AL. Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2019; 345:402-428. [PMID: 31223175 PMCID: PMC6586227 DOI: 10.1016/j.cma.2018.10.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Coronary artery bypass graft surgery (CABG) is performed on more than 400,000 patients annually in the U.S. However, saphenous vein grafts (SVGs) implanted during CABG exhibit poor patency compared to arterial grafts, with failure rates up to 40% within 10 years after surgery. Differences in mechanical stimuli are known to play a role in driving maladaptation and have been correlated with endothelial damage and thrombus formation. As these quantities are difficult to measure in vivo, multi-scale coronary models offer a way to quantify them, while accounting for complex coronary physiology. However, prior studies have primarily focused on deterministic evaluations, without reporting variability in the model parameters due to uncertainty. This study aims to assess confidence in multi-scale predictions of wall shear stress and wall strain while accounting for uncertainty in peripheral hemodynamics and material properties. Boundary condition distributions are computed by assimilating uncertain clinical data, while spatial variations of vessel wall stiffness are obtained through approximation by a random field. We developed a stochastic submodeling approach to mitigate the computational burden of repeated multi-scale model evaluations to focus exclusively on the bypass grafts. This produces a two-level decomposition of quantities of interest into submodel contributions and full model/submodel discrepancies. We leverage these two levels in the context of forward uncertainty propagation using a previously proposed multi-resolution approach. The time- and space-averaged wall shear stress is well estimated with a coefficient of variation of <35%, but ignorance about the spatial distribution on the wall elastic modulus and thickness lead to large variations in an objective measure of wall strain, with coefficients of variation up to 100%. Sensitivity analysis reveals how the interactions between the flow and material parameters contribute to output variability.
Collapse
Affiliation(s)
- Justin S Tran
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | | | - Andrew M Kahn
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Alison L Marsden
- Department of Pediatrics (Cardiology), Bioengineering and ICME, Stanford University, Stanford, CA, USA
| |
Collapse
|
41
|
Aramburu J, Antón R, Rivas A, Ramos JC, Larraona GS, Sangro B, Bilbao JI. A methodology for numerically analysing the hepatic artery haemodynamics during B-TACE: a proof of concept. Comput Methods Biomech Biomed Engin 2019; 22:518-532. [PMID: 30732467 DOI: 10.1080/10255842.2019.1567720] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Balloon-occluded transarterial chemoembolisation (B-TACE) is an intraarterial transcatheter treatment for liver cancer. In B-TACE, an artery-occluding microballoon catheter occludes an artery and promotes collateral circulation for drug delivery to tumours. This paper presents a methodology for analysing the haemodynamics during B-TACE, by combining zero-dimensional and three-dimensional modelling tools. As a proof of concept, we apply the methodology to a patient-specific hepatic artery geometry and analyse two catheter locations. Results show that the blood flow redistribution can be predicted in this proof-of-concept study, suggesting that this approach could potentially be used to optimise catheter location.
Collapse
Affiliation(s)
- Jorge Aramburu
- a Universidad de Navarra , TECNUN Escuela de Ingenieros , Donostia-San Sebastián , Spain
| | - Raúl Antón
- a Universidad de Navarra , TECNUN Escuela de Ingenieros , Donostia-San Sebastián , Spain.,b Instituto de Investigación Sanitaria de Navarra (IdiSNA) , Pamplona , Spain
| | - Alejandro Rivas
- a Universidad de Navarra , TECNUN Escuela de Ingenieros , Donostia-San Sebastián , Spain
| | - Juan Carlos Ramos
- a Universidad de Navarra , TECNUN Escuela de Ingenieros , Donostia-San Sebastián , Spain
| | - Gorka S Larraona
- a Universidad de Navarra , TECNUN Escuela de Ingenieros , Donostia-San Sebastián , Spain
| | - Bruno Sangro
- b Instituto de Investigación Sanitaria de Navarra (IdiSNA) , Pamplona , Spain.,c Clínica Universidad de Navarra , Pamplona , Spain.,d Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) , Pamplona , Spain
| | - José Ignacio Bilbao
- b Instituto de Investigación Sanitaria de Navarra (IdiSNA) , Pamplona , Spain.,c Clínica Universidad de Navarra , Pamplona , Spain
| |
Collapse
|
42
|
Grande Gutierrez N, Mathew M, McCrindle BW, Tran JS, Kahn AM, Burns JC, Marsden AL. Hemodynamic variables in aneurysms are associated with thrombotic risk in children with Kawasaki disease. Int J Cardiol 2019; 281:15-21. [PMID: 30728104 DOI: 10.1016/j.ijcard.2019.01.092] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 12/22/2018] [Accepted: 01/25/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Thrombosis is a major adverse outcome associated with coronary artery aneurysms (CAAs) resulting from Kawasaki disease (KD). Clinical guidelines recommend initiation of anticoagulation therapy with maximum CAA diameter (Dmax) ≥8 mm or Z-score ≥ 10. Here, we investigate the role of aneurysm hemodynamics as a superior method for thrombotic risk stratification in KD patients. METHODS AND RESULTS We retrospectively studied ten KD patients with CAAs, including five patients who developed thrombosis. We constructed patient-specific anatomic models from cardiac magnetic resonance images and performed computational hemodynamic simulations using SimVascular. Our simulations incorporated pulsatile flow, deformable arterial walls and boundary conditions automatically tuned to match patient-specific arterial pressure and cardiac output. From simulation results, we derived local hemodynamic variables including time-averaged wall shear stress (TAWSS), low wall shear stress exposure, and oscillatory shear index (OSI). Local TAWSS was significantly lower in CAAs that developed thrombosis (1.2 ± 0.94 vs. 7.28 ± 9.77 dynes/cm2, p = 0.006) and the fraction of CAA surface area exposed to low wall shear stress was larger (0.69 ± 0.17 vs. 0.25 ± 0.26%, p = 0.005). Similarly, longer residence times were obtained in branches where thrombosis was confirmed (9.07 ± 6.26 vs. 2.05 ± 2.91 cycles, p = 0.004). No significant differences were found for OSI or anatomical measurements such us Dmax and Z-score. Assessment of thrombotic risk according to hemodynamic variables had higher sensitivity and specificity compared to standard clinical metrics (Dmax, Z-score). CONCLUSIONS Hemodynamic variables can be obtained non-invasively via simulation and may provide improved thrombotic risk stratification compared to current diameter-based metrics, facilitating long-term clinical management of KD patients with persistent CAAs.
Collapse
Affiliation(s)
| | - Mathew Mathew
- The Hospital for Sick Children, University of Toronto, Canada
| | | | - Justin S Tran
- Department of Mechanical Engineering, Stanford University, USA
| | - Andrew M Kahn
- Department of Medicine, University of California San Diego School of Medicine, USA
| | - Jane C Burns
- Department of Pediatrics, University of California San Diego School of Medicine, USA
| | - Alison L Marsden
- Departments of Pediatrics, Bioengineering and Institute for Computational and Mathematical Engineering, Stanford University, USA.
| |
Collapse
|
43
|
Boccadifuoco A, Mariotti A, Capellini K, Celi S, Salvetti MV. Validation of Numerical Simulations of Thoracic Aorta Hemodynamics: Comparison with In Vivo Measurements and Stochastic Sensitivity Analysis. Cardiovasc Eng Technol 2018; 9:688-706. [DOI: 10.1007/s13239-018-00387-x] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 10/11/2018] [Indexed: 10/28/2022]
|
44
|
Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD. Cardiovasc Eng Technol 2018; 9:582-596. [PMID: 30284186 DOI: 10.1007/s13239-018-00381-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 09/25/2018] [Indexed: 10/28/2022]
Abstract
PURPOSE Numerical assessment of the pressure drop across an aortic coarctation using CFD is a promising approach to replace invasive catheter-based measurements. The aim of this study was to investigate and quantify the uncertainty of numerical calculation of the pressure drop introduced during two essential steps of medical image processing: segmentation of the patient-specific geometry and measurement of patient-specific flow rates from 4D-flow-MRI. METHODS Based on the baseline segmentation, geometries with different stenosis diameters were generated for a sample of ten patients. The pressure drop generated by these geometries was calculated for different volume flow rates using computational fluid dynamics. Based on these simulations, a second order polynomial fit was calculated. Based on these polynomial fits an uncertainty of pressure drop calculation was quantified. RESULTS The calculated pressure drop values varied strongly between the patients. In four patients, pressure drops above and below the clinical threshold of 20 mmHg were found. The median standard deviation of the pressure drop was 2.3 mmHg. The sensitivity of the pressure drop toward changes in the volume flow rate or the stenosis geometry varied between patients. CONCLUSION The uncertainty of numerical pressure drop calculation introduced by uncertainties during image segmentation and measurement of volume flow rates was comparable to the uncertainty of pressure drop measurements using invasive catheterization. However, in some patients this uncertainty would have led to different treatment decision. Therefore, patient-specific uncertainty assessment might help to better understand the reliability of a numerically calculated biomarker as the pressure drop across an aortic coarctation.
Collapse
|
45
|
Qureshi MU, Colebank MJ, Paun LM, Ellwein Fix L, Chesler N, Haider MA, Hill NA, Husmeier D, Olufsen MS. Hemodynamic assessment of pulmonary hypertension in mice: a model-based analysis of the disease mechanism. Biomech Model Mechanobiol 2018; 18:219-243. [DOI: 10.1007/s10237-018-1078-8] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Accepted: 09/17/2018] [Indexed: 12/26/2022]
|
46
|
Yu H, Huang GP, Yang Z, Ludwig BR. A multiscale computational modeling for cerebral blood flow with aneurysms and/or stenoses. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e3127. [PMID: 29968364 DOI: 10.1002/cnm.3127] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2017] [Revised: 05/19/2018] [Accepted: 06/21/2018] [Indexed: 06/08/2023]
Abstract
A 1-dimensional (1D)-3-dimensional (3D) multiscale model for the human vascular network was proposed by combining a low-fidelity 1D modeling of blood circulation to account for the global hemodynamics with a detailed 3D simulation of a zonal vascular segment. The coupling approach involves a direct exchange of flow and pressure information at interfaces between the 1D and 3D models and thus enables patient-specific morphological models to be inserted into flow network with minimum computational efforts. The proposed method was validated with good agreements against 3 simplified test cases where experimental data and/or full 3D numerical solution were available. The application of the method in aneurysm and stenosis studies indicated that the deformation of the geometry caused by the diseases may change local pressure loss and as a consequence lead to an alteration of flow rate to the vessel segment.
Collapse
Affiliation(s)
- Hongtao Yu
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH, 45435, USA
| | - George P Huang
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH, 45435, USA
| | - Zifeng Yang
- Department of Mechanical and Materials Engineering, Wright State University, Dayton, OH, 45435, USA
| | - Bryan R Ludwig
- Boonshoft School of Medicine, Wright State University, Dayton, OH, 45435, USA
- Department of Neurology, Division of NeuroInterventional Surgery, Wright State University/Premier Health-Clinical Neuroscience Institute, 30 E. Apple St, Dayton, OH, 45409, USA
| |
Collapse
|
47
|
Biehler J, Wall WA. The impact of personalized probabilistic wall thickness models on peak wall stress in abdominal aortic aneurysms. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2922. [PMID: 28796436 DOI: 10.1002/cnm.2922] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 07/04/2017] [Accepted: 08/06/2017] [Indexed: 06/07/2023]
Abstract
If computational models are ever to be used in high-stakes decision making in clinical practice, the use of personalized models and predictive simulation techniques is a must. This entails rigorous quantification of uncertainties as well as harnessing available patient-specific data to the greatest extent possible. Although researchers are beginning to realize that taking uncertainty in model input parameters into account is a necessity, the predominantly used probabilistic description for these uncertain parameters is based on elementary random variable models. In this work, we set out for a comparison of different probabilistic models for uncertain input parameters using the example of an uncertain wall thickness in finite element models of abdominal aortic aneurysms. We provide the first comparison between a random variable and a random field model for the aortic wall and investigate the impact on the probability distribution of the computed peak wall stress. Moreover, we show that the uncertainty about the prevailing peak wall stress can be reduced if noninvasively available, patient-specific data are harnessed for the construction of the probabilistic wall thickness model.
Collapse
Affiliation(s)
- J Biehler
- Institute for Computational Mechanics, Technische Universität München, Boltzmannstraße 15, Garching, 85748, Germany
| | - W A Wall
- Institute for Computational Mechanics, Technische Universität München, Boltzmannstraße 15, Garching, 85748, Germany
| |
Collapse
|
48
|
Chiastra C, Migliori S, Burzotta F, Dubini G, Migliavacca F. Patient-Specific Modeling of Stented Coronary Arteries Reconstructed from Optical Coherence Tomography: Towards a Widespread Clinical Use of Fluid Dynamics Analyses. J Cardiovasc Transl Res 2017; 11:156-172. [PMID: 29282628 PMCID: PMC5908818 DOI: 10.1007/s12265-017-9777-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 12/18/2017] [Indexed: 11/30/2022]
Abstract
The recent widespread application of optical coherence tomography (OCT) in interventional cardiology has improved patient-specific modeling of stented coronary arteries for the investigation of local hemodynamics. In this review, the workflow for the creation of fluid dynamics models of stented coronary arteries from OCT images is presented. The algorithms for lumen contours and stent strut detection from OCT as well as the reconstruction methods of stented geometries are discussed. Furthermore, the state of the art of studies that investigate the hemodynamics of OCT-based stented coronary artery geometries is reported. Although those studies analyzed few patient-specific cases, the application of the current reconstruction methods of stented geometries to large populations is possible. However, the improvement of these methods and the reduction of the time needed for the entire modeling process are crucial for a widespread clinical use of the OCT-based models and future in silico clinical trials.
Collapse
Affiliation(s)
- Claudio Chiastra
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy.
| | - Susanna Migliori
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Burzotta
- Institute of Cardiology, Catholic University of the Sacred Heart, Rome, Italy
| | - Gabriele Dubini
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| | - Francesco Migliavacca
- Laboratory of Biological Structure Mechanics (LaBS), Department of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milan, Italy
| |
Collapse
|
49
|
Grande Gutierrez N, Kahn A, Burns JC, Marsden AL. Computational blood flow simulations in Kawasaki disease patients: Insight into coronary artery aneurysm hemodynamics. Glob Cardiol Sci Pract 2017; 2017:e201729. [PMID: 29564350 PMCID: PMC5856960 DOI: 10.21542/gcsp.2017.29] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Noelia Grande Gutierrez
- Cardiovascular Biomechanics Computation Lab, Stanford University, Stanford CA 94305-5428, USA
| | - Andrew Kahn
- Cardiovascular Biomechanics Computation Lab, Stanford University, Stanford CA 94305-5428, USA
| | - Jane C Burns
- Cardiovascular Biomechanics Computation Lab, Stanford University, Stanford CA 94305-5428, USA
| | - Alison L Marsden
- Cardiovascular Biomechanics Computation Lab, Stanford University, Stanford CA 94305-5428, USA
| |
Collapse
|
50
|
Hariharan P, D’Souza GA, Horner M, Morrison TM, Malinauskas RA, Myers MR. Use of the FDA nozzle model to illustrate validation techniques in computational fluid dynamics (CFD) simulations. PLoS One 2017; 12:e0178749. [PMID: 28594889 PMCID: PMC5464577 DOI: 10.1371/journal.pone.0178749] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2016] [Accepted: 05/18/2017] [Indexed: 12/14/2022] Open
Abstract
A "credible" computational fluid dynamics (CFD) model has the potential to provide a meaningful evaluation of safety in medical devices. One major challenge in establishing "model credibility" is to determine the required degree of similarity between the model and experimental results for the model to be considered sufficiently validated. This study proposes a "threshold-based" validation approach that provides a well-defined acceptance criteria, which is a function of how close the simulation and experimental results are to the safety threshold, for establishing the model validity. The validation criteria developed following the threshold approach is not only a function of Comparison Error, E (which is the difference between experiments and simulations) but also takes in to account the risk to patient safety because of E. The method is applicable for scenarios in which a safety threshold can be clearly defined (e.g., the viscous shear-stress threshold for hemolysis in blood contacting devices). The applicability of the new validation approach was tested on the FDA nozzle geometry. The context of use (COU) was to evaluate if the instantaneous viscous shear stress in the nozzle geometry at Reynolds numbers (Re) of 3500 and 6500 was below the commonly accepted threshold for hemolysis. The CFD results ("S") of velocity and viscous shear stress were compared with inter-laboratory experimental measurements ("D"). The uncertainties in the CFD and experimental results due to input parameter uncertainties were quantified following the ASME V&V 20 standard. The CFD models for both Re = 3500 and 6500 could not be sufficiently validated by performing a direct comparison between CFD and experimental results using the Student's t-test. However, following the threshold-based approach, a Student's t-test comparing |S-D| and |Threshold-S| showed that relative to the threshold, the CFD and experimental datasets for Re = 3500 were statistically similar and the model could be considered sufficiently validated for the COU. However, for Re = 6500, at certain locations where the shear stress is close the hemolysis threshold, the CFD model could not be considered sufficiently validated for the COU. Our analysis showed that the model could be sufficiently validated either by reducing the uncertainties in experiments, simulations, and the threshold or by increasing the sample size for the experiments and simulations. The threshold approach can be applied to all types of computational models and provides an objective way of determining model credibility and for evaluating medical devices.
Collapse
Affiliation(s)
- Prasanna Hariharan
- US Food and Drug Administration, Silver Spring, Maryland, United States of America
- * E-mail:
| | - Gavin A. D’Souza
- US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | - Marc Horner
- ANSYS, Inc., Evanston, Illinois, United States of America
| | - Tina M. Morrison
- US Food and Drug Administration, Silver Spring, Maryland, United States of America
| | | | - Matthew R. Myers
- US Food and Drug Administration, Silver Spring, Maryland, United States of America
| |
Collapse
|