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Deus J, Martin E. Efficient Cardiovascular Parameters Estimation for Fluid-Structure Simulations Using Gappy Proper Orthogonal Decomposition. Ann Biomed Eng 2024:10.1007/s10439-024-03568-z. [PMID: 38969956 DOI: 10.1007/s10439-024-03568-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/21/2024] [Indexed: 07/07/2024]
Abstract
As full-scale detailed hemodynamic simulations of the entire vasculature are not feasible, numerical analysis should be focused on specific regions of the cardiovascular system, which requires the identification of lumped parameters to represent the patient behavior outside the simulated computational domain. We present a novel technique for estimating cardiovascular model parameters using gappy Proper Orthogonal Decomposition (g-POD). A POD basis is constructed with FSI simulations for different values of the lumped model parameters, and a linear operator is applied to retain information that can be compared to the available patient measurements. Then, the POD coefficients of the reconstructed solution are computed either by projecting patient measurements or by solving a minimization problem with constraints. The POD reconstruction is then used to estimate the model parameters. In the first test case, the parameter values of a 3-element Windkessel model are approximated using artificial patient measurements, obtaining a relative error of less than 4.2%. In the second case, 4 sets of 3-element Windkessel are approximated in a patient's aorta geometry, resulting in an error of less than 8% for the flow and less than 5% for the pressure. The method shows accurate results even with noisy patient data. It automatically calculates the delay between measurements and simulations and has flexibility in the types of patient measurements that can handle (at specific points, spatial or time averaged). The method is easy to implement and can be used in simulations performed in general-purpose FSI software.
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Affiliation(s)
- J Deus
- Departamento de Ingeniería Mecánica, Máquinas y Motores Térmicos y Fluidos, Universidade de Vigo, Campus Marcosende, 36310, Vigo, Spain
| | - E Martin
- Departamento de Ingeniería Mecánica, Máquinas y Motores Térmicos y Fluidos, Universidade de Vigo, Campus Marcosende, 36310, Vigo, Spain.
- Instituto de Física y Ciencias Aeroespaciales (IFCAE), Universidade de Vigo, Campus de As Lagoas, 32004, Ourense, Spain.
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2
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May RW, Maso Talou GD, Clark AR, Mynard JP, Smolich JJ, Blanco PJ, Müller LO, Gentles TL, Bloomfield FH, Safaei S. From fetus to neonate: A review of cardiovascular modeling in early life. WIREs Mech Dis 2023:e1608. [DOI: 10.1002/wsbm.1608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 01/31/2023] [Accepted: 03/03/2023] [Indexed: 04/03/2023]
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3
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Mair A, Wisotzki M, Bernhard S. Classification and regression of stenosis using an in-vitro pulse wave data set: Dependence on heart rate, waveform and location. Comput Biol Med 2022; 151:106224. [PMID: 36327886 DOI: 10.1016/j.compbiomed.2022.106224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 09/18/2022] [Accepted: 10/15/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Data-based approaches promise to use the information in cardiovascular signals to diagnose cardiovascular diseases. Considerable effort has been undertaken in the field of pulse-wave analysis to harness this information. However, the inverse problem, inferring arterial properties from waveform measurements, is not well understood today. Consequently, uncertainties within the estimation hinder the diagnostic application of such methods. METHOD This work contributes a publicly available data set measured at an in-vitro cardiovascular simulator, focusing on a set of input conditions (heart rate, waveform) and stenosis locations. Furthermore, a first attempt is undertaken to perform classification and regression on this data set using standard machine learning methods on features extracted from four peripheral pressure signals. RESULTS The locations of six different stenoses could be distinguished at high accuracy of 93%, where transfer function-based features outperformed features based solely on signal shape in almost all cases. Furthermore, regression on the stenosis position could be performed with a root mean square error of 2.4 cm along a 20 cm section of the arterial system using a shallow neural network. However, the performance difference between shape and transfer function features was not clear for this task. CONCLUSION The data set contains 800 measurements and allows investigating the influence of different heart boundary conditions, such as heart rate and waveform shape, on classification and regression tasks. Extracting features that minimise this influence is a promising way of improving the performance of these tasks.
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Affiliation(s)
- Alexander Mair
- Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany
| | - Michelle Wisotzki
- Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany
| | - Stefan Bernhard
- Technische Hochschule Mittelhessen, Department Life Science Engineering, Wiesenstrasse 14, 35390 Gießen, Germany; Freie Universität Berlin, Institute of Mathematics, Berlin, Germany.
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4
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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.
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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
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5
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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.
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Affiliation(s)
- David Nolte
- Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands
- Center for Mathematical ModelingUniversidad de ChileSantiagoChile
- Department of Fluid DynamicsTechnische Universität BerlinBerlinGermany
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Caforio F, Augustin CM, Alastruey J, Gsell MAF, Plank G. A coupling strategy for a first 3D-1D model of the cardiovascular system to study the effects of pulse wave propagation on cardiac function. COMPUTATIONAL MECHANICS 2022; 70:703-722. [PMID: 36124206 PMCID: PMC9477941 DOI: 10.1007/s00466-022-02206-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
A key factor governing the mechanical performance of the heart is the bidirectional coupling with the vascular system, where alterations in vascular properties modulate the pulsatile load imposed on the heart. Current models of cardiac electromechanics (EM) use simplified 0D representations of the vascular system when coupling to anatomically accurate 3D EM models is considered. However, these ignore important effects related to pulse wave transmission. Accounting for these effects requires 1D models, but a 3D-1D coupling remains challenging. In this work, we propose a novel, stable strategy to couple a 3D cardiac EM model to a 1D model of blood flow in the largest systemic arteries. For the first time, a personalised coupled 3D-1D model of left ventricle and arterial system is built and used in numerical benchmarks to demonstrate robustness and accuracy of our scheme over a range of time steps. Validation of the coupled model is performed by investigating the coupled system's physiological response to variations in the arterial system affecting pulse wave propagation, comprising aortic stiffening, aortic stenosis or bifurcations causing wave reflections. Our first 3D-1D coupled model is shown to be efficient and robust, with negligible additional computational costs compared to 3D-0D models. We further demonstrate that the calibrated 3D-1D model produces simulated data that match with clinical data under baseline conditions, and that known physiological responses to alterations in vascular resistance and stiffness are correctly replicated. Thus, using our coupled 3D-1D model will be beneficial in modelling studies investigating wave propagation phenomena.
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Affiliation(s)
- Federica Caforio
- Institute of Mathematics and Scientific Computing, NAWI Graz, University of Graz, Graz, Austria
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Christoph M. Augustin
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Jordi Alastruey
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, King’s Health Partners, St. Thomas’ Hospital, London, SE1 7EH UK
| | - Matthias A. F. Gsell
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
| | - Gernot Plank
- Gottfried Schatz Research Center: Division of Biophysics, Medical University of Graz, Graz, Austria
- BioTechMed-Graz, Graz, Austria
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7
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Zhou Y, He Y, Wu J, Cui C, Chen M, Sun B. A method of parameter estimation for cardiovascular hemodynamics based on deep learning and its application to personalize a reduced-order model. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2022; 38:e3533. [PMID: 34585523 DOI: 10.1002/cnm.3533] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/26/2021] [Indexed: 06/13/2023]
Abstract
Precise model personalization is a key step towards the application of cardiovascular physical models. In this manuscript, we propose to use deep learning (DL) to solve the parameter estimation problem in cardiovascular hemodynamics. Based on the convolutional neural network (CNN) and fully connected neural network (FCNN), a multi-input deep neural network (DNN) model is developed to map the nonlinear relationship between measurements and the parameters to be estimated. In this model, two separate network structures are designed to extract the features of two types of measurement data, including pressure waveforms and a vector composed of heart rate (HR) and pulse transit time (PTT), and a shared structure is used to extract their combined dependencies on the parameters. Besides, we try to use the transfer learning (TL) technology to further strengthen the personalized characteristics of a trained-well network. For assessing the proposed method, we conducted the parameter estimation using synthetic data and in vitro data respectively, and in the test with synthetic data, we evaluated the performance of the TL algorithm through two individuals with different characteristics. A series of estimation results show that the estimated parameters are in good agreement with the true values. Furthermore, it is also found that the estimation accuracy can be significantly improved by a multicycle combination strategy. Therefore, we think that the proposed method has the potential to be used for parameter estimation in cardiovascular hemodynamics, which can provide an immediate, accurate, and sustainable personalization process, and deserves more attention in the future.
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Affiliation(s)
- Yang Zhou
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Yuan He
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jianwei Wu
- School of Mechanical Engineering, Southeast University, Nanjing, China
| | - Chang Cui
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Minglong Chen
- Internal Medicine-Cardiovascular Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Beibei Sun
- School of Mechanical Engineering, Southeast University, Nanjing, China
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8
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Arthurs CJ, Xiao N, Moireau P, Schaeffter T, Figueroa CA. A flexible framework for sequential estimation of model parameters in computational hemodynamics. ADVANCED MODELING AND SIMULATION IN ENGINEERING SCIENCES 2020; 7:48. [PMID: 33282681 PMCID: PMC7717067 DOI: 10.1186/s40323-020-00186-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 11/06/2020] [Indexed: 06/02/2023]
Abstract
A major challenge in constructing three dimensional patient specific hemodynamic models is the calibration of model parameters to match patient data on flow, pressure, wall motion, etc. acquired in the clinic. Current workflows are manual and time-consuming. This work presents a flexible computational framework for model parameter estimation in cardiovascular flows that relies on the following fundamental contributions. (i) A Reduced-Order Unscented Kalman Filter (ROUKF) model for data assimilation for wall material and simple lumped parameter network (LPN) boundary condition model parameters. (ii) A constrained least squares augmentation (ROUKF-CLS) for more complex LPNs. (iii) A "Netlist" implementation, supporting easy filtering of parameters in such complex LPNs. The ROUKF algorithm is demonstrated using non-invasive patient-specific data on anatomy, flow and pressure from a healthy volunteer. The ROUKF-CLS algorithm is demonstrated using synthetic data on a coronary LPN. The methods described in this paper have been implemented as part of the CRIMSON hemodynamics software package.
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Affiliation(s)
| | - Nan Xiao
- Dept. of Biomedical Engineering, King’s College London, London, UK
| | - Philippe Moireau
- Inria, Inria Saclay-Ile de France, 91128 Palaiseau, France
- LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, 91128 Palaiseau, France
| | - Tobias Schaeffter
- Physikalisch-Technische Bundesanstalt, Berlin, Germany
- Technical University Berlin, Berlin, Germany
| | - C. Alberto Figueroa
- Depts. of Surgery and Biomedical Engineering, University of Michigan, 2800 Plymouth Rd, Ann Arbor, MI 48109 USA
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9
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Zhang X, Wu D, Miao F, Liu H, Li Y. Personalized Hemodynamic Modeling of the Human Cardiovascular System: A Reduced-Order Computing Model. IEEE Trans Biomed Eng 2020; 67:2754-2764. [PMID: 32142412 DOI: 10.1109/tbme.2020.2970244] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE Personalization of hemodynamic modeling plays a crucial role in functional prediction of the cardiovascular system (CVS). While reduced-order models of one-dimensional (1D) blood vessel models with zero-dimensional (0D) blood vessel and heart models have been widely recognized to be an effective tool for reasonably estimating the hemodynamic functions of the whole CVS, practical personalized models are still lacking. In this paper, we present a novel 0-1D coupled, personalized hemodynamic model of the CVS that can predict both pressure waveforms and flow velocities in arteries. METHODS We proposed a methodology by combining the multiscale CVS model with the Levenberg-Marquardt optimization algorithm for effectively solving an inverse problem based on measured blood pressure waveforms. Hemodynamic characteristics including brachial arterial pressure waveforms, artery diameters, stroke volumes, and flow velocities were measured noninvasively for 62 volunteers aged from 20 to 70 years for developing and validating the model. RESULTS The estimated arterial stiffness shows a physiologically realistic distribution. The model-fitted individual pressure waves have an averaged mean square error (MSE) of 7.1 mmHg2; simulated blood flow velocity waveforms in carotid artery match ultrasound measurements well, achieving an average correlation coefficient of 0.911. CONCLUSION The model is efficient, versatile, and capable of obtaining well-fitting individualized pressure waveforms while reasonably predicting flow waveforms. SIGNIFICANCE The proposed methodology of personalized hemodynamic modeling may therefore facilitate individualized patient-specific assessment of both physiological and pathological functions of the CVS.
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10
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Nolte D, Bertoglio C. Reducing the impact of geometric errors in flow computations using velocity measurements. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3203. [PMID: 30932361 PMCID: PMC6619346 DOI: 10.1002/cnm.3203] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/26/2019] [Accepted: 03/12/2019] [Indexed: 06/09/2023]
Abstract
Numerical blood flow simulations are typically set up from anatomical medical images and calibrated using velocity measurements. However, the accuracy of the computational geometry itself is limited by the resolution of the anatomical image. We first show that applying standard no-slip boundary conditions on inaccurately extracted boundaries can cause large errors in the results, in particular the pressure gradient. In this work, we therefore propose to augment the flow model calibration by slip/transpiration boundary conditions, whose parameters are then estimated using velocity measurements. Numerical experiments show that this methodology can considerably improve the accuracy of the estimated pressure gradients and 3D velocity fields when the vessel geometry is uncertain.
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Affiliation(s)
- David Nolte
- Bernoulli InstituteUniversity of GroningenGroningenNetherlands
- Center of Mathematical ModelingUniversity of ChileSantiagoChile
| | - Cristóbal Bertoglio
- Bernoulli InstituteUniversity of GroningenGroningenNetherlands
- Center of Mathematical ModelingUniversity of ChileSantiagoChile
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11
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Muller LO, Caiazzo A, Blanco PJ. Reduced-Order Unscented Kalman Filter With Observations in the Frequency Domain: Application to Computational Hemodynamics. IEEE Trans Biomed Eng 2019; 66:1269-1276. [DOI: 10.1109/tbme.2018.2872323] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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12
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Rapadamnaba R, Nicoud F, Mohammadi B. Backward sensitivity analysis and reduced-order covariance estimation in noninvasive parameter identification for cerebral arteries. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2019; 35:e3170. [PMID: 30426715 DOI: 10.1002/cnm.3170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/02/2018] [Accepted: 11/03/2018] [Indexed: 06/09/2023]
Abstract
Using a previously developed inversion platform for functional cerebral medical imaging with ensemble Kalman filters, this work analyzes the sensitivity of the results with respect to different parameters entering the physical model and inversion procedure, such as the inlet flow rate from the heart, the choice of the boundary conditions, and the nonsymmetry in the network terminations. It also proposes an alternative low complexity construction for the covariance matrix of the hemodynamic parameters of a network of arteries including the circle of Willis. The platform takes as input patient-specific blood flow rates extracted from magnetic resonance angiography and magnetic resonance imaging (dicom files) and is applied to several available patients data. The paper presents full analysis of the results for one of these patients, including a sensitivity study with respect to the proximal and distal boundary conditions. The results notably show that the uncertainties on the inlet flow rate led to uncertainties of the same order of magnitude in the estimated parameters (blood pressure and elastic parameters) and that three-lumped parameters boundary conditions are necessary for a correct retrieval of the target signals.
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Affiliation(s)
| | - Franck Nicoud
- IMAG, Université de Montpellier, CC051, Montpellier, France
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13
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Blanco PJ, Bulant CA, Müller LO, Talou GDM, Bezerra CG, Lemos PA, Feijóo RA. Comparison of 1D and 3D Models for the Estimation of Fractional Flow Reserve. Sci Rep 2018; 8:17275. [PMID: 30467321 PMCID: PMC6250665 DOI: 10.1038/s41598-018-35344-0] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 11/01/2018] [Indexed: 02/05/2023] Open
Abstract
In this work we propose to validate the predictive capabilities of one-dimensional (1D) blood flow models with full three-dimensional (3D) models in the context of patient-specific coronary hemodynamics in hyperemic conditions. Such conditions mimic the state of coronary circulation during the acquisition of the Fractional Flow Reserve (FFR) index. Demonstrating that 1D models accurately reproduce FFR estimates obtained with 3D models has implications in the approach to computationally estimate FFR. To this end, a sample of 20 patients was employed from which 29 3D geometries of arterial trees were constructed, 9 obtained from coronary computed tomography angiography (CCTA) and 20 from intra-vascular ultrasound (IVUS). For each 3D arterial model, a 1D counterpart was generated. The same outflow and inlet pressure boundary conditions were applied to both (3D and 1D) models. In the 1D setting, pressure losses at stenoses and bifurcations were accounted for through specific lumped models. Comparisons between 1D models (FFR1D) and 3D models (FFR3D) were performed in terms of predicted FFR value. Compared to FFR3D, FFR1D resulted with a difference of 0.00 ± 0.03 and overall predictive capability AUC, Acc, Spe, Sen, PPV and NPV of 0.97, 0.98, 0.90, 0.99, 0.82, and 0.99, with an FFR threshold of 0.8. We conclude that inexpensive FFR1D simulations can be reliably used as a surrogate of demanding FFR3D computations.
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Affiliation(s)
- P J Blanco
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil.
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil.
| | - C A Bulant
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - L O Müller
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - G D Maso Talou
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - C Guedes Bezerra
- Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, Sao Paulo, SP, 05403-904, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - P A Lemos
- Department of Interventional Cardiology, Heart Institute (InCor) and the University of São Paulo Medical School, Sao Paulo, SP, 05403-904, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
| | - R A Feijóo
- National Laboratory for Scientific Computing, LNCC/MCTIC, Av. Getúlio Vargas, 333, Petrópolis-RJ, 25651-075, Brazil
- INCT-MACC Instituto Nacional de Ciência e Tecnologia em Medicina Assistida por Computação Científica, Petrópolis, Brazil
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14
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Maso Talou GD, Blanco PJ, Ares GD, Guedes Bezerra C, Lemos PA, Feijóo RA. Mechanical Characterization of the Vessel Wall by Data Assimilation of Intravascular Ultrasound Studies. Front Physiol 2018; 9:292. [PMID: 29643815 PMCID: PMC5882902 DOI: 10.3389/fphys.2018.00292] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2017] [Accepted: 03/12/2018] [Indexed: 12/24/2022] Open
Abstract
Atherosclerotic plaque rupture and erosion are the most important mechanisms underlying the sudden plaque growth, responsible for acute coronary syndromes and even fatal cardiac events. Advances in the understanding of the culprit plaque structure and composition are already reported in the literature, however, there is still much work to be done toward in-vivo plaque visualization and mechanical characterization to assess plaque stability, patient risk, diagnosis and treatment prognosis. In this work, a methodology for the mechanical characterization of the vessel wall plaque and tissues is proposed based on the combination of intravascular ultrasound (IVUS) imaging processing, data assimilation and continuum mechanics models within a high performance computing (HPC) environment. Initially, the IVUS study is gated to obtain volumes of image sequences corresponding to the vessel of interest at different cardiac phases. These sequences are registered against the sequence of the end-diastolic phase to remove transversal and longitudinal rigid motions prescribed by the moving environment due to the heartbeat. Then, optical flow between the image sequences is computed to obtain the displacement fields of the vessel (each associated to a certain pressure level). The obtained displacement fields are regarded as observations within a data assimilation paradigm, which aims to estimate the material parameters of the tissues within the vessel wall. Specifically, a reduced order unscented Kalman filter is employed, endowed with a forward operator which amounts to address the solution of a hyperelastic solid mechanics model in the finite strain regime taking into account the axially stretched state of the vessel, as well as the effect of internal and external forces acting on the arterial wall. Due to the computational burden, a HPC approach is mandatory. Hence, the data assimilation and computational solid mechanics computations are parallelized at three levels: (i) a Kalman filter level; (ii) a cardiac phase level; and (iii) a mesh partitioning level. To illustrate the capabilities of this novel methodology toward the in-vivo analysis of patient-specific vessel constituents, mechanical material parameters are estimated using in-silico and in-vivo data retrieved from IVUS studies. Limitations and potentials of this approach are exposed and discussed.
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Affiliation(s)
- Gonzalo D Maso Talou
- National Laboratory for Scientific Computing, Department of Mathematical and Computational Methods, Petrópolis, Brazil.,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil
| | - Pablo J Blanco
- National Laboratory for Scientific Computing, Department of Mathematical and Computational Methods, Petrópolis, Brazil.,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil
| | - Gonzalo D Ares
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil.,National Scientific and Technical Research Council, Buenos Aires, Argentina.,CAE Group, National University of Mar del Plata, Mar del Plata, Argentina
| | - Cristiano Guedes Bezerra
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil.,Department of Interventional Cardiology, Heart Institute (Incor), São Paulo, Brazil
| | - Pedro A Lemos
- National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil.,Department of Interventional Cardiology, Heart Institute (Incor), São Paulo, Brazil
| | - Raúl A Feijóo
- National Laboratory for Scientific Computing, Department of Mathematical and Computational Methods, Petrópolis, Brazil.,National Institute of Science and Technology in Medicine Assisted by Scientific Computing, São Paulo, Brazil
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15
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Zhang D, Han X, Zhang Z, Liu J, Jiang C, Yoda N, Meng X, Li Q. Identification of dynamic load for prosthetic structures. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2017; 33. [PMID: 28425209 DOI: 10.1002/cnm.2889] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 04/15/2017] [Indexed: 06/07/2023]
Abstract
Dynamic load exists in numerous biomechanical systems, and its identification signifies a critical issue for characterizing dynamic behaviors and studying biomechanical consequence of the systems. This study aims to identify dynamic load in the dental prosthetic structures, namely, 3-unit implant-supported fixed partial denture (I-FPD) and teeth-supported fixed partial denture. The 3-dimensional finite element models were constructed through specific patient's computerized tomography images. A forward algorithm and regularization technique were developed for identifying dynamic load. To verify the effectiveness of the identification method proposed, the I-FPD and teeth-supported fixed partial denture structures were investigated to determine the dynamic loads. For validating the results of inverse identification, an experimental force-measuring system was developed by using a 3-dimensional piezoelectric transducer to measure the dynamic load in the I-FPD structure in vivo. The computationally identified loads were presented with different noise levels to determine their influence on the identification accuracy. The errors between the measured load and identified counterpart were calculated for evaluating the practical applicability of the proposed procedure in biomechanical engineering. This study is expected to serve as a demonstrative role in identifying dynamic loading in biomedical systems, where a direct in vivo measurement may be rather demanding in some areas of interest clinically.
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Affiliation(s)
- Dequan Zhang
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, 2006, Australia
| | - Xu Han
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
| | - Zhongpu Zhang
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, 2006, Australia
| | - Jie Liu
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
| | - Chao Jiang
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
| | - Nobuhiro Yoda
- Division of Advanced Prosthetic Dentistry, Tohoku University Graduate School of Dentistry, 4-1 Seiryo-machi, Aoba-ku, Sendai, 980-8575, Japan
| | - Xianghua Meng
- State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, China
| | - Qing Li
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales, 2006, Australia
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16
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Lal R, Nicoud F, Bars EL, Deverdun J, Molino F, Costalat V, Mohammadi B. Non Invasive Blood Flow Features Estimation in Cerebral Arteries from Uncertain Medical Data. Ann Biomed Eng 2017; 45:2574-2591. [DOI: 10.1007/s10439-017-1904-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2016] [Accepted: 08/12/2017] [Indexed: 11/30/2022]
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