scholarly journals Inverse problems in reduced order models of cardiovascular haemodynamics: aspects of data assimilation and heart rate variability

2017 ◽  
Vol 14 (126) ◽  
pp. 20160513 ◽  
Author(s):  
Sanjay Pant ◽  
Chiara Corsini ◽  
Catriona Baker ◽  
Tain-Yen Hsia ◽  
Giancarlo Pennati ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Inverse Problems ◽  
Unscented Kalman Filter ◽  
Clinical Situation ◽  
Specific Model ◽  
Patient Specific ◽  
Model Parameters ◽  
Heart Rates ◽  
Model Predictions ◽  
Patient State

Inverse problems in cardiovascular modelling have become increasingly important to assess each patient individually. These problems entail estimation of patient-specific model parameters from uncertain measurements acquired in the clinic. In recent years, the method of data assimilation, especially the unscented Kalman filter, has gained popularity to address computational efficiency and uncertainty consideration in such problems. This work highlights and presents solutions to several challenges of this method pertinent to models of cardiovascular haemodynamics. These include methods to (i) avoid ill-conditioning of the covariance matrix, (ii) handle a variety of measurement types, (iii) include a variety of prior knowledge in the method, and (iv) incorporate measurements acquired at different heart rates, a common situation in the clinic where the patient state differs according to the clinical situation. Results are presented for two patient-specific cases of congenital heart disease. To illustrate and validate data assimilation with measurements at different heart rates, the results are presented on a synthetic dataset and on a patient-specific case with heart valve regurgitation. It is shown that the new method significantly improves the agreement between model predictions and measurements. The developed methods can be readily applied to other pathophysiologies and extended to dynamical systems which exhibit different responses under different sets of known parameters or different sets of inputs (such as forcing/excitation frequencies).

scholarly journals A flexible framework for sequential estimation of model parameters in computational hemodynamics

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Christopher J. Arthurs ◽  
Nan Xiao ◽  
Philippe Moireau ◽  
Tobias Schaeffter ◽  
C. Alberto Figueroa
Keyword(s):  
Unscented Kalman Filter ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Sequential Estimation ◽  
Wall Material ◽  
Patient Specific ◽  
Model Parameters ◽  
Computational Framework ◽  
Lumped Parameter ◽  
Estimation Of Model Parameters

AbstractA 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.

scholarly journals Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. Khaki ◽  
H.-J. Hendricks Franssen ◽  
S. C. Han
Keyword(s):  
Remote Sensing ◽  
Parameter Estimation ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Remote Sensing ◽  
Model Parameters ◽  
Parameter Uncertainties ◽  
Area Index ◽  
Mission Satellite ◽  
Model Predictions

Abstract Satellite remote sensing offers valuable tools to study Earth and hydrological processes and improve land surface models. This is essential to improve the quality of model predictions, which are affected by various factors such as erroneous input data, the uncertainty of model forcings, and parameter uncertainties. Abundant datasets from multi-mission satellite remote sensing during recent years have provided an opportunity to improve not only the model estimates but also model parameters through a parameter estimation process. This study utilises multiple datasets from satellite remote sensing including soil moisture from Soil Moisture and Ocean Salinity Mission and Advanced Microwave Scanning Radiometer Earth Observing System, terrestrial water storage from the Gravity Recovery And Climate Experiment, and leaf area index from Advanced Very-High-Resolution Radiometer to estimate model parameters. This is done using the recently proposed assimilation method, unsupervised weak constrained ensemble Kalman filter (UWCEnKF). UWCEnKF applies a dual scheme to separately update the state and parameters using two interactive EnKF filters followed by a water balance constraint enforcement. The performance of multivariate data assimilation is evaluated against various independent data over different time periods over two different basins including the Murray–Darling and Mississippi basins. Results indicate that simultaneous assimilation of multiple satellite products combined with parameter estimation strongly improves model predictions compared with single satellite products and/or state estimation alone. This improvement is achieved not only during the parameter estimation period ($$\sim $$ ∼  32% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.66 to $$\sim $$ ∼  0.85) but also during the forecast period ($$\sim $$ ∼  14% groundwater RMSE reduction and soil moisture correlation increase from $$\sim $$ ∼  0.69 to $$\sim $$ ∼  0.78) due to the effective impacts of the approach on both state and parameters.

A physiological model for predicting carboxyhemoglobin formation from exposure to carbon monoxide in rats

1999 ◽  
Vol 86 (6) ◽  
pp. 1977-1983 ◽  
Author(s):  
Edgar C. Kimmel ◽  
Robert L. Carpenter ◽  
James E. Reboulet ◽  
Kenneth R. Still
Keyword(s):  
General Model ◽  
Minute Ventilation ◽  
Blood Gas Analysis ◽  
Physiological Model ◽  
Specific Model ◽  
Gas Analysis ◽  
Model Parameters ◽  
Total Blood ◽  
Continuous Simulation ◽  
Model Predictions

A time-dependent simulation model, based on the Coburn-Forster-Kane equation, was written in Advanced Continuous Simulation Language to predict carboxyhemoglobin (HbCO) formation and dissociation in F-344 rats during and after exposure to 500 parts/million CO for 1 h. Blood-gas analysis and CO-oximetry were performed on samples collected during exposure and off-gassing of CO. Volume displacement plethysmography was used to measure minute ventilation (V˙e) during exposure. CO diffusing capacity in the lung (Dl CO) was also measured. Other model parameters measured in the animals included blood pH, total blood volume, and Hb concentration. Comparisons between model predictions using values forV˙e, Dl CO, and the Haldane coefficient cited in the literature and predictions using measured V˙e, Dl CO, and calculated Haldane coefficient for individual animals were made. General model predictions using values for model parameters derived from the literature agreed with published HbCO values by a factor of 0.987 but failed to simulate experimental data. On average, the general model overpredicted measured HbCO level by nearly 9%. A specific model using the means of measured variables predicted HbCO concentration within a factor of 0.993. When experimentally observed parameter fluctuations were included, the specific model predictions reflected experimental effects on HbCO formation.

scholarly journals A Bayesian approach for the identification of patient-specific parameters in a dialysis kinetic model

2018 ◽  
Vol 28 (7) ◽  
pp. 2069-2095
Author(s):  
Camilla Bianchi ◽  
Ettore Lanzarone ◽  
Giustina Casagrande ◽  
Maria Laura Costantino
Keyword(s):  
Bayesian Approach ◽  
Likelihood Function ◽  
Compartment Model ◽  
Specific Model ◽  
Patient Specific ◽  
Model Parameters ◽  
Uremic Patient ◽  
Blood Electrolytes ◽  
Runge Kutta Method ◽  
Dialysis Complications

Hemodialysis is the most common therapy to treat renal insufficiency. However, notwithstanding the recent improvements, hemodialysis is still associated with a non-negligible rate of comorbidities, which could be reduced by customizing the treatment. Many differential compartment models have been developed to describe the mass balance of blood electrolytes and catabolites during hemodialysis, with the goal of improving and controlling hemodialysis sessions. However, these models often refer to an average uremic patient, while on the contrary the clinical need for customization requires patient-specific models. In this work, we assume that the customization can be obtained by means of patient-specific model parameters. We propose and validate a Bayesian approach to estimate the patient-specific parameters of a multi-compartment model, and to predict the single patient’s response to the treatment, in order to prevent intra-dialysis complications. The likelihood function is obtained by means of a discretized version of the multi-compartment model, where the discretization is in terms of a Runge–Kutta method to guarantee convergence, and the posterior densities of model parameters are obtained through Markov Chain Monte Carlo simulation. Results show fair estimations and the applicability in the clinical practice.

scholarly journals Prior Distributions of Material Parameters for Bayesian Calibration of Growth and Remodeling Computational Model of Abdominal Aortic Wall

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Sajjad Seyedsalehi ◽  
Liangliang Zhang ◽  
Jongeun Choi ◽  
Seungik Baek
Keyword(s):  
Computational Model ◽  
Prior Distribution ◽  
Specific Model ◽  
Patient Specific ◽  
Model Parameters ◽  
Bayesian Calibration ◽  
Prior Distributions ◽  
Group I ◽  
Biaxial Tests ◽  
Group Ii

For the accurate prediction of the vascular disease progression, there is a crucial need for developing a systematic tool aimed toward patient-specific modeling. Considering the interpatient variations, a prior distribution of model parameters has a strong influence on computational results for arterial mechanics. One crucial step toward patient-specific computational modeling is to identify parameters of prior distributions that reflect existing knowledge. In this paper, we present a new systematic method to estimate the prior distribution for the parameters of a constrained mixture model using previous biaxial tests of healthy abdominal aortas (AAs). We investigate the correlation between the estimated parameters for each constituent and the patient's age and gender; however, the results indicate that the parameters are correlated with age only. The parameters are classified into two groups: Group-I in which the parameters ce, ck1, ck2, cm2,Ghc, and ϕe are correlated with age, and Group-II in which the parameters cm1, Ghm, G1e, G2e, and α are not correlated with age. For the parameters in Group-I, we used regression associated with age via linear or inverse relations, in which their prior distributions provide conditional distributions with confidence intervals. For Group-II, the parameter estimated values were subjected to multiple transformations and chosen if the transformed data had a better fit to the normal distribution than the original. This information improves the prior distribution of a subject-specific model by specifying parameters that are correlated with age and their transformed distributions. Therefore, this study is a necessary first step in our group's approach toward a Bayesian calibration of an aortic model. The results from this study will be used as the prior information necessary for the initialization of Bayesian calibration of a computational model for future applications.

Updating the soil hydraulic parameters in a 3D subsurface model

2021 ◽  
Author(s):  
Natascha Brandhorst ◽  
Insa Neuweiler
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Parameter Uncertainty ◽  
Unsaturated Flow ◽  
Model Parameters ◽  
Hydraulic Parameters ◽  
Model Errors ◽  
Model Predictions ◽  
Soil Hydraulic Parameters ◽  
Soil Hydraulic

<p>Soil moisture is an important variable for land surface processes. To make good model predictions of soil moisture, the flow processes in the subsurface need to be captured well. Flow in the subsurface strongly depends on the soil hydraulic parameters. Information about model parameters is often not available, at least not for the entire domain of interest. The resulting parameter uncertainty needs to be accounted for in the applied model. Data assimilation can account for parameter and model errors as well as for all other possible sources of uncertainty if observations are available that can be used to condition the model states. Thus, the parameter uncertainty might be reduced and model predictions improved. However, including the parameters increases the size of the state vector and thus the computational burden. Especially for large models, this can be a problem. Furthermore, the updates can produce unphysical parameter combinations which in unsaturated zone models often lead to numerical problems.</p><p>In this work, we test the effect of updating the soil hydraulic parameters along with soil moisture in a 3D subsurface hillslope model. We use the ensemble Kalman filter for data assimilation and synthetic observations of soil moisture. In a similar study using a 1D unsaturated flow model, parameter updates were found to be the best way to handle parameter uncertainty. Updating parameters resulted in improved predictions of soil moisture, although not necessarily in more realistic model parameters. The parameter updates should rather be considered a method of treating parameter uncertainty than a method for parameter identification. In the 1D settings, updating all uncertain parameters led to the best results. Whether this still holds and is feasible for a more complex 3D model is the question addressed in this presentation.</p>

scholarly journals Coupled personalization of cardiac electrophysiology models for prediction of ischaemic ventricular tachycardia

2011 ◽  
Vol 1 (3) ◽  
pp. 396-407 ◽  
Author(s):  
Jatin Relan ◽  
Phani Chinchapatnam ◽  
Maxime Sermesant ◽  
Kawal Rhode ◽  
Matt Ginks ◽  
...  
Keyword(s):  
Ventricular Tachycardia ◽  
Cardiac Electrophysiology ◽  
Specific Model ◽  
Biophysical Model ◽  
Patient Specific ◽  
Model Parameters ◽  
Tissue Conductivity ◽  
Mapping Procedure ◽  
Macroscopic Models ◽  
Contact Mapping

In order to translate the important progress in cardiac electrophysiology modelling of the last decades into clinical applications, there is a requirement to make macroscopic models that can be used for the planning and performance of the clinical procedures. This requires model personalization, i.e. estimation of patient-specific model parameters and computations compatible with clinical constraints. Simplified macroscopic models can allow a rapid estimation of the tissue conductivity, but are often unreliable to predict arrhythmias. Conversely, complex biophysical models are more complete and have mechanisms of arrhythmogenesis and arrhythmia sustainibility, but are computationally expensive and their predictions at the organ scale still have to be validated. We present a coupled personalization framework that combines the power of the two kinds of models while keeping the computational complexity tractable. A simple eikonal model is used to estimate the conductivity parameters, which are then used to set the parameters of a biophysical model, the Mitchell–Schaeffer (MS) model. Additional parameters related to action potential duration restitution curves for the tissue are further estimated for the MS model. This framework is applied to a clinical dataset derived from a hybrid X-ray/magnetic resonance imaging and non-contact mapping procedure on a patient with heart failure. This personalized MS model is then used to perform an in silico simulation of a ventricular tachycardia (VT) stimulation protocol to predict the induction of VT. This proof of concept opens up possibilities of using VT induction modelling in order to both assess the risk of VT for a given patient and also to plan a potential subsequent radio-frequency ablation strategy to treat VT.

scholarly journals Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

scholarly journals Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Gaoyang Li ◽  
Haoran Wang ◽  
Mingzi Zhang ◽  
Simon Tupin ◽  
Aike Qiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Artery Bypass ◽  
Computational Cost ◽  
Specific Model ◽  
Arterial System ◽  
Patient Specific ◽  
Cardiovascular Hemodynamics ◽  
Before And After ◽  
Deep Learning Network ◽  
Cfd Method

AbstractThe clinical treatment planning of coronary heart disease requires hemodynamic parameters to provide proper guidance. Computational fluid dynamics (CFD) is gradually used in the simulation of cardiovascular hemodynamics. However, for the patient-specific model, the complex operation and high computational cost of CFD hinder its clinical application. To deal with these problems, we develop cardiovascular hemodynamic point datasets and a dual sampling channel deep learning network, which can analyze and reproduce the relationship between the cardiovascular geometry and internal hemodynamics. The statistical analysis shows that the hemodynamic prediction results of deep learning are in agreement with the conventional CFD method, but the calculation time is reduced 600-fold. In terms of over 2 million nodes, prediction accuracy of around 90%, computational efficiency to predict cardiovascular hemodynamics within 1 second, and universality for evaluating complex arterial system, our deep learning method can meet the needs of most situations.

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