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.

3D modeling and inversion of VLF and VLF-R electromagnetic data

Geophysics ◽  
2012 ◽  
Vol 77 (4) ◽  
pp. WB219-WB231 ◽  
Author(s):  
P. Kaikkonen ◽  
S. P. Sharma ◽  
S. Mittal
Keyword(s):  
Field Data ◽  
Three Dimensional ◽  
Synthetic Data ◽  
Low Frequency ◽  
Reliability And Validity ◽  
Physical Parameters ◽  
Model Parameters ◽  
Data Inversion ◽  
Inversion Procedure ◽  
Using Data

Three-dimensional linearized nonlinear electromagnetic inversion is developed for revealing the subsurface conductivity structure using isolated very low frequency (VLF) and VLF-resistivity anomalies due to conductors that may be arbitrarily directed towards the measuring profiles and the VLF transmitter. We described the 3D model using a set of variables in terms of geometric and physical parameters. These model parameters were then optimized (parametric inversion) to obtain their best estimates to fit the observations. Two VLF transmitters, i.e., the [Formula: see text], [Formula: see text] (“E”) and the [Formula: see text], [Formula: see text] (“H”) polarizations, respectively, can be considered jointly in inversion. After inverting several noise-free and noisy synthetic data, the results revealed that the estimated model parameters and the functionality of the approach were very good and reliable. The inversion procedure also worked well for the field data. The reliability and validity of the results after the field data inversion have been checked using data from a shear zone associated with uranium mineralization.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2017 ◽  
pp. 76-99 ◽  
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

scholarly journals Virtual surgeries in patients with congenital heart disease: a multi-scale modelling test case

2011 ◽  
Vol 369 (1954) ◽  
pp. 4316-4330 ◽  
Author(s):  
A. Baretta ◽  
C. Corsini ◽  
W. Yang ◽  
I. E. Vignon-Clementel ◽  
A. L. Marsden ◽  
...  
Keyword(s):  
Boundary Conditions ◽  
Congenital Heart ◽  
Closed Loop ◽  
Three Dimensional ◽  
Scale Model ◽  
Patient Specific ◽  
Loop Model ◽  
Multi Scale ◽  
Venae Cavae ◽  
Lumped Parameter

The objective of this work is to perform a virtual planning of surgical repairs in patients with congenital heart diseases—to test the predictive capability of a closed-loop multi-scale model. As a first step, we reproduced the pre-operative state of a specific patient with a univentricular circulation and a bidirectional cavopulmonary anastomosis (BCPA), starting from the patient's clinical data. Namely, by adopting a closed-loop multi-scale approach, the boundary conditions at the inlet and outlet sections of the three-dimensional model were automatically calculated by a lumped parameter network. Successively, we simulated three alternative surgical designs of the total cavopulmonary connection (TCPC). In particular, a T-junction of the venae cavae to the pulmonary arteries (T-TCPC), a design with an offset between the venae cavae (O-TCPC) and a Y-graft design (Y-TCPC) were compared. A multi-scale closed-loop model consisting of a lumped parameter network representing the whole circulation and a patient-specific three-dimensional finite volume model of the BCPA with detailed pulmonary anatomy was built. The three TCPC alternatives were investigated in terms of energetics and haemodynamics. Effects of exercise were also investigated. Results showed that the pre-operative caval flows should not be used as boundary conditions in post-operative simulations owing to changes in the flow waveforms post-operatively. The multi-scale approach is a possible solution to overcome this incongruence. Power losses of the Y-TCPC were lower than all other TCPC models both at rest and under exercise conditions and it distributed the inferior vena cava flow evenly to both lungs. Further work is needed to correlate results from these simulations with clinical outcomes.

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 Data-driven prediction and origin identification of epidemics in population networks

2021 ◽  
Vol 8 (1) ◽  
pp. 200531
Author(s):  
Karen Larson ◽  
Georgios Arampatzis ◽  
Clark Bowman ◽  
Zhizhong Chen ◽  
Panagiotis Hadjidoukas ◽  
...  
Keyword(s):  
Model Fitting ◽  
Synthetic Data ◽  
Model Complexity ◽  
Model Parameters ◽  
Computational Framework ◽  
Related Data ◽  
Robust Model ◽  
Massively Parallel Computing ◽  
Disease Spreading ◽  
Origin Identification

Effective intervention strategies for epidemics rely on the identification of their origin and on the robustness of the predictions made by network disease models. We introduce a Bayesian uncertainty quantification framework to infer model parameters for a disease spreading on a network of communities from limited, noisy observations; the state-of-the-art computational framework compensates for the model complexity by exploiting massively parallel computing architectures. Using noisy, synthetic data, we show the potential of the approach to perform robust model fitting and additionally demonstrate that we can effectively identify the disease origin via Bayesian model selection. As disease-related data are increasingly available, the proposed framework has broad practical relevance for the prediction and management of epidemics.

Parameter Estimation of Nonlinear Biomedical Systems Using Extended Kalman Filter Algorithm

2018 ◽  
pp. 690-713
Author(s):  
Kamalanand Krishnamurthy
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Computational Models ◽  
Three Dimensional ◽  
Model Parameters ◽  
State Variables ◽  
Three Dimensional Model ◽  
Biomedical Systems ◽  
Estimation Of Model Parameters

Parameter estimation is a central issue in mathematical modelling of biomedical systems and for the development of patient specific models. The technique of estimating parameters helps in obtaining diagnostic information from computational models of biological systems. However, in most of the biomedical systems, the estimation of model parameters is a challenging task due to the nonlinearity of mathematical models. In this chapter, the method of estimation of nonlinear model parameters from measurements of state variables, using the extended Kalman filter, is extensively explained using an example of the three-dimensional model of the HIV/AIDS system.

COMPUTATIONAL MODEL OF RECIRCULATING LYMPHOCYTES

2015 ◽  
Vol 23 (01) ◽  
pp. 93-114 ◽  
Author(s):  
Alireza Naeimifard ◽  
Ali Ghaffari
Keyword(s):  
Computational Model ◽  
Human Subjects ◽  
Model Parameters ◽  
Computational Framework ◽  
Driven Systems ◽  
Disease Outcomes ◽  
Health And Disease ◽  
Lymphocyte Recirculation ◽  
Estimation Of Model Parameters ◽  
Using Data

Lymphocyte recirculation plays an important role in controlling the spread of both pathogenic infections and tumor-producing cancer cells in the human body. We present a mathematical and computational framework that allows investigation of recirculating lymphocytes and estimation of model parameters using a genetic algorithm. The framework allows estimating parameters using data obtained from experiments performed in laboratory studies of rats as well as clinical studies of human subjects. Our computational model allows improved understanding of these data. Mathematical models enable investigators to obtain a quantitative picture of immune system kinetics and diversity in human health and disease outcomes. Our data-driven systems biology and immunological modeling approach contributes to a growing understanding of the dynamics of lymphocyte recirculation.

Tuning of Computer Model Parameters in Managed-Pressure Drilling Applications Using an Unscented-Kalman-Filter Technique

SPE Journal ◽  
2010 ◽  
Vol 15 (03) ◽  
pp. 856-866 ◽  
Author(s):  
Jan Einar Gravdal ◽  
Rolf J. Lorentzen ◽  
Kjell K. Fjelde ◽  
Erlend H. Vefring
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Synthetic Data ◽  
Pressure Profile ◽  
Model Parameters ◽  
High Pressure High Temperature ◽  
Managed Pressure Drilling ◽  
High Degree ◽  
Advanced Computer

Summary To manage the annular pressure profile during managed-pressure drilling (MPD) operations, simulations performed with advanced computer models are needed. To obtain a high degree of accuracy in these simulations, it is crucial that all parameters describing the system are as correct as possible. A new methodology for real-time updating of key parameters in a well-flow model by taking into account real-time measurements, including measuring uncertainty, is presented. Key model parameters are tuned using a recently developed estimation technique based on the traditional Kalman filter. The presented methodology leads to a more-accurate prediction of well-flow scenarios. Although the present study is motivated by applications in MPD, the idea of tuning model parameters should be of great importance in a wide area of applications. The performance of the filter is studied, using both synthetic data and real measurements from a North Sea high-pressure/high-temperature (HP/HT) drilling operation. Benefits by this approach are seen in more-accurate downhole-pressure predictions, which are of major importance for safety and economic reasons during MPD operations.

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