Backward sensitivity analysis and reduced-order covariance estimation in noninvasive parameter identification for cerebral arteries

Autor: Bijan Mohammadi, Franck Nicoud, Robert Rapadamnaba
Přispěvatelé: Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)
Rok vydání: 2018
Předmět:
Computer science
uncertainty quantification
Quantitative Biology::Tissues and Organs
0206 medical engineering
Physics::Medical Physics
Biomedical Engineering
reduced order compartment blood model
02 engineering and technology
030204 cardiovascular system & hematology
ensemble Kalman filter
03 medical and health sciences
Estimation of covariance matrices
0302 clinical medicine
Elastic Modulus
Medical imaging
Pressure
Humans
Boundary value problem
Uncertainty quantification
Molecular Biology
covariance matrix
sensitivity analysis
Estimation theory
Covariance matrix
Applied Mathematics
Models
Cardiovascular
Uncertainty
Kalman filter
Cerebral Arteries
020601 biomedical engineering
[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Magnetic Resonance Imaging
Computational Theory and Mathematics
Modeling and Simulation
Ensemble Kalman filter
Vascular Resistance
parameter estimation
Algorithm
Software
Blood Flow Velocity
Zdroj: International Journal for Numerical Methods in Biomedical Engineering
International Journal for Numerical Methods in Biomedical Engineering, John Wiley and Sons, 2018, pp.1-24. ⟨10.1002/cnm.3170⟩
ISSN: 2040-7947
2040-7939
DOI: 10.1002/cnm.3170⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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