Personalized blood flow computations: A hierarchical parameter estimation framework for tuning boundary conditions

Itu, Lucian Mihai; Sharma, Puneet; Suciu, Constantin; Moldoveanu, Florica; Comanicìu, Dorin

doi:10.1002/cnm.2803

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…While these works substantiate the potential of this approach, theory-based nICP methods still require significant development both in terms of modeling the physiology and effective assimilation of data. Data assimilation (DA) is emerging in other areas of biomechanics modeling 28 , and has recently included the use of variational-based methods 39,21 , unscented Kalman filtering 5,29,31 , and most recently ensemble Kalman filtering (EnKF) 2,25 .…”

Section: Introductionmentioning

confidence: 99%

Data-Augmented Modeling of Intracranial Pressure

Wang

Shadden

2019

Ann Biomed Eng

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Precise management of patients with cerebral diseases often requires intracranial pressure (ICP) monitoring, which is highly invasive and requires a specialized ICU setting. The ability to noninvasively estimate ICP is highly compelling as an alternative to, or screening for, invasive ICP measurement. Most existing approaches for noninvasive ICP estimation aim to build a regression function that maps noninvasive measurements to an ICP estimate using statistical learning techniques. These * data-based approaches have met limited success, likely because the amount of training data needed is onerous for this complex applications. In this work, we discuss an alternative strategy that aims to better utilize noninvasive measurement data by leveraging mechanistic understanding of physiology. Specifically, we developed a Bayesian framework that combines a multiscale model of intracranial physiology with noninvasive measurements of cerebral blood flow using transcranial Doppler. Virtual experiments with synthetic data are conducted to verify and analyze the proposed framework. A preliminary clinical application study on two patients is also performed in which we demonstrate the ability of this method to improve ICP prediction.

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Section: Introductionmentioning

confidence: 99%

Data-Augmented Modeling of Intracranial Pressure

Wang

Shadden

2019

Ann Biomed Eng

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“…The estimated aortic pressures and flow rate distributions between supra-aortic branches and descending aorta were matched with an error of less than 1%. We used three-element Windkessel models as outlet boundary conditions, but other types of physiologically sound boundary conditions, like the structured tree boundary condition, may be employed instead [31]. The cost function related to the estimation of regional mechanical wall properties, computed based on the differences between the cross-sectional area variations in the 4D MRI data and the blood flow model, was minimized to an average value of 0.23 cm 2 for the entire cohort.…”

Section: Discussionmentioning

confidence: 99%

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

et al. 2017

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We introduce a parameter estimation framework for automatically and robustly personalizing aortic haemodynamic computations from four-dimensional magnetic resonance imaging data. The framework is based on a reduced-order multiscale fluid-structure interaction blood flow model, and on two calibration procedures. First, Windkessel parameters of the outlet boundary conditions are personalized by solving a system of nonlinear equations. Second, the regional mechanical wall properties of the aorta are personalized by employing a nonlinear least-squares minimization method. The two calibration procedures are run sequentially and iteratively until both procedures have converged. The parameter estimation framework was successfully evaluated on 15 datasets from patients with aortic valve disease. On average, only 1.27 ± 0.96 and 7.07 ± 1.44 iterations were required to personalize the outlet boundary conditions and the regional mechanical wall properties, respectively. Overall, the computational model was in close agreement with the clinical measurements used as objectives (pressures, flow rates, cross-sectional areas), with a maximum error of less than 1%. Given its level of automation, robustness and the short execution time (6.2 ± 1.2 min on a standard hardware configuration), the framework is potentially well suited for a clinical setting.

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“…Algorithmic improvements to Spilker and Taylor, 126 addressing the issue of the initial parameters, were introduced by Itu et al, 129,130 but only applied in a reduced-order 1D/0D multiscale approach.…”

Section: Root-findingmentioning

confidence: 99%

Inverse problems in blood flow modeling: A review

Nolte

Bertoglio

2022

Numer Methods Biomed Eng

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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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Personalized blood flow computations: A hierarchical parameter estimation framework for tuning boundary conditions

Cited by 12 publications

References 49 publications

Data-Augmented Modeling of Intracranial Pressure

Data-Augmented Modeling of Intracranial Pressure

Non-invasive assessment of patient-specific aortic haemodynamics from four-dimensional flow MRI data

Inverse problems in blood flow modeling: A review

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