Non-Invasive Hemodynamic Assessment of Aortic Coarctation: Validation with In Vivo Measurements

Itu, Lucian Mihai; Sharma, Puneet; Ralovich, Kristóf; Mihalef, Viorel; Ionasec, Razvan Ioan; Everett, Allen D.; Ringel, Richard; Kamen, Ali; Comanicìu, Dorin

doi:10.1007/s10439-012-0715-0

Cited by 66 publications

(74 citation statements)

References 26 publications

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“…1D wave propagation or 0D lumped models) can efficiently replace 3D models (Moore et al, 2005;Blanco et al, 2009;Grinberg et al, 2011;Formaggia et al;Reymond et al, 2012). However, 3D models are really warranted when pressure losses and velocity features are largely determined by the interplay between hemodynamics and complex geometry Keshavarz-Motamed et al (2012); Itu et al (2013), as in these repaired ToF cases. On the other hand, the issue of complexity has also been addressed by developing so-called model order reduction techniques, which aim to reduce the dimension of the problem by restricting the numerical solution to a pre-defined low order space.…”

Section: Introductionmentioning

confidence: 99%

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Guibert

McLeod

Caiazzo

et al. 2014

Medical Image Analysis

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Abstract3D computational fluid dynamics (CFD) in patient-specific geometries provides complementary insights to clinical imaging, to better understand how heart disease, and the side effects of treating heart disease, affect and are affected by hemodynamics. This information can be useful in treatment planning for designing artificial devices that are subject to stress and pressure from blood flow. Yet, these simulations remain relatively costly within a clinical context. The aim of this work is to reduce the complexity of patient-specific simulations by combining image analysis, computational fluid dynamics and model order reduction techniques. The proposed method makes use of a reference geometry estimated as an average of the population, within an efficient statistical framework based on the currents representation of shapes. Snapshots of blood flow simulations performed in the reference geometry are used to build a POD (Proper Orthogonal Decomposition) basis, which can then be mapped on new patients to perform reduced order blood flow simulations with patient specific boundary conditions. This approach is applied to a data-set of 17 tetralogy of Fallot patients to simulate blood flow through the pulmonary artery under normal (healthy or synthetic valves with almost no backflow) and pathological (leaky or absent valve with backflow) conditions to better understand the impact of regurgitated blood on pressure and velocity at the outflow tracts.The model reduction approach is further tested by performing patient simulations under exercise and varying degrees of pathophysiological conditions based on reduction of reference solutions (rest and medium backflow conditions respectively).

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Section: Introductionmentioning

confidence: 99%

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Guibert

McLeod

Caiazzo

et al. 2014

Medical Image Analysis

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show abstract

“…Since the predictions have been shown to be accurate, this model has more recently also been employed for computations under pathologic conditions in specific parts of the circulation: coronary atherosclerosis [32], aortic coarctation [33], abdominal aorta aneurysm [34], and femoral bypass [35]. The details of the one-dimensional model used in this study are described in [32], [33]. The inlet boundary condition is provided by time-varying flow rate profiles, while a structured tree is coupled at each outlet of the anatomical model.…”

Section: Parameter Estimation Frameworkmentioning

confidence: 99%

“…Onedimensional models have been used in the past to compute time-varying flow rate and pressure waveforms in full body arterial models [31]. Since the predictions have been shown to be accurate, this model has more recently also been employed for computations under pathologic conditions in specific parts of the circulation: coronary atherosclerosis [32], aortic coarctation [33], abdominal aorta aneurysm [34], and femoral bypass [35]. The details of the one-dimensional model used in this study are described in [32], [33].…”

Section: Parameter Estimation Frameworkmentioning

confidence: 99%

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

Itu

Sharma

Suciu

et al. 2016

Numer Methods Biomed Eng

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We propose a hierarchical parameter estimation framework for performing patient-specific hemodynamic computations in arterial models, which use structured tree boundary conditions. A calibration problem is formulated at each stage of the hierarchical framework, which seeks the fixed point solution of a nonlinear system of equations. Common hemodynamic properties, like resistance and compliance, are estimated at the first stage in order to match the objectives given by clinical measurements of pressure and/or flow rate. The second stage estimates the parameters of the structured trees so as to match the values of the hemodynamic properties determined at the first stage. A key feature of the proposed method is that to ensure a large range of variation, two different structured tree parameters are personalized for each hemodynamic property. First, the second stage of the parameter estimation framework is evaluated based on the properties of the outlet boundary conditions in a full body arterial model: the calibration method converges for all structured trees in less than 10 iterations. Next, the proposed framework is successfully evaluated on a patient-specific aortic model with coarctation: only six iterations are required for the computational model to be in close agreement with the clinical measurements used as objectives, and overall, there is a good agreement between the measured and computed quantities. Copyright © 2016 John Wiley & Sons, Ltd.

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“…For is reason, a procedure that combines patient-specific image data and numerical tools to further understand the hemodynamics alterations, under resting and nonresting situations will allow clinicians to improve the diagnosis and define which should be the CoA treatment for the patient [10] [13]. Some authors have used CFD models to study the hemodynamics in the CoA [11] [20]. However, different numerical approaches might lead to different pressure predictions.…”

Section: Introductionmentioning

confidence: 99%

A reduced-order model based on the coupled 1D-3D finite element simulations for an efficient analysis of hemodynamics problems

et al. 2014

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A reduced-order model for an efficient analysis of cardiovascular hemodynamics problems using multiscale approach is presented in this work. Starting from a patient-specific computational mesh obtained by medical imaging techniques, an analysis methodology based on a two-step automatic procedure is proposed. First a coupled 1D-3D Finite Element Simulation is performed and the results are used to adjust a reduced-order model of the 3D patient-specific area of interest. Then, this reduced-order model is coupled with the 1D model. In this way, threedimensional effects are accounted for in the 1D model in a cost effective manner, allowing fast computation under different scenarios. The methodology proposed is validated using a patient-specific aortic coarctation model under rest and non-rest conditions

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Non-Invasive Hemodynamic Assessment of Aortic Coarctation: Validation with In Vivo Measurements

Cited by 66 publications

References 26 publications

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

Group-wise construction of reduced models for understanding and characterization of pulmonary blood flows from medical images

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

A reduced-order model based on the coupled 1D-3D finite element simulations for an efficient analysis of hemodynamics problems

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