Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD

Brüning, Jan; Hellmeier, Florian; Yevtushenko, Pavlo; Kühne, Titus; Goubergrits, Leonid

doi:10.1007/s13239-018-00381-3

Cited by 26 publications

(11 citation statements)

References 42 publications

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“…Segmentation - It is worth underlining that the reconstruction results are strongly dependent on the imaging technique adopted and operator dependant as well ( 21 ). Even though semi-automatic segmentation algorithms are available ( 22 ), the operator influence in geometry reconstruction remains. Different segmentation tools, including both manual and machine-learning-based produce valid outcomes.…”

Section: Introductionmentioning

confidence: 99%

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

Celi

Vignali

Capellini

et al. 2021

Front. Med. Technol.

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The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.

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Section: Introductionmentioning

confidence: 99%

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

Celi

Vignali

Capellini

et al. 2021

Front. Med. Technol.

View full text Add to dashboard Cite

show abstract

“…Patient-specific aorta geometry parameterization 2.1.1. Aorta dataset pre-processing Eight 3-D aorta geometries are reconstructed from MRI scans of patients with Coarctation of the aorta (COA) conditions by the German Heart Institute Berlin [36]. Prior to the parameterization of the variation of aorta shapes, one crucial step is to build correspondence among them, which is very challenging since the deformation across different patients is nonisometric, especially aortas with a complex topological structure (e.g.…”

Section: Methodsmentioning

confidence: 99%

Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics

Du,

Zhu,

Wang

2022

Preprint

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Optimization and uncertainty quantification have been playing an increasingly important role in computational hemodynamics. However, existing methods based on principled modeling and classic numerical techniques have faced significant challenges, particularly when it comes to complex 3D patient-specific shapes in the real world. First, it is notoriously challenging to parameterize the input space of arbitrarily complex 3-D geometries. Second, the process often involves massive forward simulations, which are extremely computationally demanding or even infeasible. We propose a novel deep learning surrogate modeling solution to address these challenges and enable rapid hemodynamic predictions. Specifically, a statistical generative model for 3-D patient-specific shapes is developed based on a small set of baseline patient-specific geometries. An unsupervised shape correspondence solution is used to enable geometric morphing and scalable shape synthesis statistically. Moreover, a simulation routine is developed for automatic data generation by automatic meshing, boundary setting, simulation, and post-processing. An efficient supervised learning solution is proposed to map the geometric inputs to the hemodynamics predictions in latent spaces. Numerical studies on aortic flows are conducted to demonstrate the effectiveness and merit of the proposed techniques.

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“…Different CFD; models have been established to quantitatively analyze the hemodynamics in the aorta under varying physiological conditions. For instance, studies by coupling CFD and some medical imaging technologies like Doppler echocardiography and magnetic resonance imaging (MRI) effectively predicted aneurysm and coarctation of the aorta (Brüning et al, 2018;Perinajová et al, 2021). Compared with MRI or computed tomography (CT), some expected dynamic behaviors of aortic diseases under stressed conditions can be better assessed via CFD modeling (Osswald et al, 2017;Febina et al, 2018), which can also be used to analyze the formation mechanism of aortic diseases (Zhang X. et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Effects of a Short-Term Left Ventricular Assist Device on Hemodynamics in a Heart Failure Patient-Specific Aorta Model: A CFD Study

Wang

Peng

et al. 2021

Front. Physiol.

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Patients with heart failure (HF) or undergoing cardiogenic shock and percutaneous coronary intervention require short-term cardiac support. Short-term cardiac support using a left ventricular assist device (LVAD) alters the pressure and flows of the vasculature by enhancing perfusion and improving the hemodynamic performance for the HF patients. However, due to the position of the inflow and outflow of the LVAD, the local hemodynamics within the aorta is altered with the LVAD support. Specifically, blood velocity, wall shear stress, and pressure difference are altered within the aorta. In this study, computational fluid dynamics (CFD) was used to elucidate the effects of a short-term LVAD for hemodynamic performance in a patient-specific aorta model. The three-dimensional (3D) geometric models of a patient-specific aorta and a short-term LVAD, Impella CP, were created. Velocity, wall shear stress, and pressure difference in the patient-specific aorta model with the Impella CP assistance were calculated and compared with the baseline values of the aorta without Impella CP support. Impella CP support augmented cardiac output, blood velocity, wall shear stress, and pressure difference in the aorta. The proposed CFD study could analyze the quantitative changes in the important hemodynamic parameters while considering the effects of Impella CP, and provide a scientific basis for further predicting and assessing the effects of these hemodynamic signals on the aorta.

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Uncertainty Quantification for Non-invasive Assessment of Pressure Drop Across a Coarctation of the Aorta Using CFD

Cited by 26 publications

References 42 publications

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

On the Role and Effects of Uncertainties in Cardiovascular in silico Analyses

Deep learning-based surrogate model for 3-D patient-specific computational fluid dynamics

Effects of a Short-Term Left Ventricular Assist Device on Hemodynamics in a Heart Failure Patient-Specific Aorta Model: A CFD Study

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