Combining statistical shape modeling, <scp>CFD</scp>, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Hoeijmakers, M.; Waechter-Stehle, Irina; Weese, Jürgen; Vosse, Frans N. van de

doi:10.1002/cnm.3387

Cited by 16 publications

(34 citation statements)

References 64 publications

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“…Density based methods which characterize the output distribution by the cumulative density function, may in those cases be more appropriate, and should be considered in future work. 62 In line with Hoeijmakers et al, 4 the transvalvular pressure-drop at peak systole was approximated by CFD simulations, and expressed as an uncertain scalar parameter Y . In-vivo however, the transvalvular pressure-drop would strongly vary throughout the cardiac cycle due to flow pulsatility.…”

Section: Limitationsmentioning

confidence: 95%

“…However, 3D patient‐specific computational models are generally of complex shape, difficult to parameterize, and the influence of shape variation is therefore mostly neglected. Instead of using physically meaningful parameters, this work used statistical shape modeling to parameterize the shape of the valve 4 . The statistical shape modes provided a parameterization of the geometry, and facilitated the training of a meta‐model (Figure 1(A)).…”

Section: Methodsmentioning

confidence: 99%

“…The shape coefficients

α_{p, m}

can be regarded as a patient‐specific parameterization of the aortic valve and surrounding structures, and values for the 74 original valve segmentations were found by Equation (6). Besides approximating the surface meshes in the training set, Equation (5) was used to generate a population of “virtual” aortic valve geometries that were within three times the square root of the eigenvalue (

([],, - 3 \sqrt{λ_{m}}, 3 \sqrt{λ_{m}})

) of its respective shape mode, also see Hoeijmakers et al 4 or Heimann and Meinzer 33 . Figure 2 illustrates the shape variation that was contained in the first five shape modes, and showed that the first shape mode (

Φ_{1}

), weighted by

α_{1}

, controlled the opening and closing behavior of the aortic valve.…”

Section: Methodsmentioning

confidence: 99%

“…This weighting factor was empirically established, and helped to reduce the approximation error of vertices that were part of, or close to the free cusp edges. For a more detailed description on the performance (e.g., compactness and generalizability) of the statistical shape model and for a comparison between CFD results on the original segmentation and the approximated shape by the SSM we would like to refer to the work of Hoeijmakers et al 4 The shape coefficients α p,m can be regarded as a patient-specific parameterization of the aortic valve and surrounding structures, and values for the 74 original valve segmentations were found by Equation ( 6). Besides approximating the surface meshes in the training set, Equation ( 5) was used to generate a population of "virtual" aortic valve geometries that were within three times the square root of the eigenvalue ( À3 ffiffiffiffiffi ffi…”

Section: Statistical Shape Modelmentioning

confidence: 99%

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The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Hoeijmakers

Huberts

Rutten

et al. 2021

Numer Methods Biomed Eng

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Patient‐specific image‐based computational fluid dynamics (CFD) is widely adopted in the cardiovascular research community to study hemodynamics, and will become increasingly important for personalized medicine. However, segmentation of the flow domain is not exact and geometric uncertainty can be expected which propagates through the computational model, leading to uncertainty in model output. Seventy‐four aortic‐valves were segmented from computed tomography images at peak systole. Statistical shape modeling was used to obtain an approximate parameterization of the original segmentations. This parameterization was used to train a meta‐model that related the first five shape mode coefficients and flowrate to the CFD‐computed transvalvular pressure‐drop. Consequently, shape uncertainty in the order of 0.5 and 1.0 mm was emulated by introducing uncertainty in the shape mode coefficients. A global variance‐based sensitivity analysis was performed to quantify output uncertainty and to determine relative importance of the shape modes. The first shape mode captured the opening/closing behavior of the valve and uncertainty in this mode coefficient accounted for more than 90% of the output variance. However, sensitivity to shape uncertainty is patient‐specific, and the relative importance of the fourth shape mode coefficient tended to increase with increases in valvular area. These results show that geometric uncertainty in the order of image voxel size may lead to substantial uncertainty in CFD‐computed transvalvular pressure‐drops. Moreover, this illustrates that it is essential to assess the impact of geometric uncertainty on model output, and that this should be thoroughly quantified for applications that wish to use image‐based CFD models.

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Section: Limitationsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

“…The shape coefficients

α_{p, m}

([],, - 3 \sqrt{λ_{m}}, 3 \sqrt{λ_{m}})

Φ_{1}

), weighted by

α_{1}

, controlled the opening and closing behavior of the aortic valve.…”

Section: Methodsmentioning

confidence: 99%

Section: Statistical Shape Modelmentioning

confidence: 99%

See 2 more Smart Citations

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Hoeijmakers

Huberts

Rutten

et al. 2021

Numer Methods Biomed Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…PCA has been used in predicting cerebral aneurysm rupture based on morphology and haemodynamics [43]. Statistical shape modelling with PCA could facilitate geometric characterization of the vasculature [44][45][46][47] and the heart [48,49]. One challenge in using PCA is that the rows need to be consistently placed in different snapshots.…”

Section: Opportunities and Challengesmentioning

confidence: 99%

Data-driven cardiovascular flow modelling: examples and opportunities

Arzani

Dawson

2021

J. R. Soc. Interface.

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High-fidelity blood flow modelling is crucial for enhancing our understanding of cardiovascular disease. Despite significant advances in computational and experimental characterization of blood flow, the knowledge that we can acquire from such investigations remains limited by the presence of uncertainty in parameters, low resolution, and measurement noise. Additionally, extracting useful information from these datasets is challenging. Data-driven modelling techniques have the potential to overcome these challenges and transform cardiovascular flow modelling. Here, we review several data-driven modelling techniques, highlight the common ideas and principles that emerge across numerous such techniques, and provide illustrative examples of how they could be used in the context of cardiovascular fluid mechanics. In particular, we discuss principal component analysis (PCA), robust PCA, compressed sensing, the Kalman filter for data assimilation, low-rank data recovery, and several additional methods for reduced-order modelling of cardiovascular flows, including the dynamic mode decomposition and the sparse identification of nonlinear dynamics. All techniques are presented in the context of cardiovascular flows with simple examples. These data-driven modelling techniques have the potential to transform computational and experimental cardiovascular research, and we discuss challenges and opportunities in applying these techniques in the field, looking ultimately towards data-driven patient-specific blood flow modelling.

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Generation of synthetic aortic valve stenosis geometries for in silico trials

Verstraeten,

Hoeijmakers,

Tonino

et al. 2023

Numer Methods Biomed Eng

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In silico trials are a promising way to increase the efficiency of the development, and the time to market of cardiovascular implantable devices. The development of transcatheter aortic valve implantation (TAVI) devices, could benefit from in silico trials to overcome frequently occurring complications such as paravalvular leakage and conduction problems. To be able to perform in silico TAVI trials virtual cohorts of TAVI patients are required. In a virtual cohort, individual patients are represented by computer models that usually require patient‐specific aortic valve geometries. This study aimed to develop a virtual cohort generator that generates anatomically plausible, synthetic aortic valve stenosis geometries for in silico TAVI trials and allows for the selection of specific anatomical features that influence the occurrence of complications. To build the generator, a combination of non‐parametrical statistical shape modeling and sampling from a copula distribution was used. The developed virtual cohort generator successfully generated synthetic aortic valve stenosis geometries that are comparable with a real cohort, and therefore, are considered as being anatomically plausible. Furthermore, we were able to select specific anatomical features with a sensitivity of around 90%. The virtual cohort generator has the potential to be used by TAVI manufacturers to test their devices. Future work will involve including calcifications to the synthetic geometries, and applying high‐fidelity fluid–structure‐interaction models to perform in silico trials.

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Combining statistical shape modeling, CFD, and meta‐modeling to approximate the patient‐specific pressure‐drop across the aortic valve in real‐time

Cited by 16 publications

References 64 publications

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

The impact of shape uncertainty on aortic‐valve pressure‐drop computations

Data-driven cardiovascular flow modelling: examples and opportunities

Generation of synthetic aortic valve stenosis geometries for in silico trials

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