Reconciling PC‐MRI and CFD: An in‐vitro study

Puiseux, Thomas; Sewonu, A.; Meyrignac, Olivier; Rousseau, Hervé; Nicoud, Franck; Mendez, Simon; Moreno, Ramiro

doi:10.1002/nbm.4063

Cited by 29 publications

(34 citation statements)

References 46 publications

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“…This indicates that the pure CFD simulation cannot be corrected just by scaling the velocity values by a constant factor. It can also be seen that the simulated 4D‐Flow MRI has lower velocity magnitudes compared to the reference CFD simulation, which is a result of the limited spatial‐resolution and volume averaging; similar results have been reported by other researchers 36,68,69 . The patient specific pure CFD simulation which relies on boundary conditions estimated from the simulated 4D‐Flow MRI has lower velocity magnitudes compared to the simulated 4D‐Flow MRI.…”

Section: Resultssupporting

confidence: 85%

Towards multi‐modal data fusion for super‐resolution and denoising of 4D‐Flow MRI

Perez-Raya

Fathi

Baghaie

et al. 2020

Numer Methods Biomed Eng

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4D‐Flow magnetic resonance imaging (MRI) has enabled in vivo time‐resolved measurement of three‐dimensional blood flow velocities in the human vascular system. However, its clinical use has been hampered by two main issues, namely, low spatio‐temporal resolution and acquisition noise. While patient‐specific computational fluid dynamics (CFD) simulations can address the resolution and noise issues, its fidelity is impacted by accuracy of estimation of boundary conditions, model parameters, vascular geometry, and flow model assumptions. In this paper a scheme to address limitations of both modalities through data‐fusion is presented. The solutions of the patient‐specific CFD simulation are characterized using proper orthogonal decomposition (POD). Next, a process of projecting the 4D‐Flow MRI data onto the POD basis and projection coefficient mapping using generalized dynamic mode decomposition (DMD) enables simultaneous super‐resolution and denoising of 4D‐Flow MRI. The method has been tested using numerical phantoms derived from patient‐specific aneurysmal geometries and applied to in vivo 4D‐Flow MRI data.

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Section: Resultssupporting

confidence: 85%

Towards multi‐modal data fusion for super‐resolution and denoising of 4D‐Flow MRI

Perez-Raya

Fathi

Baghaie

et al. 2020

Numer Methods Biomed Eng

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“…It can be seen that the reduction of spatial resolution of CFD reduced the peak values, but also introduced a shift of the peak location. Similar behavior was shown also in a related study of [ 22 ], but these findings were not addressed. This shift should be taken into account when analyzing cases where the exact location of the peak WSS is of importance.…”

Section: Discussionsupporting

confidence: 65%

Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations

et al. 2021

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In this study, we analyzed turbulent flows through a phantom (a 180$$^{\circ }$$ ∘ bend with narrowing) at peak systole and a patient-specific coarctation of the aorta (CoA), with a pulsating flow, using magnetic resonance imaging (MRI) and computational fluid dynamics (CFD). For MRI, a 4D-flow MRI is performed using a 3T scanner. For CFD, the standard $$k-\epsilon $$ k - ϵ , shear stress transport $$k-\omega $$ k - ω , and Reynolds stress (RSM) models are applied. A good agreement between measured and simulated velocity is obtained for the phantom, especially for CFD with RSM. The wall shear stress (WSS) shows significant differences between CFD and MRI in absolute values, due to the limited near-wall resolution of MRI. However, normalized WSS shows qualitatively very similar distributions of the local values between MRI and CFD. Finally, a direct comparison between in vivo 4D-flow MRI and CFD with the RSM turbulence model is performed in the CoA. MRI can properly identify regions with locally elevated or suppressed WSS. If the exact values of the WSS are necessary, CFD is the preferred method. For future applications, we recommend the use of the combined MRI/CFD method for analysis and evaluation of the local flow patterns and WSS in the aorta.

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“…28 This statistical approach is commonly employed in biomedical image-based research 1 , 2 and has been used to compare 4D flow MRI measured velocities to numerical predictions and experimental data. 27 , 34 In this study we calculated the Pearson’s product-moment correlation coefficient (R) for the velocity field in both the entire aortic fluid domain as well as the ascending aorta where disturbed flow may occur. In general, largest differences between MRI and LES are observed at peak systole, 6 , 34 therefore peak systole was selected for comparison.…”

Section: Resultsmentioning

confidence: 99%

“… 27 , 34 In this study we calculated the Pearson’s product-moment correlation coefficient (R) for the velocity field in both the entire aortic fluid domain as well as the ascending aorta where disturbed flow may occur. In general, largest differences between MRI and LES are observed at peak systole, 6 , 34 therefore peak systole was selected for comparison. Owing to the different spatial resolutions of 4D flow MRI and LES, the LES velocity field was down sampled to the 4D flow MRI resolution allowing a pixel by pixel evaluation, as recommended in Reference 34 .…”

Section: Resultsmentioning

confidence: 99%

Analysis of Turbulence Effects in a Patient-Specific Aorta with Aortic Valve Stenosis

Manchester

Pirola

Salmasi

et al. 2021

Cardiovasc Eng Tech

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Blood flow in the aorta is often assumed laminar, however aortic valve pathologies may induce transition to turbulence and our understanding of turbulence effects is incomplete. The aim of the study was to provide a detailed analysis of turbulence effects in aortic valve stenosis (AVS). Methods Large-eddy simulation (LES) of flow through a patient-specific aorta with AVS was conducted. Magnetic resonance imaging (MRI) was performed and used for geometric reconstruction and patient-specific boundary conditions. Computed velocity field was compared with 4D flow MRI to check qualitative and quantitative consistency. The effect of turbulence was evaluated in terms of fluctuating kinetic energy, turbulence-related wall shear stress (WSS) and energy loss. Results Our analysis suggested that turbulence was induced by a combination of a high velocity jet impinging on the arterial wall and a dilated ascending aorta which provided sufficient space for turbulence to develop. Turbulent WSS contributed to 40% of the total WSS in the ascending aorta and 38% in the entire aorta. Viscous and turbulent irreversible energy losses accounted for 3.9 and 2.7% of the total stroke work, respectively. Conclusions This study demonstrates the importance of turbulence in assessing aortic haemodynamics in a patient with AVS. Neglecting the turbulent contribution to WSS could potentially result in a significant underestimation of the total WSS. Further work is warranted to extend the analysis to more AVS cases and patients with other aortic valve diseases.

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Reconciling PC‐MRI and CFD: An in‐vitro study

Cited by 29 publications

References 46 publications

Towards multi‐modal data fusion for super‐resolution and denoising of 4D‐Flow MRI

Towards multi‐modal data fusion for super‐resolution and denoising of 4D‐Flow MRI

Assessment of turbulent blood flow and wall shear stress in aortic coarctation using image-based simulations

Analysis of Turbulence Effects in a Patient-Specific Aorta with Aortic Valve Stenosis

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