Numerical simulation of time-resolved 3D phase-contrast magnetic resonance imaging

Puiseux, Thomas; Sewonu, A.; Moreno, Ramiro; Mendez, Simon; Nicoud, Franck

doi:10.1371/journal.pone.0248816

Cited by 13 publications

(12 citation statements)

References 47 publications

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“…Flow-induced displacement artifacts were not included, and cycle-to-cycle variations were condensed into cycle-averaged quantities. These assumptions were made to reduce the computational cost as compared to more accurate modelling of the imaging processes 31 . Recent work from Dillinger et al 42 demonstrates that the implementation of realistic encoding gradients in an Eulerian–Lagrangian Bloch solver results in systematic underestimation of high frequency components of turbulence.…”

Section: Discussionmentioning

confidence: 99%

“…Inference was limited to velocity fields and turbulence was not incorporated. A more realistic approach consists of using data from CFD to compute the trajectories of individual material points while their complex-valued magnetization, and corresponding MRI signal, can be evaluated by solving the Bloch equations in the Lagrangian frame of reference 30 , 31 . Such a method can be used to evaluate specific MRI sequences while inherently accounting for flow-induced displacement and dephasing artifacts 32 .…”

Section: Introductionmentioning

confidence: 99%

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Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

Dirix

Buoso

Peper

et al. 2022

Sci Rep

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We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier–Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

Dirix

Buoso

Peper

et al. 2022

Sci Rep

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“…Future work could include implementing postprocessing methods to compensate for artifacts on velocity fields due to acceleration‐induced displacement 46 and to gradient field distortions. 45 Another perspective is to simulate 4D flow MRI to further characterize the observed divergences, 47 for instance by considering the Rician distribution of the noise in the magnitude images in MRI 48 or computing acceleration‐induced displacement artifacts. 49 …”

Section: Discussionmentioning

confidence: 99%

Accelerated sequences of 4D flow MRI using GRAPPA and compressed sensing: A comparison against conventional MRI and computational fluid dynamics

Garreau

Puiseux

Toupin

et al. 2022

Magnetic Resonance in Med

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To evaluate hemodynamic markers obtained by accelerated GRAPPA (R = 2, 3, 4) and compressed sensing (R = 7.6) 4D flow MRI sequences under complex flow conditions. Methods: The accelerated 4D flow MRI scans were performed on a pulsatile flow phantom, along with a nonaccelerated fully sampled k-space acquisition.Computational fluid dynamics simulations based on the experimentally measured flow fields were conducted for additional comparison. Voxel-wise comparisons (Bland-Altman analysis, L 2 -norm metric), as well as nonderived quantities (velocity profiles, flow rates, and peak velocities), were used to compare the velocity fields obtained from the different modalities.Results: 4D flow acquisitions and computational fluid dynamics depicted similar hemodynamic patterns. Voxel-wise comparisons between the MRI scans highlighted larger discrepancies at the voxels located near the phantom's boundary walls. A trend for all MR scans to overestimate velocity profiles and peak velocities as compared to computational fluid dynamics was noticed in regions associated with high velocity or acceleration. However, good agreement for the flow rates was observed, and eddy-current correction appeared essential for consistency of the flow rates measurements with respect to the principle of mass conservation. Conclusion:GRAPPA (R = 2, 3) and highly accelerated compressed sensing showed good agreement with the fully sampled acquisition. Yet, all 4D flow MRI scans were hampered by artifacts inherent to the phase-contrast acquisition procedure. Computational fluid dynamics simulations are an interesting tool to assess these differences but are sensitive to modeling parameters.

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“…In practice, 4D flow MRI quantification would require more complex methods in order to quantify the effects of these hardware source of errors. Specific methods have been developed for creating synthetic 4D flow MRI data from raw Phase Contrast MRI data to better assess turbulent features, e.g., turbulence intensity and TKE (45)(46)(47)(48)(49)(50), and for completely excluding the effects of the hardware source errors by generating synthetic MRI data fields (51).…”

Section: Discussionmentioning

confidence: 99%

Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations

Marinis

Obrist

2021

Front. Cardiovasc. Med.

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We propose a data assimilation methodology that can be used to enhance the spatial and temporal resolution of voxel-based data as it may be obtained from biomedical imaging modalities. It can be used to improve the assessment of turbulent blood flow in large vessels by combining observed data with a computational fluid dynamics solver. The methodology is based on a Stochastic Ensemble Kalman Filter (SEnKF) approach and geared toward pulsatile and turbulent flow configurations. We describe the observed flow fields by a mean value and its covariance. These flow fields are combined with forecasts obtained from a direct numerical simulation of the flow field. The method is validated against canonical pulsatile and turbulent flows. Finally, it is applied to a clinically relevant configuration, namely the flow downstream of a bioprosthetic valve in an aorta phantom. It is demonstrated how the 4D flow field obtained from experimental observations can be enhanced by the data assimilation algorithm. Results show that the presented method is promising for future use with in vivo data from 4D Flow Magnetic Resonance Imaging (4D Flow MRI). 4D Flow MRI returns spatially and temporally averaged flow fields that are limited by the spatial and the temporal resolution of the tool. These averaged flow fields and the associated uncertainty might be used as observation data in the context of the proposed methodology.

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Numerical simulation of time-resolved 3D phase-contrast magnetic resonance imaging

Cited by 13 publications

References 47 publications

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

Accelerated sequences of 4D flow MRI using GRAPPA and compressed sensing: A comparison against conventional MRI and computational fluid dynamics

Data Assimilation by Stochastic Ensemble Kalman Filtering to Enhance Turbulent Cardiovascular Flow Data From Under-Resolved Observations

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