Using fourier velocity encoded MRI data to guide CFD simulations

Rispoli, V. C.; Nielsen, Jon Fredrik; Nayak, Krishna S.; Carvalho, João L. A.

doi:10.1109/isbi.2015.7163941

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…However, recent work on physics-informed neural networks (PINNs) applied to hemodynamics have shown promise in one-dimensional pulsatile flows [10] and two-and three-dimensional steady flows [11]. Several simulation-based imaging (SBI) methods [12][13][14][15][16][17][18] exist that match a computational fluid dynamics (CFD) simulation to magnetic resonance (MR) flow data by optimizing free parameters of the CFD simulation (usually boundary and initial conditions), use a metric to measure the difference between CFD and MR flow imaging, and update the free parameters to minimize the cost function via their own strategy. The method used in this work [18] uses a high-order CFD discretization, efficient adjoint-based PDE-constrained optimization, and a novel objective function that mimics the point-spread function of MRI scanners.…”

Section: Introductionmentioning

confidence: 99%

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Naudet

Töger

Zahr

2022

Engineering with Computers

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Simulation-based imaging (SBI) is a blood flow imaging technique that optimally fits a computational fluid dynamics (CFD) simulation to low-resolution, noisy magnetic resonance (MR) flow data to produce a high-resolution velocity field. In this work, we study the effectivity of SBI in predicting wall shear stress (WSS) relative to standard magnetic resonance imaging (MRI) postprocessing techniques using two synthetic numerical experiments: steady flow through an idealized, two-dimensional stenotic vessel and a model of an adult aorta. In particular, we study the sensitivity of these two approaches with respect to the Reynolds number of the underlying flow, the resolution of the MRI data, and the noise in the MRI data. We found that the SBI WSS reconstruction: (1) is insensitive to Reynolds number over the range considered ($$\mathrm {Re} \le 1000$$ Re ≤ 1000 ), (2) improves as the amount of MRI data increases and provides accurate reconstructions with as little as three MRI voxels per diameter, and (3) degrades linearly as the noise in the data increases with a slope determined by the resolution of the MRI data. We also consider the sensitivity of SBI to the resolution of the CFD mesh and found there is flexibility in the mesh used for SBI, although the WSS reconstruction becomes more sensitive to other parameters, particularly the resolution of the MRI data, for coarser meshes. This indicates a fundamental trade-off between scan time (i.e., MRI data quality and resolution) and reconstruction time using SBI, which is inherently different than the trade-off between scan time and reconstruction quality observed in standard MRI postprocessing techniques.

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Section: Introductionmentioning

confidence: 99%

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Naudet

Töger

Zahr

2022

Engineering with Computers

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“…Machine learning approaches are valuable, but face drawbacks of high training cost and not being patient-specific. Several simulation-based imaging (SBI) methods 10,11,12,13,14,15,16,17,18 exist that match a computational fluid dynamics (CFD) simulation to magnetic resonance (MR) flow data by optimizing free parameters of the CFD simulation (usually boundary and initial conditions), use a metric to measure the difference between CFD and MR flow imaging, and update the free parameters to minimize the cost function via their own strategy. The method used in this work 17 uses a high-order CFD discretization, efficient adjoint-based PDE-constrained optimization, and a novel objective function that mimics the point-spread function of MRI scanners.…”

Section: Introductionmentioning

confidence: 99%

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Naudet¹,

Töger²,

Zahr³

2021

Preprint

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Simulation-based imaging (SBI) is a blood flow imaging technique that optimally fits a computational fluid dynamics (CFD) simulation to low-resolution, noisy magnetic resonance (MR) flow data to produce a high-resolution velocity field. In this work, we study the effectivity of SBI in predicting wall shear stress (WSS) relative to standard magnetic resonance imaging (MRI) postprocessing techniques using two synthetic numerical experiments: flow through an idealized, two-dimensional stenotic vessel and a model of an adult aorta. In particular, we study the sensitivity of these two approaches with respect to the Reynolds number of the underlying flow, the resolution of the MRI data, and the noise in the MRI data. We found that the SBI WSS reconstruction: 1) is insensitive to Reynolds number over the range considered (Re ≤ 1000), 2) improves as the amount of MRI data increases and provides accurate reconstructions with as little as three MRI voxels per diameter, and 3) degrades linearly as the noise in the data increases with a slope determined by the resolution of the MRI data. We also consider the sensitivity of SBI to the resolution of the CFD mesh and found there is flexibility in the mesh used for SBI, although the WSS reconstruction becomes more sensitive to other parameters, particularly the resolution of the MRI data, for coarser meshes. This indicates a fundamental trade-off between scan time (i.e., MRI data quality and resolution) and reconstruction time using SBI, which is inherently different than the trade-off between scan time and reconstruction quality observed in standard MRI postprocessing techniques.

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“…Current methods for matching CFD to 4D‐flow typically consist of three parts: an accurate CFD simulation with free parameters to optimize, a metric to measure the difference between CFD and 4D‐flow, and an efficient strategy to update the parameters to minimize the difference metric. Most studies to date use CFD simulations with low order of accuracy, such as particle methods, finite differences, or low‐order finite‐element methods 14‐15,21‐24 . Furthermore, the difference metric is usually not designed to model the 4D‐flow measurement process 14‐16,20‐23 .…”

Section: Introductionmentioning

confidence: 99%

Blood flow imaging by optimal matching of computational fluid dynamics to 4D‐flow data

Töger

Zahr

Aristokleous

et al. 2020

Magnetic Resonance in Med

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Purpose Three‐dimensional, time‐resolved blood flow measurement (4D‐flow) is a powerful research and clinical tool, but improved resolution and scan times are needed. Therefore, this study aims to (1) present a postprocessing framework for optimization‐driven simulation‐based flow imaging, called 4D‐flow High‐resolution Imaging with a priori Knowledge Incorporating the Navier‐Stokes equations and the discontinuous Galerkin method (4D‐flow HIKING), (2) investigate the framework in synthetic tests, (3) perform phantom validation using laser particle imaging velocimetry, and (4) demonstrate the use of the framework in vivo. Methods An optimizing computational fluid dynamics solver including adjoint‐based optimization was developed to fit computational fluid dynamics solutions to 4D‐flow data. Synthetic tests were performed in 2D, and phantom validation was performed with pulsatile flow. Reference velocity data were acquired using particle imaging velocimetry, and 4D‐flow data were acquired at 1.5 T. In vivo testing was performed on intracranial arteries in a healthy volunteer at 7 T, with 2D flow as the reference. Results Synthetic tests showed low error (0.4%‐0.7%). Phantom validation showed improved agreement with laser particle imaging velocimetry compared with input 4D‐flow in the horizontal (mean −0.05 vs −1.11 cm/s, P < .001; SD 1.86 vs 4.26 cm/s, P < .001) and vertical directions (mean 0.05 vs −0.04 cm/s, P = .29; SD 1.36 vs 3.95 cm/s, P < .001). In vivo data show a reduction in flow rate error from 14% to 3.5%. Conclusions Phantom and in vivo results from 4D‐flow HIKING show promise for future applications with higher resolution, shorter scan times, and accurate quantification of physiological parameters.

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Using fourier velocity encoded MRI data to guide CFD simulations

Cited by 3 publications

References 5 publications

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Accurate quantification of blood flow wall shear stress using simulation-based imaging: a synthetic, comparative study

Blood flow imaging by optimal matching of computational fluid dynamics to 4D‐flow data

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