Fundamentals of turbulent flow spectrum imaging

Dillinger, Hannes; McGrath, Charles; Guenthner, Christian; Kozerke, Sebastian

doi:10.1002/mrm.29001

Cited by 8 publications

(6 citation statements)

References 63 publications

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“…A one‐time calibration using the GMTF 3 allows for reduction of the effects and would be favorable; however, due to heating effects, the mechanical resonance frequencies may change during measurement time 12,18,24 . A possible alternative, simple method for the identification of mechanical resonances using audio recordings has been presented previously, 38 which could be used concurrently to measurements and allow monitoring mechanical resonance frequencies online. However, this method may be limited to modes that also couple acoustically 39 …”

Section: Discussionmentioning

confidence: 99%

Beat phenomena of oscillating readouts

Dillinger,

Peereboom,

Kozerke

2024

Magnetic Resonance in Med

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PurposeTo demonstrate slowly varying, erroneous magnetic field gradients for oscillating readouts due to the mechanically resonant behavior of gradient systems.MethodsProjections of a static phantom were acquired using a one‐dimensional (1D) EPI sequence with varying EPI frequencies ranging from 1121 to 1580 Hz on clinical 3T systems (30 mT/m, 200 T/m/s). Phase due to static B0 inhomogeneities was eliminated by a complex division of two separate scans with different polarities of the EPI readout. The temporal evolution of phase was evaluated and related to the mechanical resonances of the gradient systems derived from the gradient modulation transfer function. Additionally, the impact of temporally varying mechanical resonance effects on EPI was evaluated using an echo‐planar spectroscopic imaging sequence.ResultsA beat phenomenon resulting in a slowly varying phase was observed. Its temporal frequency was given by the difference between the EPI frequency and the mechanical resonance frequency of the activated gradient axis. The maximum erroneous, oscillating phase during phase encoding was ±0.5 rad for an EPI frequency of 1281 Hz. Echo‐planar spectroscopic imaging images showed the resulting time‐dependent stretching/compression of the FOV.ConclusionOscillating readouts such as those used in EPI can result in low‐frequency, erroneous phase contributions, which are explained by the beat phenomenon. Therefore, EPI phase‐correction approaches may need to include beat effects for accurate image reconstruction.

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Section: Discussionmentioning

confidence: 99%

Beat phenomena of oscillating readouts

Dillinger,

Peereboom,

Kozerke

2024

Magnetic Resonance in Med

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“…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. Therefore, quantification of in-vivo turbulence is subject to overestimation due to partial volume effects on one hand and underestimation due to band-limited velocity encoding gradients on the other hand.…”

Section: Discussionmentioning

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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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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“…Accordingly, the development of dedicated simulations for phase-contrast (PC) CMR has been of significant interest. These works can be approximately divided into two categories: (1) Euler-Lagrangian frameworks, in which the Bloch equations are solved for magnetization moving according to velocity-vector fields derived from computational fluid dynamics (CFDs) 2,8,[30][31][32][33][34][35][36] ; and (2) Eulerian approaches, in which the Bloch equations are solved in a fixed reference frame and motion is incorporated through transport equations. 3,[37][38][39][40][41] Discretizing the digital anatomic structure into noninteracting particles is a common strategy in MR simulations, which can also be deployed for incorporating complex organ motion such as cardiac contraction.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, the development of dedicated simulations for phase‐contrast (PC) CMR has been of significant interest. These works can be approximately divided into two categories: (1) Euler‐Lagrangian frameworks, in which the Bloch equations are solved for magnetization moving according to velocity‐vector fields derived from computational fluid dynamics (CFDs) 2,8,30–36 ; and (2) Eulerian approaches, in which the Bloch equations are solved in a fixed reference frame and motion is incorporated through transport equations 3,37–41 …”

Section: Introductionmentioning

confidence: 99%

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

Weine,

McGrath,

Dirix

et al. 2024

Magnetic Resonance in Med

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PurposeTo present an open‐source MR simulation framework that facilitates the incorporation of complex motion and flow for studying cardiovascular MR (CMR) acquisition and reconstruction.MethodsCMRsim is a Python package that allows simulation of CMR images using dynamic digital phantoms with complex motion as input. Two simulation paradigms are available, namely, numerical and analytical solutions to the Bloch equations, using a common motion representation. Competitive simulation speeds are achieved using TensorFlow for GPU acceleration. To demonstrate the capability of the package, one introductory and two advanced CMR simulation experiments are presented. The latter showcase phase‐contrast imaging of turbulent flow downstream of a stenotic section and cardiac diffusion tensor imaging on a contracting left ventricle. Additionally, extensive documentation and example resources are provided.ResultsThe Bloch simulation with turbulent flow using approximately 1.5 million particles and a sequence duration of 710 ms for each of the seven different velocity encodings took a total of 29 min on a NVIDIA Titan RTX GPU. The results show characteristic phase contrast and magnitude modulation present in real data. The analytical simulation of cardiac diffusion tensor imaging with bulk‐motion phase sensitivity took approximately 10 s per diffusion‐weighted image, including preparation and loading steps. The results exhibit the expected alteration of diffusion metrics due to strain.ConclusionCMRsim is the first simulation framework that allows one to feasibly incorporate complex motion, including turbulent flow, to systematically study advanced CMR acquisition and reconstruction approaches. The open‐source package features modularity and transparency, facilitating maintainability and extensibility in support of reproducible research.

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Fundamentals of turbulent flow spectrum imaging

Abstract: This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Cited by 8 publications

References 63 publications

Beat phenomena of oscillating readouts

Beat phenomena of oscillating readouts

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

CMRsim–A python package for cardiovascular MR simulations incorporating complex motion and flow

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