Quantification of intravoxel velocity standard deviation and turbulence intensity by generalizing phase‐contrast MRI

Dyverfeldt, Petter; Sigfridsson, Andreas; Kvitting, John‐Peder Escobar; Ebbers, Tino

doi:10.1002/mrm.21022

Cited by 128 publications

(190 citation statements)

References 29 publications

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“…The fact that the highest velocities in the aortic stenosis and coarctation exceed the VENC value is not a concern as it is the magnitude, not phase, of the MRI signal that is used in IVSD mapping (19). The voxel-wise TKE was computed as:…”

Section: D Flow Mri Simulationsmentioning

confidence: 99%

“…The Swedish National Infrastructure for Computing (SNIC) is acknowledged for computational resources provided by the National Supercomputer Centre (SNIC2014- [11][12][13][14][15][16][17][18][19][20][21][22] …”

Section: Acknowledgmentsmentioning

confidence: 99%

“…Moreover, experimental assessment of the effects of turbulent flow on the vessel wall using non-invasive imaging has not been reported. However, recent developments have extended 4D Flow MRI to permit estimation of velocity fluctuation intensity and turbulent kinetic energy (TKE) (12,19,20). This approach has been validated and demonstrated in a variety of applications (21)(22)(23)(24)(25).…”

Section: Introductionmentioning

confidence: 99%

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Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

et al. 2016

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PurposeTo explore the use of MR-estimated turbulence quantities for the assessment of turbulent flow effects on the vessel wall. MethodsNumerical velocity data for two patient-derived models was obtained using computational fluid dynamics (CFD) for two physiological flow rates. 4D Flow MRI measurements were simulated at three different spatial resolutions and used to investigate the estimation of turbulent wall shear stress (tWSS) using the intravoxel standard deviation (IVSD) of velocity and turbulent kinetic energy (TKE) estimated near the vessel wall. ResultsAccurate estimation of tWSS using the IVSD is limited by the spatial resolution achievable with 4D Flow MRI. TKE, estimated near the wall, has a strong linear relationship to the tWSS (mean R 2 = 0.84). Near-wall TKE estimates from MR simulations have good agreement to CFD-derived ground truth (mean R 2 = 0.90). Maps of near-wall TKE have strong visual correspondence to tWSS. ConclusionNear-wall estimation of TKE permits assessment of relative maps of tWSS, but direct estimation of tWSS is challenging due to limitations in spatial resolution. Assessment of tWSS and nearwall TKE may open new avenues for analysis of different pathologies.

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Section: D Flow Mri Simulationsmentioning

confidence: 99%

Section: Acknowledgmentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

et al. 2016

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“…IVSD mapping enables the estimation of turbulence intensity (11,12) and has potential for the estimation of wall shear stress (18). Applications of methods based on 3rd or higher order moments are yet to be evaluated.…”

Section: Discussionmentioning

confidence: 99%

“…The standard deviation of the intravoxel spin velocity distribution [intravoxel velocity standard deviation (IVSD)] can be estimated from the magnitude relationship of two or more MRI signals (11). Under the assumption that the intravoxel velocity distribution s(v) is gaussian, the IVSD, s, is obtained from (11,12) r ¼ sqrt…”

Section: ½2mentioning

confidence: 99%

A novel MRI framework for the quantification of any moment of arbitrary velocity distributions

Dyverfeldt

Sigfridsson

Knutsson

et al. 2010

Magnetic Resonance in Med

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MRI can measure several important hemodynamic parameters but might not yet have reached its full potential. The most common MRI method for the assessment of flow is phasecontrast MRI velocity mapping that estimates the mean velocity of a voxel. This estimation is precise only when the intravoxel velocity distribution is symmetric. The mean velocity corresponds to the first raw moment of the intravoxel velocity distribution. Here, a generalized MRI framework for the quantification of any moment of arbitrary velocity distributions is described. This framework is based on the fact that moments in the function domain (velocity space) correspond to differentials in the Fourier transform domain (k v -space). For proof-of-concept, moments of realistic velocity distributions were estimated using finite difference approximations of the derivatives of the MRI signal. In addition, the framework was applied to investigate the symmetry assumption underlying phase-contrast MRI velocity mapping; we found that this assumption can substantially affect phase-contrast MRI velocity estimates and that its significance can be reduced by increasing the velocity encoding range. Magn Reson Med 65:725-731,

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Inverse problems in blood flow modeling: A review

Nolte

Bertoglio

2022

Numer Methods Biomed Eng

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Mathematical and computational modeling of the cardiovascular system is increasingly providing non‐invasive alternatives to traditional invasive clinical procedures. Moreover, it has the potential for generating additional diagnostic markers. In blood flow computations, the personalization of spatially distributed (i.e., 3D) models is a key step which relies on the formulation and numerical solution of inverse problems using clinical data, typically medical images for measuring both anatomy and function of the vasculature. In the last years, the development and application of inverse methods has rapidly expanded most likely due to the increased availability of data in clinical centers and the growing interest of modelers and clinicians in collaborating. Therefore, this work aims to provide a wide and comparative overview of literature within the last decade. We review the current state of the art of inverse problems in blood flows, focusing on studies considering fully dimensional fluid and fluid–solid models. The relevant physical models and hemodynamic measurement techniques are introduced, followed by a survey of mathematical data assimilation approaches used to solve different kinds of inverse problems, namely state and parameter estimation. An exhaustive discussion of the literature of the last decade is presented, structured by types of problems, models and available data.

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Quantification of intravoxel velocity standard deviation and turbulence intensity by generalizing phase‐contrast MRI

Cited by 128 publications

References 29 publications

Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

Assessment of turbulent flow effects on the vessel wall using four-dimensional flow MRI

A novel MRI framework for the quantification of any moment of arbitrary velocity distributions

Inverse problems in blood flow modeling: A review

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