Incoherent Flow Imaging

Nalcioğlu, Orhan; Cho, Z. H.; Xiang, Qing; Ahn, Changmin

doi:10.1117/12.966708

Cited by 14 publications

(11 citation statements)

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“…Several studies have investigated the effects of turbulent flow on the magnetic resonance imaging (MRI) signal in order to establish methods for quantifying turbulence [11][12][13][14][15][16][17][18]. Recently, an in vitro study indicated good agreement between the turbulence quantities obtained by MRI and particle image velocimetry [19].…”

Section: Introductionmentioning

confidence: 97%

On MRI turbulence quantification

Dyverfeldt

Gårdhagen

Sigfridsson

et al. 2009

Magnetic Resonance Imaging

129

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Turbulent flow, characterized by velocity fluctuations, accompanies many forms of cardiovascular disease and may contribute to their progression and hemodynamic consequences. Several studies have investigated the effects of turbulence on the magnetic resonance imaging (MRI) signal. Quantitative MRI turbulence measurements have recently been shown to have great potential for application both in human cardiovascular flow and in engineering flow. In this article, potential pitfalls and sources of error in MRI turbulence measurements are theoretically and numerically investigated. Data acquisition strategies suitable for turbulence quantification are outlined. The results show that the sensitivity of MRI turbulence measurements to intravoxel mean velocity variations is negligible, but that noise may degrade the estimates if the turbulence encoding parameter is set improperly. Different approaches for utilizing a given amount of scan time were shown to influence the dynamic range and the uncertainty in the turbulence estimates due to noise. The findings reported in this work may be valuable for both in vitro and in vivo studies employing MRI methods for turbulence quantification.

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Section: Introductionmentioning

confidence: 97%

On MRI turbulence quantification

Dyverfeldt

Gårdhagen

Sigfridsson

et al. 2009

Magnetic Resonance Imaging

129

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“…By applying the sinc function of the flow velocity magnitude to the diffusion-weighted signal, we assumed that the microvascular blood flow is random-directional with a zero mean velocity and a constant velocity magnitude. In a given situation, the mean velocity may not be zero, but this net averaged velocity only contributes to the phase of the signal, not to the signal attenuation when measured by the diffusion-weighted MRI, as shown by Nalcioglu et al (22). The higher flow in large vessels also contributes mainly to the signal phase, not the diffusion-weighted signal attenuation, and thus is not shown in the microvascular random flow u(x, y) maps in Figs.…”

Section: Discussionmentioning

confidence: 92%

“…The random-directional flow dependence of the diffusion-weightcd MRI signal has been studied by many groups. The early work by Nalcioglu et al (22,23) and Ahn et al (25) showed that the diffusion-weighted signal can be approximated by a sinc function of the random-directional flow velocity magnitude and verified it by phantom and ex vivo experiments (43). Flow compensation techniques (25)(26)(27)(28)(29)(30)(31)(32), such as even echo rephasing and back-to-back bipolar diffusion encoding, have been proposed to separate the random-directional flowinduced signal change from the molecular diffusion.…”

Section: Discussionmentioning

confidence: 94%

“…Nalcioglu et 01. (22,23) and Xiang et al (24) have shown the existence of the random flow-induced signal attenuation in the presence of the diffusion-encoding gradients. Ahn et al (25) applied the even echo rcphasing technique and demonstrated the sinc functional dependence of the signal due to the random flow using a "random hall" phantom.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measurement of tumor vascular volume and mean microvascular random flow velocity magnitude by dynamic GD‐DTPA‐Albumin enhanced and diffusion‐weighted MRI

Wang

Nalcioğlu

1998

Magnetic Resonance in Med

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Tumor vascular volume fraction and the magnitude of the mean microvascular random flow velocity were measured in an animal tumor model by combining dynamic Gd-DTPA-albumin enhanced MRI and diffusion-weighted MRI in conjunction with a compartmental modeling analysis. The vascular volume fraction maps were obtained from the dynamic Gd-DTPA-albumin enhanced MRI measurement. It was found that the vascular volume fraction for Walker 256 tumor was higher within the outgrowing rim and decreased towards the central region. The average value obtained from five animals was 0.062 +/- 0.009 ml/g. By using the vascular volume fraction from the Gd-DTPA-albumin enhanced MRI measurement, maps of the magnitude of the mean microvascular random flow velocity were obtained from the diffusion-weighted MRI measurements with the compartmental modeling analysis. The relative extravascular and intravascular contributions to the diffusion-weighted MRI signal were determined for three tissue groups with different Gd-DTPA-albumin enhancement characteristics, and the flow and molecular diffusion-induced attenuation factors for the intravascular compartment were also compared. The mean microvascular random flow velocity magnitude maps were obtained with an average value of 0.67 +/- 0.06 mm/s.

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“…The particle motions and interac-tions induce fluid velocity fluctuations about the average velocity for each isochromat. These velocity fluctuations generate signal attenuation proportional to their intensity (Nalcioglu et al 1986). The effect of velocity fluctuations in turbulent flow on the NMR signal has been shown to cause attenuation of the first echo, which is measured to generate the experimental spectra (DeGennes 1969).…”

Section: Discussionmentioning

confidence: 99%

Rheological Characterization of Fluids Using NMR Velocity Spectrum Measurements

Seymour

Maneval

McCarthy

et al. 1995

Journal of Texture Studies

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This paper demonstrates the use of the nuclear magnetic resonance (NMR) velocity spectrum to rheologically characterize a fluid‐like material in steady tube flow. The velocity spectra for four different materials, an oil‐water solution, a polyacrylamide solution, tomato juice and a paper pulp suspension are measured and qualitative agreement with theory based upon simple constitutive models is demonstrated. These materials exhibit Newtonian, power law and Bingham plastic behavior. Homogeneous and heterogeneous samples are used and the applicability to each discussed. The velocity spectra from NMR experiments allows for a nonin‐vasive method of monitoring flow behavior and as such has applications to online process monitoring of rheological properties.

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Incoherent Flow Imaging

Cited by 14 publications

References 0 publications

On MRI turbulence quantification

On MRI turbulence quantification

Measurement of tumor vascular volume and mean microvascular random flow velocity magnitude by dynamic GD‐DTPA‐Albumin enhanced and diffusion‐weighted MRI

Rheological Characterization of Fluids Using NMR Velocity Spectrum Measurements

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