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- W2236354827 abstract "Flow compensated and monopolar diffusion weighting gradients are employed to determine the characteristic time scale of the incoherent blood motion causing the biexponential signal decay. A pulse sequence for diffusion weighted magnetic resonance imaging is developed, which allows one to suppress velocity encoding of imaging gradients and which is designed such that the influence of concomitant fields is reduced. It is tested with phantoms and healthy volunteers, revealing different signal attenuation curves for flow compensated and monopolar diffusion gradients in liver and pancreas. Furthermore, a dependence on the total duration of the applied diffusion gradient profile is measured. To describe the experimentally observed signal attenuation curves, a model is developed, which allows one to calculate the signal attenuation due to incoherent blood motion for arbitrary diffusion gradient profiles. Precalculated normalized phase distributions allow one to fit the model to the experimental data. For the signal attenuation curves averaged over test subjects, the characteristic timescale of the blood motion is found to be τ = 184±64 ms in pancreasand τ = 156 ± 22 ms in liver. To facilitate a pixel-wise evaluation and the creation of parameter maps, a denoising algorithm based on principal component analysis is implemented. The denoising reduces the effect of pseudo-random signal contributions allowing one to obtain parameter maps from only 33% of the originally acquired data, which are less affected by noise than the original ones." @default.
- W2236354827 created "2016-06-24" @default.
- W2236354827 creator A5016113314 @default.
- W2236354827 date "2013-01-01" @default.
- W2236354827 modified "2023-09-27" @default.
- W2236354827 title "Magnetic Resonance Diffusion Weighted Imaging: Flow Compensated Intravoxel Incoherent Motion Imaging as a Tool to Probe Microvasculature" @default.
- W2236354827 doi "https://doi.org/10.11588/heidok.00015226" @default.
- W2236354827 hasPublicationYear "2013" @default.
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