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Solute-surface interactions are critical in membrane science and dominate a number of diffusion and selectivity parameters.
A number of diffusion tensor imaging (DTI) studies have reported substantial white matter (WM) abnormalities in alcohol-dependent patients.
A switching diffusion process can be thought of as a number of diffusion processes coupled by a random switching process.
For most methods, these results did not vary strongly over a wide range of acquisition parameters (number of diffusion weighting directions and b value).
Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR).
However these investigations are not in full agreement and some of those, especially in cluster headache, restricted the analysis to only a limited number of diffusion parameters.
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We use GraphNet regularization – imposing both signal smoothness and sparsity – to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qτ-space.
By increasing the number of diffusion-encoding gradients, complex crossings of white matter tracts within one voxel can be resolved.
For instance, measurements of large numbers of diffusion events in anisotropic rod-shaped particles have quantified differences in diffusion rates along the long and short axes.
Each competition step may be followed by a number (D) of diffusion steps.
The intensity of replicator diffusion is scaled by the average number D of diffusion steps per site per generation.
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