Apparent diffusion coefficient (ADC) measures the magnitude of diffusion (of water molecules) within cerebral tissue. A low value for ADC indicates that the cortical white matter tracts are organized (good), while a high value for ADC indicates that these tracts are disorganized (bad). When evaluating an acute ischemic stroke, ADC image sequences play a crucial role. Ischemic brain parenchyma tends to have a low ADC value. This is in turn correlated with a high diffusion weighted imaging (DWI) value to confirm that the ischemia is not secondary to an MRI artifact known as T2 shine through. The latter would demonstrate a high signal on DWI imaging, but would continue to demonstrate high intensity on ADC sequences.
Apparent diffusion coefficient (ADC) is a measure of water diffusion in all directions, while mean diffusivity is a measure of the average diffusion within a voxel. ADC includes the effects of both isotropic and anisotropic diffusion, whereas mean diffusivity reflects the overall diffusion within the voxel. In DTI, ADC is calculated as the average of the three eigenvalues, which correspond to the three principal diffusion directions and contribute to mean diffusivity.
Do you mean "equations involving exponential functions"? Yes,
The abbreviation ion stands for several things. One of the things it stands for is Inferior Esophageal Nerve. Another item that ion stands for is Ionic Self-Diffusion Coefficient D.
A number is in exponential form when it is written with a base and an exponent.
a coefficient is the number before the variable.example- 4y the 4 before the y is the coefficient.
A coefficient is any number in front of a variable .
applications of carl Pearson coefficient of corelation applications of carl Pearson coefficient of corelation applications of carl Pearson coefficient of corelation applications of carl Pearson coefficient of corelation applications of carl Pearson coefficient of corelation applications of carl Pearson coefficient of corelation
Of course it is! If the mean of a set of data is negative, then the coefficient of variation will be negative.
The coefficient
The correlation coefficient is symmetrical with respect to X and Y i.e.The correlation coefficient is the geometric mean of the two regression coefficients. or .The correlation coefficient lies between -1 and 1. i.e. .
Of course it is! If the mean of a set of data is negative, then the coefficient of variation will be negative.
It's an elasticity coefficient of demand: deltaD/deltaP When the coefficient is >1 it is an elastic demand When the coefficient is <1 it is a nonelastic demand