Select None to create only the restored images.

Select Cor Coef to create a correlation coefficient image along with the restored images.

Select # Avg to create an image of the number of pixels averaged to create each pixel, along with the restored images.

Select MLE to perform restoration using a maximum likelihood estimate to perform the restoration.

Warning

Do not use this method when using restoring less than 3 images.

Select Kalman to perform restoration using a Kalman filter.

	Enter the P value in P(0).
	Enter the R value in R(i) for each image. Press Sel R to calculate the R value on each image using the current region of interest.
	Press Set Alpha to set the alpha values for the pixels in the current region of interest. Enter a value for Alpha.

Note

This restoration method has not yet been implemented.

Select Average to perform restoration using the neighborhood average.

Select Median to perform restoration using the neighborhood median.

Select Average, Median to use the best fit between Average or Median on a per pixel basis.

Select Average, MLE to use the Average method on all the images, followed by the MLE method on the first four.

Note

This restoration method has not yet been implemented.

Select Butterworth to perform restoration using a butterworth filter with cosine extrapolation.

	Enter a value of the Butterworth filter in Order.
	Enter the cutoff frequency for the Butterworth filter in Cutoff.

Select Exponential to perform restoration using the best fit to an exponential curve.

Select 2-Gaussian to perform restoration using the best fit to 2 gaussian curves.

Select Gaussian-Exponential to perform restoration using the best fit to a gaussian and exponential curve.

Select LMRQ to perform restoration using the Levenberg-Marquardt method.

	Gamma
	Gaussian

Select Spline to perform restoration using a spline fit.

Select F(t) to perform equation analysis on the selected images. Images will be created that fit the original curve to a model of the passage of the bolus through the brain.

Select -ln (S(t)/So) to calculate the -ln (S(t)/So) on the images in the selected browser.

Select Linear to use linear value for X in the restoration.  1, 2, 3, .., n where n is the number of images will be used for the X values.

Select Non-Linear to use non-linear values for X in the restoration.  The user supplies the values for X in X(i).

Select Diffusion to use the gradient (squared) for the X values in the restoration.  The user supplies values for S in S(i) to be converted to gradient values.

Select T2  to use TE values for X in the restoration.  A constant TR is assumed.  The user supplies the values for TE in TE(i) and TR in TR, if the values cannot be determined from the image header information.  The output will be the T2 slope.

Select T1-Original to use TR values for X in the restoration using the original method.  A constant TE is assumed.  The user supplies the values for TR in TR(i) and TE in TE, if the values cannot be determined from the image header information.  The output will be the T1 slope.

Select T1-Eigen to use TR values for X in the restoration using the eigen method.  A constant TE is assumed.  The user supplies the values for TR in TR(i) and TE in TE, if the values cannot be determined from the image header information.  The output will be the T1 slope.

Select Linear to use the pixel values for the Y values.

Select Log to use the log of the pixels values for the Y values.

Enter recovery time in TR(i) or TR.  TR(i) is used to specify a recovery time for each image.  TR is used to specify a constant recovery time.

Enter echo time in TE(i) or TE.  TE(i) is used to specify an echo time for each image.  TE is to specify a constant echo time.

Enter a value in X(i) for each image.

Enter the S value in S(i) for each image.

Enter the B value in B(i) for each image.

Neighborhood size

	Select the size, in pixels, of the neighborhood in the X direction, centered on a pixel.
	Select the size, in pixels, of the neighborhood in the Y direction, centered on a pixel.
	Select the size, in pixels, of the neighborhood in the Z direction, centered on a pixel.

Radius

Select Radius to use an elliptical/spherical shape for the neighborhood, instead of a rectangular/cubic shape.

sigma

Enter the value for the euclidian distance between the center and non-center pixel in sigma.  If the euclidian-distance for a non-center pixel is less than sigma, that pixel is part of the neighborhood.

Note

The default is no limit.

Select First Estimate to use the restoration results from the first pixel, within the neighborhood.

Select Center Estimate to use the restoration results from the center pixel.

Select Mean of All Estimates to average the restoration results from all pixels, within the neighborhood.

Select None to use no weighting in the fit of a pixel vector.

Select Std Dev to use the standard deviation of the current region of interest, on each image, as the weights in the fit of the pixel vector.

Select Pixel to use the pixel intensity of each image as the weights in the fit of the pixel vector.