tend-estim - Man Page

tend estim [-old] [-sigma <sigma>] [-v <verbose>] [-est <estimate method>] [-wlsi <WLS iters>] [-fixneg] [-ee <filename>] [-eb <filename>] [-t <thresh>] [-soft <soft>] [-scale <scale>] [-mv <min val>] -B <B-list> [-b <b>] -knownB0 <bool> [-i <dwi0 dwi1 ...>] [-o <nout>]

Estimate tensors from a set of DW images. The tensor coefficient weightings associated with each of the DWIs, the B-matrix, is given either as a separate array, (see tend-bmat(1)for details), or by the key-value pairs in the DWI nrrd header. A “confidence” value is computed with the tensor, based on a soft thresholding of the sum of all the DWIs, according to the threshold and softness parameters.

-old

instead of the new tenEstimateContext code, use the old tenEstimateLinear code

-sigma <sigma>

Rician noise parameter (float)

-v <verbose>

verbosity level (int) default: “0”

-est <estimate method>

estimation method to use. “lls”: linear-least squares; default: “lls”

-wlsi <WLS iters>

when using weighted-least-squares (“-est wls”), how many iterations to do after the initial weighted fit. (unsigned int) default: “1”

-fixneg

after estimating the tensor, ensure that there are no negative eigenvalues by adding (to all eigenvalues) the amount by which the smallest is negative (corresponding to increasing the non-DWI image value).

-ee <filename>

Giving a filename here allows you to save out the tensor estimation error: a value which measures how much error there is between the tensor model and the given diffusion weighted measurements for each sample. By default, no such error calculation is saved. (string)

-eb <filename>

In those cases where there is no B=0 reference image given (“-knownB0 false”), giving a filename here allows you to save out the B=0 image which is estimated from the data. By default, this image value is estimated but not saved. (string)

-t <thresh>

value at which to threshold the mean DWI value per pixel in order to generate the “confidence” mask. By default, the threshold value is calculated automatically, based on histogram analysis. (double)

-soft <soft>

how fuzzy the confidence boundary should be. By default, confidence boundary is perfectly sharp (float) default: “0”

-scale <scale>

After estimating the tensor, scale all of its elements (but not the confidence value) by this amount. Can help with downstream numerical precision if values are very large or small. (float) default: “1”

-mv <min val>

minimum plausible value (especially important for linear least squares estimation) (double) default: “1.0”

-B <B-list>

6-by-N list of B-matrices characterizing the diffusion weighting for each image. tend-bmat(1) is one source for such a matrix; see its usage info for specifics on how the coefficients of the B-matrix are ordered. An unadorned plain text file is a great way to specify the B-matrix.

OR

Can say just “-B kvp” to try to learn B matrices from key/value pair information in input images.

(string)

-b <b>

“b” diffusion-weighting factor (units of sec/mm^2) (double)

-knownB0 <bool>

Indicates if the B=0 non-diffusion-weighted reference image is known, or if it has to be estimated along with the tensor elements

• if “true”: in the given list of diffusion gradients or B-matrices, there are one or more with zero norm, which are simply averaged to find the B=0 reference image value
• if “false”: there may or may not be diffusion-weighted images among the input; the B=0 image value is going to be estimated along with the diffusion model

(bool)

-i <dwi0 dwi1 ...>

all the diffusion-weighted images (DWIs), as separate 3D nrrds, OR: One 4D nrrd of all DWIs stacked along axis 0 (1 or more nrrds); default: “-”

-o <nout>

output tensor volume (string) default: “-”

tend(1) tend-bmat(1), tend-msim(1), tend-sim(1)

tend(1), tend-bmat(1), tend-msim(1), tend-sim(1).