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tend-sim - Man Page

simulate DW images from a tensor field

Synopsis

tend sim [-old] [-sigma <sigma>] [-seed <seed>] [-g <grad list>] [-B <B matrix>] -r <reference field> [-i <tensor field>] [-b <b>] [-kvp] [-t <type>] [-o <nout>]

Description

Simulate DW images from a tensor field. The output will be in the same form as the input to tend-estim(1). The B-matrices (“-B”) can be the output from tend-bmat(1), or the gradients can be given directly (“-g”); one of these is required. Note that the input tensor field (“-i”) is the basis of the output per-axis fields and image orientation. NOTE: this includes the measurement frame used in the input tensor field, which implies that the given gradients or B-matrices are already expressed in that measurement frame.

Options

-old

don’t use the new tenEstimateContext functionality

-sigma <sigma>

Rician noise parameter (float) default: “0.0

-seed <seed>

seed value for RNG which creates noise (int) default: “42

-g <grad list>

gradient list, one row per diffusion-weighted image

-B <B matrix>

B matrix, one row per diffusion-weighted image. Using this overrides the gradient list input via “-g

-r <reference field>

reference anatomical scan, with no diffusion weighting

-i <tensor field>

input diffusion tensor field

-b <b>

b value for simulated scan (float) default: “1000

-kvp

generate key/value pairs in the NRRD header corresponding to the input b-value and gradients or B-matrices.

-t <type>

output type of DWIs; default: “float

-o <nout>

output image (floating point) (string) default: “-

See Also

tend(1), tend-bmat(1), tend-estim(1)

Referenced By

tend(1), tend-bmat(1), tend-estim(1).

April 2021