tend-msim - Man Page

tend msim [-sigma <sigma>] [-seed <seed>] -g <grad list> [-b0 <b0 image>] [-i <model image>] -m <model> [-ib0 <bool>] [-b <b>] [-kvp <bool>] [-t <type>] [-o <nout>]

Simulate DW images from an image of models. 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 image (“-i”) is the basis of the output per-axis fields and image orientation. NOTE: this includes the measurement frame used in the input tensor image, which implies that the given gradients or B-matrices are already expressed in that measurement frame.

-sigma <sigma>

Gaussian/Rician noise parameter (double) 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

-b0 <b0 image>

reference non-diffusion-weighted (“B0”) image, which may be needed if it isn’t part of the given model parameter image

-m <model>

model with which to simulate DWIs, which must be specified if it is not indicated by the first axis in input model image. “string”

-ib0 <bool>

insert a non-DW B0 image at the beginning of the experiment specification (useful if the given gradient list doesn’t already have one) and hence also insert a B0 image at the beginning of the output simulated DWIs “bool” default: “false”

-b <b>

b value for simulated scan “double” default: “1000”

-kvp <bool>

generate key/value pairs in the NRRD header corresponding to the input b-value and gradients. “bool” default: “true”

-t <type>

output type of DWIs; default: “float”

-o <nout>

output dwis (string) default: “-”

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

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